Companion Proceedings of the Conference on Graphics, Patterns and Images (SIBGRAPI)
Sociedade Brasileira de Computação - SBC
Towards an Agnostic Superpixel Segmentation Framework
Superpixel segmentation partitions multiple objects into disjoint parts so that their delineation can be accurately achieved by their grouping, and it has been used as an intermediary step for solving multiple problems. However, state-of-the-art algorithms face a significant challenge of effective and efficient segmentation irrespective of the problem’s domain (object and background characteristics, and user’s desires). In this work, we address such challenge by proposing several contributions. One of such is a novel superpixel segmentation framework, named Superpixels through Iterative CLEarcutting (SICLE), which generalizes two other contributions of this work. In SICLE, three independent steps are defined: (i) seed oversampling; (ii) superpixel generation using the Image Foresting Transform (IFT) framework; and (iii) seed removal. From (i), where a significantly high amount of seeds is selected, steps (ii) and (iii) are performed for generating superpixels from a refined seed set until achieving the desired number of superpixels. SICLE overcomes domain shifts primarily through steps (ii) and (iii), where the user may provide an objective function for optimization. Experimental results show that SICLE variants surpass several state-of-the-art algorithms concerning speed and accuracy for distinct domains while generating a series of segmentations in a single execution. Still, in SICLE, the contours from a preceding scale might not be present in the subsequent one leading to hierarchical violations. Thus, we studied eight possible cases when analyzing pairwise subsequent segmentations, and we conceived three measures for estimating the hierarchiness of a multiscale segmentation: (i) nestedness; (ii) inflation ratio; and (iii) refinement error. From our results, it is possible to verify if a multiscale is a hierarchy and, when it is not the case, to analyze and state the nature and extent of the hierarchical violations that prevent it from being hierarchical.
Digital 3D Animation of Powder-Snow Avalanches
Physically based animation of fluids such as smoke, water, and fire provides some of the most stunning computer graphics in the entertainment industry. However, several phenomena still need to be fully understood, and their formulations are still the focus of intense research in other fields, such as Physics and Civil Engineering. That is the case with snow avalanches, whose numerical modeling is challenging due to their complex dynamics. The manipulation of such phenomena is new to computer graphics, and few works exist. This project aims to bring these formulations to the field of computer graphics regarding the digital animation of powder-snow avalanches.
Self-supervised learning for fully unsupervised re-identification in real-world applications
Re-Identification (ReID) is vital for real-world applications such as AI-powered security, event understanding, and smart city development. It aims to retrieve all instances of a given person or object across a network of non-overlapping cameras, based solely on visual appearance. It is challenging due to occlusions, viewpoint changes, and background similarities. Supervised methods perform well but rely on costly, biased annotations, limiting scalability. To address this, we propose self-supervised learning algorithms for Unsupervised ReID, extendable to other modalities, like Text Authorship Verification, marked by high intra-class variation and low inter-class distinction. Our work introduces three fully unsupervised ReID methods: one using camera labels, one without side information, and one scalable to larger datasets. We also present a fourth hybrid method for long-range recognition under distortions. These solutions enhance generalization and enable real-world applications in forensics and biometrics. We have released open-source code and demonstrated practical impact, including a consultancy project for the Public Prosecutor’s Office of the State of São Paulo (MPSP), and House of Representatives (Brazilian Federal Chamber and Senate). This research was recognized by the Brazilian Computing Society (SBC) as the best Ph.D. thesis defended in Brazil in 2024.
From Robustness to Efficiency: Deformation-Aware and Efficient Local Feature Extraction for Images
Just as animals rely on visual perception and geometric understanding to navigate the 3D world, modern computers emulate this ability through Simultaneous Localization and Mapping (SLAM), image-based 3D reconstruction, and visual place recognition techniques, all relying on image features for obtaining correspondences. However, most feature extraction methods handle only affine transformations, ignoring non-rigid deformations, ubiquitous in the real-world. This work investigates deformation-aware local features, leveraging RGB-D images to compute geodesics, where RGB denotes the visible channels (Red, Green, Blue) and D represents image depth. Then, we generalize the concept to RGB-only images via learned representations. We introduce a novel RGB-D dataset with non-rigid deformations for real-world benchmarking, where experiments showed significant improvements in foundational vision tasks as matching and registration when adopting our proposed strategies. Finally, we present an efficient local feature extractor, balancing accuracy with reduced computational cost, expanding visual perception for mobile computers.
Strengthening Scientific Integrity: Digital Forensics for Biomedical Research Imaging
To combat the increasing number of misconduct cases in science, this Ph.D. research addressed the challenge of scientific integrity with a pioneering investigation into digital forensic analysis specifically tailored for biomedical research imaging. This work conducted extensive research into key manipulation types—copy-move forgery, image reuse, and AI-generated content—developing novel, fully explainable, and auditable computational detection methods for each. In a commitment to transparency and to promote research in the area, these techniques are provided as open-source resources. Besides the isolated techniques for each type of image forged, a central contribution is the development of an end-to-end system, created through collaboration with international forensic experts and the U.S. Office of Research Integrity (ORI). This system automates the analysis of scientific publications, starting from PDF documents and ending by identifying figures with potential integrity concerns.
Machine Learning and Information Retrieval Techniques for Time Series Analysis
This work explores the intersection of Machine Learning and Information Retrieval for time series analysis, addressing key challenges in representation, classification, clustering, and retrieval. Four methodologies are proposed, covering univariate and multivariate time series across supervised, unsupervised, and semi-supervised scenarios. The approaches integrate contextual similarity learning, image-based representations, and domain-specific graph modeling, demonstrating competitive performance across multiple datasets.
A Study on Gaussian Splatting and Neural Fields
3D reconstruction from posed images is undergoing a fundamental transformation, driven by continuous advances in Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS), where scenes are volumetrically represented and rendered using volume rendering techniques. However, extracting detailed surfaces from these fuzzy, density-based representations remains a challenge due to the lack of effective surface regularization. To address this, recent work has explored alternative representations such as 2D Gaussian Splatting (2DGS), which enables stronger regularization and has shown promising results for surface extraction. Although 2DGS is a powerful representation, it suffers from certain drawbacks, such as high memory consumption. Motivated by these limitations, this work presents a study of two increasingly popular methodologies: Implicit Neural Representations (INRs) and 2DGS. First, we demonstrate that augmenting 2DGS with depth information can significantly improve geometric accuracy—by up to 10% compared to models without depth. Importantly, depth is relatively inexpensive to obtain, as it can be predicted using state-of-the-art depth estimation methods. Next, we introduce an application that extracts an INR from 2D Gaussian representations. However, previous efforts have focused primarily on isolated objects of interest. In practice, scenes are often more complex and contain both foreground and background elements. To tackle this, we propose a method to segment foreground and background Gaussians. Leveraging this segmentation, we further demonstrate the extraction of a 360° panorama of the scene.
Position-based Shape Constraint for Real-time Hair Simulation
In this work we present a method to simulate curly hair with its spring-like characteristics in real-time, while maintaining the strand inextensibility. By using the position-based simulation framework and expanding on previous works on the same topic, a new constraint is devised to keep the curls shape. We share our results and stablish parallels with other related works.
Unraveling Neural Cellular Automata for Lightweight Image Compression
Neural Cellular Automata (NCA) are computational models inspired by cellular growth, capable of learning complex behaviours through local interactions. While NCAs have been applied to various tasks like image restoration and synthesis, their potential for image compression remains largely unexplored. This paper aims to unravel the capabilities of NCAs for lightweight image compression by introducing a Grid Neural Cellular Automata (GNCA) training strategy. Unlike traditional methods that depend on large deep learning models, NCAs offer a low-cost, compact and highly parallelizable alternative with intrinsic robustness to noise. Through experiments on the COCO 2017 dataset, we compare the compression performance of NCAs against JPEG, JPEG-2000 and WebP, using the metrics PSNR, SSIM, MSE and Compression Rate. Our results demonstrate that NCAs achieve competitive compression rates and image quality reconstruction, highlighting their potential as a lightweight solution for efficient image compression.
Image registration applied to estimation of the coupling angle between tractor vehicles and semi-trailers +
Accurate estimation of the coupling angle between a tractor and a semitrailer is crucial for the control of autonomous and semi-autonomous articulated vehicles. In freight transport scenarios, it is particularly important to estimate this angle without instrumenting the semitrailer. Existing methods in the literature often suffer from limitations such as dependence on trailer geometry, the need for recalibration at each coupling, sensitivity to weather conditions, or proprietary constraints. This paper proposes an image registration-based method for coupling angle estimation that addresses these issues and can be extended to support trailer approach maneuvers.
Tatuoca Project: Automated data extraction from magnetogram images
The Earth’s magnetic field acts as a natural shield against solar radiation, with several magnetic observatories established around the world. Among them, the Tatuoca Magnetic Observatory, located in the equatorial region, stands out for the intense variations of the field in this area. Its 50 years of historical records are fundamental to the study of the Earth’s magnetic field. However, preserving these records faces significant challenges, as they degrade over time. To prevent this loss, the Tatuoca Project proposes the computerized extraction of data from these records and making this information available to the global scientific community. Using image processing and data analysis techniques, including clustering methods such as DBSCAN, the project aims to transform the physical records into accurate and accessible digital formats, as well as enabling automatic extraction, replacing the time-consuming manual work of data extraction.
Multimodal Graph Attention Networks for Real-Time Action Prediction and Accident Prevention in Industrial Environments
One of the major challenges faced by the manufacturing industry is the prevention of workplace accidents. In this context, ensuring compliance with safety regulations, such as the use of personal protective equipment (PPE) and the proper execution of specific tasks according to safety protocols, is essential, especially when supervisors or safety personnel are not present on site. To address this issue, we propose a multimodal method for real-time human action prediction in industrial environments, aimed at supporting accident prevention systems. Our approach integrates two parallel Graph Attention Networks (GATs): one based on human skeleton pose estimation, and another built from scene object detection graphs. By combining these two complementary modalities, the model captures both human motion dynamics and contextual environmental information. To the best of our knowledge, this approach has not yet been explored in the literature. The proposed method will be evaluated on two benchmark datasets: Kinetics-400 (a large-scale video dataset with diverse real-world actions), and UnsafeNet (a dataset featuring factory-recorded videos annotated with safe and unsafe behaviors). The expected results aim to demonstrate the feasibility of applying multimodal GAT-based architectures to enhance occupational safety through intelligent action recognition systems.
Are You Listening to Me? Fine-Tuning Chatbots for Empathetic Dialogue
Conversational agents have made significant progress since ELIZA, expanding their role across various domains, including healthcare, education, and customer service. As these agents become increasingly integrated into daily human interactions, the need for emotional intelligence, particularly empathetic listening, becomes increasingly essential. In this study, we explore how Large Language Models (LLMs) respond when tasked with generating emotionally rich interactions. We analyzed the emotional progression of the dialogues using both sentiment analysis (via VADER) and expert assessments. While the generated conversations often mirrored the intended emotional structure, human evaluation revealed important differences in the perceived empathy and coherence of the responses. These findings suggest that emotion modeling in dialogues requires not only structural alignment in the expressed emotions but also qualitative depth, highlighting the importance of combining automated and human-centered methods in the development of emotionally competent agents.
Towards Automated Detection of Rheumatic Heart Disease Using Classical Computer Vision Techniques
Acute Rheumatic Fever (ARF) and its primary sequela, Chronic Rheumatic Heart Disease (RHD), remain a significant public health problem, particularly in developing countries. Early diagnosis of RHD, based on echocardiographic criteria such as anterior mitral leaflet (AML) thickening, is crucial for preventing disease progression. However, manual assessment of these parameters is subjective, time-consuming, and limited by the inherently noisy quality of echocardiography images. This study proposes and validates the tip-to-base thickness ratio as a quantitative biomarker, derived from a semi-automated, interpretable pipeline for assessing AML thickening. The methodology encompasses leaflet segmentation through Otsu’s thresholding and morphological operations, followed by the extraction of its medial axis and division into anatomically relevant segments (base, middle, and tip). Validating on a dataset of 80 pediatric echocardiograms (17 ARF, 63 controls), our results indicate that the proposed tip-to-base thickness ratio was significantly higher in patients with ARF (p = 0.03), yielding an Area Under the Curve (AUC) of 0.67 for disease classification. By providing a fully transparent and reproducible measurement, this work demonstrates the feasibility of using classic computational methods to create objective tools for RHD screening, highlighting the value of interpretable classical methods for clinical decision support in RHD screening.
An Unbiased Benchmark for Domain Generalization Face Anti-Spoofing
Domain Generalization for Face Anti-Spoofing (DGFAS) is an area of growing interest due to its importance in fraud prevention in Face Recognition Systems. Current benchmarks used for DG-FAS in evaluating state-of-the-art methods allow verification of test set performance during training, which causes bias towards test data. Consequently, practitioners cannot properly translate research conclusions to real-world applications, since there is no access to labels for production data in practice. We propose as an alternative an unbiased benchmark where a validation dataset is used so that the model’s generalization capability is evaluated without compromising restricted data and the scientific rigor of research. Our experiments show that model performance benefits from current biased benchmarks and that introducing a new validation dataset makes for more challenging and scientifically rigorous benchmarks that also better represent real-world performance. We additionally experiment with training on current standard benchmarks and testing on WFAS, a recent in-the-wild large FAS dataset with more attack types than the standard datasets for DG-FAS, and similarly observe poor generalization capabilities for state-of-the-art models.
Hemorrhagic Stroke Segmentation via Hierarchical Feature Extraction and Active Contours
Hemorrhagic stroke remains a leading cause of mortality and disability worldwide, demanding accurate and timely diagnosis. This study proposes a hybrid segmentation method combining Convolutional Neural Networks (CNNs) with Active Contours to delineate hemorrhagic lesions in brain CT scans. The approach extracts hierarchical representations through a multi-scale CNN inspired by Cross Stage Partial (CSP) blocks and enhances interpretability via contextual activation maps. These maps guide both the internal energy and initialization of the active contour, yielding anatomically coherent segmentations. Quantitative evaluation on public and private datasets demonstrates the model’s robustness and precision, with specificity, precision, and sensitivity of 99.96%, 96.41%, and 92.02%, respectively. Compared to state-of-the-art methods, the proposed architecture achieved competitive performance with improved anatomical consistency and reduced reliance on manual input. These findings support the method’s potential for clinical decision support in stroke diagnosis.
Development of a High-resolution Geospatial Dataset for Sidewalk and Flood Analysis using Computer Vision
Sidewalk infrastructure plays a vital role in advancing sustainable cities by enhancing mobility, promoting social inclusion, and strengthening urban resilience, particularly in the face of climate-related risks such as flooding. In several developing countries, sidewalks are not only of poor quality but also lack consistent and context-sensitive data that would allow for an adequate assessment of their condition. This paper presents the development of a high-resolution multimodal geospatial dataset aimed at analyzing sidewalk infrastructure in urban environments, with a focus on the borough of São Miguel in São Paulo, Brazil, an area marked by frequent flooding and socio-spatial inequalities. By integrating remote sensing, deep learning, and computer vision techniques, the study supports the adaptation and validation of semantic segmentation tools such as Tile2Net for the Brazilian context. The combination of these methods for sidewalk segmentation shows promising results, with preliminary findings demonstrating the effectiveness of Tile2Net integrated with dilation operation. The proposed approach contributes to scalable and socially equitable solutions for urban infrastructure assessment and inclusive territorial planning, with a particular focus on sidewalk networks in flood-prone areas, making them especially relevant for analyzing potential escape routes during such events.
Superpixel Segmentation Effect on Hierarchical GNN applied to Image Classification
In the image processing field, Graph Neural Networks (GNNs) are employed to learn from the graph structure to its full potential. Before model training, the data transformation converts the image into a graph structure. In this sense, many superpixel algorithms such as SLIC, DISF, SCALP, and ODISF have been adopted for reducing complexity and creating meaningful pixel regions based on some criteria. As these algorithms offer distinct characteristics and superpixel aspects, this work-inprogress presents a study on the performance of the HiErarchical Layered Multigraph Network (HELMNet) according to the variation of superpixel methods used to generate the base graph of the HiErarchical Layered Multigraph (HELM) representation with images from the STL-10 dataset. An attention function called Region Graph Readout (RGR), used for classification, is also set to guide the clarification of the divergent results. Furthermore, this study aims to highlight the nonexistence of a general rule for selecting a superpixel method given a specific visual task.
Optimal Parameterization of the Phase Correlation Method for Image Registration
Phase correlation is a widely used and effective method for estimating translation, rotation, and scaling parameters between two images. This technique relies on a (log-)polar mapping of the coordinates of the images’ magnitude spectra. However, the accuracy of the estimation strongly depends on the parameterization of this mapping. While it is well established that low angular resolutions lead to the loss of relevant information and impair registration performance, recent studies have shown that excessively high resolutions can also degrade performance due to spurious correlations introduced by interpolation. To address this issue, we propose a methodology for parameterizing the coordinate mapping that preserves the information contained in the Cartesian plane while minimizing factors that cause false correlations and unnecessary memory consumption. Beyond its direct application to phase correlation, the proposed approach has the potential to benefit any signal processing task that requires lossless discrete-to-discrete Cartesian-to-Polar mapping.
CAPS-BQ: A Compact Attention-Based Approach for Part Segmentation in 3D Objects
Technological progress has led to the development of high-precision sensors for three-dimensional scanning, such as LiDAR. Part segmentation of point clouds generated by these sensors is a fundamental task in data processing and analysis, yielding valuable insights for decision-making processes. This study introduces a lightweight, attention-based neural network architecture for point cloud part segmentation that employs a radius-based search algorithm for point selection. By leveraging a widely recognized benchmark dataset, we developed a model that is lightweight, robust, and computationally efficient. The proposed model achieves a competitive mIoU of 84.26% and shows an inference speed approximately 45.56% faster than its leading competitor on a test set of 2874 point clouds, highlighting its computational efficiency.We conducted a comparative analysis against the Local and Global APES models, the PartField network, and the consolidated PointNet, confirming the proposed architecture’s efficacy in delivering high performance with an optimized inference time.
Diffuse Until You Fake It—Synthesizing High-Fidelity Chest CT Volumes from the LIDC-IDRI Dataset Using Diffusion Models
We propose a diffusion-based generative framework for synthesizing realistic thoracic CT volumes from the LIDC–IDRI dataset, aiming to address data scarcity, privacy concerns, and augmentation needs in medical imaging. Our approach leverages a 3D U-Net architecture with residual connections as the denoising function within a Denoising Diffusion Probabilistic Model, enabling volumetric reconstruction from isotropic Gaussian noise across 1,000 reverse diffusion steps. The model is trained end-to-end on standardized CT scans resampled to 1283 voxels, with intensities normalized in the Hounsfield scale. Due to the high computational demands of volumetric diffusion, the training was distributed across two consumer-grade GPUs over 58 days, incorporating memory-efficient strategies such as gradient checkpointing and small batch sizes. Evaluation is conducted using Fréchet Inception Distance (FID) and Multi-Scale Structural Similarity Index (MS-SSIM), with results computed against a held-out test set. Qualitative inspection and quantitative metrics jointly demonstrate that the generated samples exhibit high anatomical plausibility, cross-slice coherence, and distributional alignment with real CT scans. These findings highlight the potential of diffusion models to surpass GAN-based alternatives in generating clinically meaningful synthetic 3D medical images.
ClearFace: Facial Acne Detection and Classification System Using YOLOv11 and EfficientNet-B0
Facial acne is a highly prevalent dermatological condition that significantly affects individuals’ physical and psychological well-being. Although clinical diagnosis remains the gold standard, limited access to specialized care highlights the need for scalable, automated solutions. This study presents a deep learning-based system for the detection and classification of facial acne lesions, combining object detection and image classification techniques. The YOLOv11-m model was employed to localize lesions in facial images, followed by classification using an EfficientNet-B0 network. To train and evaluate the system, the Acne21 dataset was adapted to a two-stage pipeline: bounding boxes were used for detection, while cropped regions were labeled into six classes for classification. The system generates lesion counts and calculates the Investigator’s Global Assessment (IGA) severity score based on clinical weights. Experimental results demonstrate the model’s effectiveness in detecting and categorizing different lesion types, offering a promising tool for preliminary acne self-assessment in mobile health applications. This approach contributes to the advancement of accessible dermatological care through AI-driven technologies.
Dealing With Strong Imbalance to Classifying Glomerular Lesions via Self-Supervised Learning
The histopathological analysis of renal glomeruli is fundamental for diagnosing kidney diseases, but it is hampered by challenges such as inter-observer variability, the high cost of expert annotation, and the inherent complexity of renal lesions, which often coexist. Self-Supervised Learning (SSL) offers a promising strategy to mitigate the reliance on large-scale annotated datasets. This work-in-progress paper presents a systematic comparison of SSL methods for classifying glomerular lesions on a challenging real-world dataset of 11,928 histopathology images. Our preliminary results demonstrate that the DINO method, significantly outperforms supervised baselines and other SSL techniques. An interesting finding from our data analysis is that several images depict glomeruli with multiple, co-occurring lesions, meaning that only normal glomeruli consistently represent a single, isolated class. This suggests that a standard multiclass classification approach may be suboptimal. Based on this finding, we propose the development of a novel two-stage, binarymultilabel pipeline as the main direction for our future work. This pipeline will first distinguish between normal and Lesion images and then apply a multi-label model to identify all specific lesions present, better reflecting clinical reality.
Solid Waste Detection and Classification System with Edge Computing Using Deep Learning
This paper presents an innovative intelligent system for the automatic detection and classification of municipal solid waste, aiming to improve the efficiency and reliability of selective collection processes. Using artificial intelligence and advanced deep learning techniques, such as the YOLOv8n model, in conjunction with TensorFlow Lite, the system is capable of identifying and classifying waste into four main categories: metal, paper/cardboard, glass, and plastic. The trained model was incorporated into a mobile application developed in Flutter, aiming to optimize selective collection and increase the accuracy of waste separation, promoting proper disposal. Users can easily interact with the application by sending or capturing images and receiving instant feedback on the type of waste detected, accompanied by standardized color codes to assist in the correct separation of materials. After comparative tests with different versions of the network, we chose to use YOLOv8n, with an average accuracy (mAP) of 95.08% and an F1-score of 93.12%. Developed for mobile devices, the system is an accessible tool that contributes to environmental sustainability, encouraging responsible disposal practices and facilitating use in edge computing scenarios.
Towards evaluating FLIM attention regions
FLIM (Feature Learning from Image Markers) has been used to interactively train convolutional networks from user-defined attention regions. Although FLIM has shown competitive results with large deep models, resulting in shallow networks trained with very few weakly annotated images, the performance of FLIM models is related to those attention regions. Much effort has been made to improve the FLIM framework itself, but no investigation has been conducted into these marked locations and their relationship to model performance. In this work, we propose to open ways to evaluate the image markers’ location impacts on FLIM. For that, we exploit multiple marker positions, determine their relevance, and create a color code for each location, resulting in a heatmap of marker locations that reflects the FLIM performance from those image regions. From our results, we showed how the FLIM performance differs depending on the background marker location, which provides some trends for better/worse marker scenarios.
Improving Fine-Grained Vehicle Classification via Multitask Learning and Hierarchical Consistency
Fine-Grained Vehicle Classification (FGVC) plays a key role in intelligent transportation systems, enabling the recognition of vehicle attributes – such as type, make, and model – from images. Such information supports vehicle identification and can complement automatic license plate recognition by enabling cross-checks and addressing cases with unreadable plates. However, existing approaches often treat these attributes independently, overlooking their hierarchical relationships and differences in task difficulty. This work-in-progress study explores the use of Multitask Learning (MTL) and hierarchical regularization to address these gaps. We evaluate seven deep learning models on a diverse dataset under three training setups: singletask learning, MTL with balanced optimization, and MTL with hierarchical regularization. Results show that MTL consistently improves classification accuracy, while incorporating hierarchical information significantly reduces semantic inconsistencies and enhances confidence calibration. In our best-performing configuration, hierarchy-violating errors dropped from 32.87% (singletask) to 4.10% (MTL with hierarchical regularization). These findings highlight the importance of modeling semantic relationships among attributes in FGVC and suggest promising directions for building more accurate and reliable classifiers. Future work will expand attribute granularity, investigate optimal task combinations, and benchmark against state-of-the-art methods.
A Study on Fine-Grained Motorcycle Classification for Intelligent Transportation Systems
Vehicle recognition from images is crucial to Intelligent Transportation System (ITS), supporting applications such as tolling, access control, and forensics. Fine-Grained Vehicle Classification (FGVC) extends this capability by identifying vehicles by make, model, and type. However, research has largely centered on four-wheeled vehicles, with motorcycles receiving limited attention despite representing a substantial share of traffic in many countries, including Brazil. This under-representation can reduce ITS effectiveness and fairness. This work-in-progress study addresses this gap by investigating Fine-Grained Motorcycle Classification (FGMC) in real-world ITS scenarios. We evaluate seven deep learning architectures under two training protocols for independent make and model classification. To enable this, we augment a widely adopted dataset for Automatic License Plate Recognition (ALPR) with motorcycle make and model annotations. Results show that FGMC is feasible within the studied context, yet performance is hindered by severe class imbalance, underscoring the need for improved balancing strategies. The results also reveal a drop in accuracy under challenging conditions, particularly at night or in low-light environments. Future directions include expanding the dataset to more diverse scenarios and exploring FGMC integration with ALPR to enhance overall vehicle identification accuracy.
Generation of Aerial Images with Controllable Diffusion Models: A Data Augmentation Case Study with ControlNet Fine-Tuning for Pine Forests
This work presents a method for aerial imagery dataset expansion through the fine-tuning of controllable diffusion models, focusing on the monitoring of pinus forests in Brazil. Starting from the pre-trained Seg2Sat model, which combines Stable Diffusion with ControlNet, this study performs a fine-tuning process on a custom dataset of 29 high-resolution images. It introduces a new semantic class, “pinus”, using multiclass segmentation masks and corresponding text prompts. The results, evaluated by Fréchet Inception Distance (FID) and CLIP Score metrics, demonstrate that the model successfully learns to generate the new class according to the spatial control provided by the mask. The study concludes that, even with an extremely limited dataset, fine-tuning ControlNet is a viable and promising approach for data augmentation in remote sensing and forestry monitoring applications.
Evaluation of Embedded OCR for Mobile Devices: Performance, Privacy, and Usability
This study presents a comparative analysis between three OCR technologies for mobile devices: Vision OCR, MLKit Mobile, and Tesseract Mobile. A total of 848 real examples of words extracted from urban environments were used, and the tools were evaluated with and without the application of preprocessing techniques. The metrics used enabled a complete evaluation of the performance of each solution. The results show that Vision OCR performs best, followed by MLKit and Tesseract with optimized preprocessing. Security aspects (processing on the device), usability (ease of operation, installation, and documentation, considering file size in embedded environments), and ease of integration were also analyzed. Native solutions offer superior performance with less need for adjustments, while Tesseract, despite requiring a more sophisticated pipeline, ensures maximum privacy because it is open source and controlled by the developer.
AirSnip: Gesture-Based AR Screenshot Enhanced by GenAI
This paper presents the development of an interactive tool for image capture in Augmented Reality (AR) environments, based on a custom mid-air hand gesture that mimics the behavior of a snipping tool. The system allows users to delimit a rectangular area in space using a gesture, capture a real-world view through the Meta Quest 3’s passthrough camera, and generate a 3D virtual frame with the resulting image. These frames can be manipulated using natural gestures (e.g., push, pinch) and are enhanced with personalized descriptions generated by a Large Language Model (LLM) via Google Gemini, with audio output through Text-to-Speech (TTS). Built using Unity and the Meta XR SDK, the prototype offers a seamless and intuitive interface for storytelling, education, and spatial memory. Although user studies are planned for future work, the tool demonstrates the technical feasibility of integrating gesture recognition, spatial rendering, and Generative AI in a unified AR experience.
Survey and classification of recommendations for evaluating visualization and usability quality criteria in Business Intelligence systems
The growing reliance on data for strategic decision-making intensifies the relevance of Business Intelligence (BI) tools. In this context, it is crucial to ensure the quality of user interactions and visualizations provided by these platforms to the end-user. However, there are gaps in the evaluation of BI tools quality in terms of usability and visualization quality criteria, especially with respect to the heuristic evaluation method. This paper presents partial results of an ongoing doctoral research project aimed at filling this gap. Through a literature review, we surveyed 22 recommendations for BI tools regarding visualization and usability aspects. We also mapped these recommendations to the visualization heuristics of Morroni et al. to identify similarities between groups of recommendations. We expect to use this classification to generate a new set of heuristics for evaluating BI systems, with the goal of enabling the improvement of these systems’ quality.
Real-Time Detection of Volleyball Player Movements with YOLOv8 to Support Rehabilitation
This paper presents a computer vision system based on YOLOv8 for detecting and classifying critical volleyball postures, serve, set, pass (bump), and block, alongside ball tracking in real time. Designed to support physiotherapist and coaches, the system enables objective monitoring of biomechanical correctness during rehabilitation, addressing one of the key challenges in injury recovery: ensuring accurate execution of sport-specific movements. The project builds upon previous clinical research on volleyball rehabilitation and was implemented using open source tools, leveraging Google Colab for training on a custom-labeled dataset. Results demonstrate promising detection accuracy, indicating potential for integration into physiotherapy workflows and future augmented reality(AR) rehabilitation systems.
Proxy Tasks Ensemble for Explainable Inference in Sensitive Data
Child sexual abuse imagery (CSAI) classification inherits several challenges by its nature. Due to the limited access to training data and the highly sensitive nature of the images in question, most solutions are not reproducible, not distributable, not explainable, and vulnerable to attacks. In this context, we propose the Ensemble for Inference in Sensitive data using Proxy Tasks (EISP) framework to realize explainable inference in sensitive data, without having to train directly in target images, by using an ensemble of Proxy Tasks related to CSAI detection, such as nudity detection, age estimation, and perceived gender classification. If the EISP system is provided with Proxy Tasks that correlate to the target task, it can be optimized with feature combination, realize predictions about the given task and provide explainability both by creating data visualizations, and extracting SHAP feature importance through each prediction. In this Work In Progress paper, we test the framework and explain its inner works using a public non-CSAI dataset.
Machine Learning Models for Predicting Mortality in Hemodialysis Patients
In Brazil, over 133,464 individuals with Chronic Kidney Disease (CKD) undergo hemodialysis, facing significant mortality risks. The mandatory biomarkers for monitoring these patients are specified by the 2014 Clinical Guideline of the Ministry of Health. Annually, more than thirty biomarkers are periodically evaluated; however, no critical evaluation of the predictive value of these biomarkers using machine learning (ML) has been conducted in Brazil to date. This paper aims to develop ML models to predict mortality outcomes in hemodialysis patients based on routine biomarkers. The goal is to investigate technologies that can assess the predictive effectiveness of clinical tests, ultimately improving patient quality of life and contributing to cost management within the Brazilian Unified Health System (SUS). This study utilizes data from hemodialysis patients in a retrospective cohort study conducted between 2012 and 2016 across 23 dialysis units in five Brazilian states. The features used in model development include biomarkers, patient profile variables, and clinical outcomes. Various ML approaches and algorithms are tested, including Decision Tree, Random Forest, Logistic Regression, XGBoost and TabPFN to identify and compare the most accurate predictive model. Among the tested models, TabPFN exhibited the best overall predictive performance, notably benefiting from balanced training data. Furthermore, the application of SHAP (SHapley Additive ExPlanations) provided clear and interpretable insights into the most influential biomarkers, which contributed to understanding the clinical plausibility of the results.
Dense Recognition for Understanding Ambiguities in Urban Environments of Autonomous Vehicles
In autonomous navigation of mobile robots and vehicles, dense scene understanding—through semantic segmentation and depth estimation—is essential to ensure safety and adaptability in complex urban environments. Despite recent advances in computer vision, such as vision transformers and foundational models, there are still few studies that unify dense recognition applied to mobile robots and autonomous vehicles. This work proposes a lightweight, integrated architecture that exploits the complementarity between these two tasks by combining representations extracted from foundational models like DINOv2 and Depth Anything. Semantic-geometric integration strategies, including depth map concatenation, weighted spatial attention, and cross-attention, are investigated to enhance segmentation robustness in ambiguous situations, such as reflections, occlusions, and unreal objects. The approach is evaluated on an embedded platform (Jetson AGX Orin), considering quantitative metrics (mIoU, AbsRel, FPS) and qualitative assessments, focusing on computational feasibility. Expected results indicate that, even with a frozen encoder, training limited to the heads combined with efficient strategies can achieve a relevant balance between semantic performance, geometric accuracy, and real-time resource usage.
A Comparative Study of Loss Functions for Short-Range Crime Forecasting: Enhancing Embedding Generation for Visualization Systems
Criminal incidents have complex socio-economic impacts and reduce the population’s perceived security. Accurate short-term crime-rate forecasting enables more efficient allocation of policing resources and better strategic decisions to mitigate criminal activity. Transformer-based architectures are effective at capturing complex temporal dependencies in time series forecasting; however, model behaviour and the structure of learned representations are strongly influenced by the training loss. In this work, we present a comparative analysis of Transformer and recurrent architectures trained with two different losses, the Mean Squared Error (MSE) and the Soft Dynamic Time Warping (Soft-DTW), with focus on short-term forecasting of vehicle thefts in São Paulo. Our goal is to produce embeddings that are more representative of crime dynamics and thus more useful for downstream tasks such as visualization and pattern analysis. We evaluate Autoformer, ITransformer, and Informer alongside LSTM and GRU baselines, using three performance metrics: MSE, MAE and DTW. Overall, models trained with the DTW-based loss achieved performance similar to, or slightly worse than, those trained with MSE; an important exception is the Autoformer, which showed improved accuracy with Soft-DTW at the 14-day horizon. We discuss several factors that likely affected these results: (i) the short forecasting horizons studied, (ii) the formulation of the prediction task (forecasting the entire aggregated series may not be optimal), and (iii) aggregation to daily city-level counts, which discards spatial heterogeneity and may remove salient signal. These findings motivate further experiments (e.g., multi-scale and spatially resolved forecasting) to more comprehensively assess the comparative effectiveness of Soft-DTW and MSE for criminal time series prediction.
Towards Efficient Training through Critical Periods
Critical Learning Periods encompass an important phenomenon involving deep learning, where early epochs play a decisive role in the success of many training recipes, such as data augmentation. Existing works confirm the existence of this phenomenon and provide useful insights. However, the literature lacks efforts to precisely identify when critical periods occur. In this work, we fill this gap by introducing a systematic approach for identifying critical periods during the training of deep neural networks, focusing on eliminating computationally intensive regularization techniques and effectively applying mechanisms for reducing computational costs, such as data pruning. Our method leverages generalization prediction mechanisms to pinpoint critical phases where training recipes yield maximum benefits to the predictive ability of models. By halting resourceintensive recipes beyond these periods, we significantly accelerate the learning phase and reduce training time, energy consumption, and CO2 emissions. Experiments on standard architectures and benchmarks confirm the effectiveness of our method. Specifically, we achieve significant milestones by reducing the training time of popular architectures by up to 59.67%, leading to a 59.47% decrease in CO2 emissions and a 60% reduction in financial costs, without compromising performance. Our work enhances understanding of training dynamics and paves the way for more sustainable and efficient deep learning practices, particularly in resource-constrained environments. In the era of the race for foundation models, we believe our method emerges as a valuable framework. Code and supplementary material available at https://github.com/baunilhamarga/critical-periods.
Multi-Pathology Segmentation of the Lumbar Spine
The diagnosis of spinal pathologies is complex due to the frequent overlap of multiple diseases in the same anatomical location, a scenario that current segmentation or classification methods do not efficiently address. This work presents an empirical study on the segmentation of multiple overlapping pathologies, proposing and systematically comparing three strategies: (i) a baseline binary class approach using independent models; (ii) a multi-class approach mapping disease combinations to unique labels; and (iii) a multi-label approach using parallel channels to explicitly model co-occurrence.We evaluated over 300 training and inference pipelines, combining five neural network architectures and three loss functions. Our preliminary results show that the multi-label strategy significantly outperforms the other approaches in both accuracy and computational efficiency, establishing a promising direction for developing robust, scalable diagnostic tools.
Terrain generation using Neural Transfer Style and Fractal Brownian Motion noise with constraints
In the gaming industry, recent technological and economic growth has made it one of the most lucrative markets, driving demand for solutions that reduce costs and complexity in development. Procedural Content Generation (PCG) has emerged as a promising resource, particularly in terrain generation, which remains technically challenging. Existing approaches can be grouped into three categories: AI-based methods that provide realistic details, geometric techniques that enable scalability, and rule-based methods that enforce structural constraints. However, issues such as limited variety, quality, and customization remain. To address these challenges, we propose a system that integrates a Transfer Style Network with Fractal Brownian Motion (FBM) noise under user-defined constraints. This hybrid design generates terrains that are realistic, customizable, and semantically rich. Results show that our method preserves user constraints while producing diverse terrains similar to real-world samples, offering developers a robust and efficient tool for accelerating terrain creation and enhancing variability in game environments.
Visual Identification System for Aedes Detection using Drones
The proliferation of diseases transmitted by the Aedes aegypti mosquito, such as dengue, poses a persistent challenge to public health systems worldwide. Traditional surveillance methods for identifying breeding sites are often laborintensive and limited in scale. This paper introduces VISADE (Visual Identification System for Aedes Detection), a system designed to automate this process through the use of Unmanned Aerial Vehicles (UAVs) and deep learning. By capturing and analyzing aerial imagery, VISADE employs a state-of-the-art object detection model, YOLOv11m, to identify and georeference potential breeding sites like discarded tires, buckets, and water tanks. A key aspect of our methodology is the use of high-resolution images (1200x1200 pixels) to enhance the detection of small objects, a significant challenge in aerial surveys. Our model, trained on a custom dataset collected in urban areas of Minas Gerais, Brazil, achieved a mean Average Precision (mAP@0.5) of 0.934, demonstrating high efficacy in identifying potential breeding sites. VISADE aims to provide public health authorities with an efficient, scalable, and low-cost tool, generating risk maps to direct vector control actions more effectively.
3D Medical Imaging by using Point Cloud Generation
Three-dimensional (3D) reconstruction of anatomical structures from medical images represents a significant advancement in the healthcare field—a topic already explored in various studies. Traditional methods often rely on contrastenhanced computed tomography (CT), which is contraindicated for certain patients. To address this limitation, this work proposes a new methodology for 3D lung modeling. The method consists in generating a unified point cloud from segmented CT slices, followed by the construction of a polygonal mesh using Poisson surface reconstruction. To enhance result visualization, a graphical user interface was developed to simultaneously display the reconstructed 3D model and the original DICOM slices. The results of this new approach confirm the feasibility of the project, establishing an alternative method for the 3D representation of lungs.
Multi-object triangulation and 3D footprint tracking for multi-camera systems
This paper presents a method for 3D footprint triangulation and tracking of multiple objects from different classes using a multi-camera system with overlapping views. The proposed method is designed to operate in complex environments such as Intelligent Spaces, where localization of heterogeneous entities (e.g., humans, robots, and furniture) is essential. Our approach integrates an object detection model (YOLO neural network) and a tracking algorithm (BoT-SORT). To establish cross-view correspondences, we exploit epipolar geometry by computing the normalized cross-distance between detection pairs and introducing a temporal consistency mechanism that favors previously matched pairs. Then a greedy matching algorithm, with filtering heuristics, provides robust association across camera views. From the valid matches, a graph is constructed, and connected components are triangulated to estimate the 3D footprint positions of each object. Our system requires only camera calibration and supports a flexible number of views, making it suitable for deployment in real-world multi-camera setups. The method is first evaluated by comparing reconstruction results against ArUco marker position estimation. Then a set of experiments demonstrates the effectiveness of the algorithm in tracking multiple entities of the same class, as well as different object classes. Due to its modular and generalizable structure, the framework supports any detector and tracker combination, while the proposed method is applicable to a wide range of scenarios.
Topological Data Structure for Manipulation Operations of 2D/3D Graphic Objects
Current tools for 3D mesh manipulation and advanced topological operations are limited or specialized. This work presents a C++ graphical application for visualizing and manipulating two-dimensional and three-dimensional meshes, aiming to fill this gap. The methodology is based on the Mate Face Topological Data Structure, using explicit data representation and preprocessing for optimization. Results validate its functionality on various meshes, and performance comparisons show that the proposed approach, with preprocessing, is significantly faster in topological operations, especially on complex meshes.
Development and Evaluation of Advanced Morphological Algorithms for Automated Fish Measurement in Sustainable Fisheries
Accurate identification and measurement of fish populations present critical challenges in ecosystems characterized by significant morphological variability. This work presents morphological algorithms developed to enhance automated fish measurement processes in freshwater fisheries, with potential applications in coastal and marine ecosystems. Key contributions include developing algorithms tailored for non-linear morphologies and integrating these advancements into the ”ICTIOBIOMETRIA” application, which systematically records fish biometric data such as weight, dimensions, and capture locations. The dataset used originated from three primary sources: images inherited from prior collaborators, samples collected in partnership with the Ichthyology and Fisheries Laboratory at the Federal University of Rondônia, and acquisitions from fish markets. These sources provided 588 images representing diverse fish species from the Madeira River basin in Porto Velho, Rondônia, located in the Western Amazon. A dataset from established authors was also used as a reference to validate the proposed measurement models, ensuring a comprehensive comparative analysis that reinforced the robustness of the developed methods. To address challenges posed by larger fish and those exhibiting non-linear shapes, algorithms were developed and implemented to manage these characteristics effectively. Among the three measurement methods evaluated, the skeletonization method demonstrated superior performance, reducing the relative total error by 1.56%, and demonstrating robustness across diverse morphologies.
Framework for Fairness Evaluation in Classification Models Applied to Healthcare
Artificial intelligence (AI) models have been widely used in medical applications, particularly in tasks such as image classification. However, growing concerns have emerged regarding algorithmic bias, especially in scenarios where sensitive attributes like gender or age influence model performance. In this work, we propose an interactive visual framework for evaluating fairness across different classification models, incorporating a variety of algorithmic fairness metrics. As a case study, we have chosen the automatic detection of clinical findings in chest X-rays, using three public datasets and variants of the EfficientNet architecture. The results show that even in models with high performance according to traditional classification metrics, the fairness metrics reveal significant disparities between certain groups of patients, reinforcing the importance of specific approaches for equity assessment. The proposed framework proves to be useful for assisting in the critical analysis of models in medical contexts.
Detection of Retinal Anatomical Structures and its Application to Image Quality Assessment
Given the general shortage of ophthalmologists in Brazil, automated retinal image screening emerges as a fundamental tool to promote early diagnosis of retinal diseases and prevent, for example, blindness. Motivated by this, we propose the use of YOLO-based object detection models to detect two retinal structures – the optic disc and the macular region – which play a crucial role in ophthalmological examinations. As a case study, based on detections performed on BRSET images, we determined the distances between the structures and the retinal edges. We found that the calculated distances do not always meet the minimum distance established in the Image Quality Assessment Image Field protocol. The results highlight the ability of YOLO-based models to reliably detect anatomical structures in retinal images and their potential to contribute to improving analyses that rely on these detections.
Evaluating Simulation Platforms for Visual Affordance Understanding in Computer Vision
Affordance understanding in computer vision goes beyond object recognition — it involves interpreting scenes in terms of potential agent-object interactions. This work examines the role of simulated environments in supporting such reasoning in visual scenes. Three simulation tools (Gymnasium, SUMO, and CARLA) are evaluated regarding their suitability for affordance research, with special attention to video-based data generation, multimodal perception, and interaction modeling. Within the Gymnasium library, selected environments are compared based on criteria such as element countability, scene diversity, and logging capabilities. The analysis identifies key limitations in existing tools and underscores the need for scalable, userconfigurable platforms designed to support perception, learning, and generalization in affordance-centric applications.
Pointing Gesture Recognition from 3D Human Skeleton Data
Gesture recognition in Human-Machine Interaction (HMI) refers to the automatic detection of human gestures, assigning semantic meaning to physical movements and enabling interaction with computers, robots, or the analysis of human behavior. This work proposes a method for static gesture recognition, focusing specifically on the “pointing” gesture, based on 3D human skeletons reconstructed through a multi-camera system. The objective is to automatically detect the presence or absence of the gesture using spatial pose data derived from the three-dimensional reconstruction of the human body from multiple viewpoints. Following manual annotation and segmentation of the gesture sequences, structural features were extracted from the skeletons. A normalization process was applied, which performs translation to the origin and rotation to align the skeleton with the X-axis. Classical supervised machine learning models were then employed to classify body poses: Logistic Regression, Random Forest, and Decision Tree. Experiments were carried out using Leave-One-Subject-Out (LOSO) cross-validation. The results demonstrate the viability of the proposed approach for applications in intelligent environments, where recognizing the pointing gesture could be used to indicate goals, highlight objects of interest, or define target positions for mobile robots.
Evaluation of visual language model quantization for deployment in hospital environments
The deployment of state-of-the-art Artificial Intelligence models in hospitals is limited by ethical issues and high computational costs, especially for smaller institutions and in resource constrained countries. This study evaluates quantization as a strategy to enable the local execution of Vision and Language Models (VLMs) for the interpretation of chest radiographs. Using the CheXagent, CheXagent 2, and MedGemma models, we apply 4-bit Post-Training Quantization (PTQ) and analyze the trade-off between accuracy (F1-Score), memory usage (VRAM), and inference time. The results show that quantization drastically reduced VRAM usage, with MedGemma demonstrating less accuracy loss compared to CheXagent. The non-quantized CheXagent 2 achieved the highest accuracy and lowest inference time. Although CheXagent 2 is relatively lightweight, its hardware requirements still limit its deployment and reinforce that quantization is a viable tool for adapting advanced megamodels to the potential limitations of a clinical environment.
Vessel-Width-Based Metrics and Weight Masks for Retinal Blood Vessel Segmentation
Visual inspection of fundus images is widely used to diagnose various eye diseases, such as diabetic retinopathy and glaucoma. Alterations in the blood vessel structure are known to be related to these and other diseases. Therefore, automatic segmentation of vessels is of great interest. Existing methods, in general, struggle in segmenting thin vessels accurately. Motivated by this, we propose weight masks built based on the thickness of the vessels to be used with the well-known binary-cross entropy (BCE) loss. We also propose segmentation performance metrics conditioned on the vessel thickness. Then, we experiment different compositions of the loss function with the U-Net architecture to better understand the impact of each weight mask, especially on the segmentation of thin vessels. The experimental results indicate that the proposed weight masks present better performance regarding thin vessel segmentation, while, at the same time, maintaining competitive overall performance.
Binary Image Denoising via Incremental Neighborhood-Based Energy Optimization: A Markov Random Field Approach
A digital image may exhibit undesired noise variations, making it necessary to apply preprocessing methods to reduce noise while preserving the original structure. In this context, this paper proposed a Markov random field-inspired energy approach for optimizing binary noise reduction, considering a variable neighborhood set. Our algorithm combines Iterated Conditional Modes with Markovian prior information about the image to compute the energy associated with each pixel based on its local neighborhood, which is progressively expanded at each iteration. Experimental results on binary images corrupted with 10% random noise demonstrate that the proposed approach achieves an agreement of up to 99.85% with the original noisefree images. Future research will focus on incorporating more prior knowledge about the image to enhance the energy model, as well as its extension to grayscale images, along with the exploration of new metrics and configuration strategies.
Balancing Known and Unknown in Open-Set Domain Adaptation via Attention
Open-set unsupervised domain adaptation (OS-UDA) for video action recognition is a critical yet largely underexplored problem. Real-world applications must be robust to distribution shifts between training and deployment data (domain gap) and capable of identifying actions not seen during training (open-set). We introduce OS-DTAB, a lightweight, plug-and-play Vision-Transformer block designed to enhance existing OS-UDA frameworks. OS-DTAB replaces the standard clip-aggregation mechanism with a domain-transferable attention module. This component compels the model to focus on spatio-temporal cues that are simultaneously transferable and open-set aware. Our experiments on the HMDB↔UCF benchmark show that OS-DTAB sets a new state-of-the-art, achieving a Harmonic Open-Set (HOS) score that surpasses models with more robust backbones.
Expanding the Generalization Capability of Deep Learning Methods for Facial Expression Recognition
Facial expression recognition is a fundamental task in computer vision, yet models often suffer from poor generalization to unseen data. This work aims to enhance the robustness and generalization of the CAFE architecture, a state-of-the-art model designed for FER in real-world scenarios. To this end, we propose a modification to its loss function by introducing a sparsity regularization term. This approach compels the model to focus on a more concise and discriminative subset of facial features, penalizing the activation of irrelevant contextual information. The experimental evaluation was conducted by comparing the modified model with its original version on intradomain and cross-domain benchmarks. The results demonstrate that sparsity regularization yielded a significant performance gain, particularly in cross-domain scenarios, thus confirming the hypothesis that the technique enhances generalization ability. We conclude that inducing sparsity is an effective strategy for developing more reliable and adaptable FER systems, capable of operating with greater accuracy in diverse and uncontrolled environments.
Fingerprint Matching Using Pores Extracted with Machine Learning
Fingerprint recognition is widely used for biometric applications due to its uniqueness and convenience. However, traditional systems often overlook level 3 features, particularly pores, which can improve precision, especially in partial or latent fingerprints. The integration of these features is highly beneficial in contexts such as forensic analysis, where complete fingerprint patterns are not always available. This work proposes a pore detection model, which is a convolution neural network with U-Net architecture followed by a pore matching algorithm, applied to the dataset L3-SF, a collection of extremely realistic, labeled synthetic fingerprints. The results demonstrated high effectiveness, achieving high accuracy in pore detection and matching even in partial fingerprint images, outperforming many existing methods and, consequently, the power of deep learning applied to biometrics.
Multiclass Classification of Denim Fabric Defects Using Convolutional Neural Networks
In denim fabric manufacturing, quality assessment traditionally relies on visual inspection by weavers, as guided by ABNT NBR 13484. This manual, post-production process can delay defect detection, allowing issues to persist and recur, which disrupts the production chain. To address this, we developed a deep neural network for multiclass classification of common fabric defects, including double yarns, coarse ends, harness misdraws, slack ends, broken wefts, and soft wefts. Our model integrates convolutional layers for feature extraction and linear layers for classification. It was trained on 2,985 images captured using a low-cost camera mounted above a textile manufacturing loom in a denim manufacturing laboratory, replicating realworld inspection conditions. Our model achieved a classification accuracy of 89%, outperforming other state-of-the-art machine learning algorithms. These results demonstrate the potential for real-time defect detection during production, reducing material waste and improving overall fabric quality.
Named Entity Extraction in Industrial Diagrams with Computer Vision and MLLMs
This paper presents a comprehensive evaluation of methods for extracting named entities (tags) from Piping and Instrumentation Diagrams (P&IDs), critical documents used throughout the lifecycle of industrial facilities. Efficient indexing and retrieval of information from such technical documentation play a key role in improving industrial project management. The paper proposes integrating deep neural networks for object detection with state-of-the-art Multimodal Large Language Models (MLLMs), specifically GPT-4o and LLaMA-3.2-Vision-90B. The methodology includes a preprocessing stage to isolate and downscale symbols before tag extraction. Experimental results on a challenging dataset of 30 real-world symbols show that MLLM-based approaches significantly outperform traditional OCR techniques (82%), achieving near-perfect accuracy in most cases. The approach demonstrates the robustness of MLLMs under noisy and degraded conditions, offering a practical solution for augmenting engineering document databases with reliable named entities. These findings highlight the potential of MLLMs to enable robust, high-precision tag text extraction, thereby streamlining the indexing and management of complex engineering documentation. All 30 test symbols used in this study are provided as supplementary material to ensure reproducibility.
Neural Network Compression for Visual Quality Control in Industrial Edge Computing
Ensuring that customers receive defect-free products is a big challenge for industries. Visual inspection is a method that can retain defective products before they leave the production line. Deep Learning has enabled us to automate this process using very complex neural networks. However, edge devices are resource-constrained, and the deployment of very deep models may not be feasible. To address this problem, our methodology investigated the application of Knowledge Distillation and Network Quantization to reduce the requirement for resources while trying not to lose performance, enabling deployment on edge computing devices. Knowledge Distillation consists of training a small model (student) under the guidance of a complex one (teacher). Network Quantization reduces the numerical precision of the weights to further decrease the storage size of the model. Our methodology was evaluated in the MVTecAD, DML, and VAD datasets. The results were compared between the teacher and student models. To measure the benefits brought by compressed techniques, we also fine-tuned the student model without the teacher’s guidance and compared it to our approach. The results show that our methodology reduced the model size to 5.5% of the teacher’s size to the best-case scenario, being 36 times faster, while preserving similar performance to the complex model for most objects.
Impact of Grid Refinement on a Non-Conventional Mesh Structure: A Case Study with INSIM-FT
To support decision-making, the oil and gas industry relies on flow simulators to evaluate multiple production scenarios. This demands simulation tools that are both accurate and computationally efficient. INSIM-FT (Interwell Numeric Simulation Model with Front-Tracking) is a hybrid flow simulator that combines physical modeling with data-driven components, using a non-conventional mesh built through Mitchell’s Best Candidate algorithm and Delaunay Triangulation. Although finer meshes are expected to improve spatial representation, they also increase computational costs and may interfere with the performance of data-driven elements. This trade-off highlights the need to assess the influence of the number of imaginary nodes used in INSIM-FT. This work investigates how mesh configuration impacts the accuracy and performance of the simulator through a case study of a heterogeneous reservoir. Considering randomly generated models whose parameters are adjusted by the datadriven component, the results show that mesh refinement affects the spread of the adjusted prior models around the mean. Nevertheless, the mean of the simulations remains reasonably close to the observed data in all cases.
A Study of Computational Vision Methods for Corrosion Detection in Industrial Assets
Corrosion has a significant impact on the global economy, leading to high costs for preventing damage, repairing deterioration, and replacing defective components. Industry 4.0 brings computational vision as a key role for visual inspection of assets ensuring quality and reducing the downtime. This study conducts a comparative analysis of computer vision techniques for detecting corrosion, with an emphasis on segmentation neural networks built upon U-Net architectures, focusing on the trade-off between segmentation accuracy and computational efficiency. We benchmark four U-Net-based segmentation models using pretrained backbones: MobileNetV2, ResNet152, InceptionV4 and VGG16 on a publicly available data set of corroded metallic surfaces. All models are trained using the full dataset and evaluated based on per-image F1-score and training time. Our results show that InceptionV4 achieves the highest F1-score (0.895), while MobileNetV2 offers a comparable score (0.892) with nearly half the training time. These findings suggest that lightweight backbones can deliver accurate segmentation with significantly reduced computational cost, making them promising candidates for near real-time industrial inspection systems under resource constraints.
LLM-Generated Dataset for Speech-Driven 3D Facial Animation Models with Text-Controlled Expressivity
The field of speech-driven 3D facial animation has a fundamental limitation regarding style control by natural language: scarcity of datasets that pair nuanced language with direct, controllable facial parameters. To address this bottleneck, we introduce two core contributions: (1) a large-scale, synthetically generated dataset of over 53,000 items that link emotional transcripts, descriptive tags, and explicit facial descriptions to 51-dimensional blendshape vectors; and (2) a CLIP-based alignment module that learns a shared semantic manifold between this text and the corresponding expressions. Our model employs a Transformer-based blendshape autoencoder and a frozen CLIP text encoder, optimized via a cross-modal objective. We evaluate the structure of this learned manifold using t-SNE projections and latent space traversals, which demonstrate semantically coherent clustering and smooth, plausible transitions in expression. We release our dataset specification, generation methodology, and training recipe to facilitate reproducible research in text-driven facial animation.
Between Memory and Ethics: Real Human-Educators Transformed into Virtual Human-Educators
This article examines the ethical dilemmas that arise from the transformation of real human-educators into virtual human-educators using artificial intelligence, particularly in posthumous contexts. Based on the project “Dados Além da Vida” (DAVI – Data Beyond Life), it investigates the boundaries between memory and technology in the recreation of deceased educators using generative models. The study explores how digital traces, such as voice, scholarly productions, and images, are mobilized to simulate presence and interaction, raising tensions between memory and ethics. Considering the current capabilities of Artificial Intelligence, the article discusses whether it is ethically acceptable to convert the memory of real humaneducators into virtual human-educators, and what implications this entails.
Replacing Real Faces with Virtual Humans: A New Paradigm for Facial De-Identification
Face de-identification seeks to protect individual privacy in visual data while preserving useful facial features. In this work, we propose a novel approach that replaces original facial images with realistic 3D virtual human representations. These persistent virtual faces, generated via automatic reconstruction (Deep3D) and manual tailoring (MetaHumans), serve as identity representatives, enabling face recognition without storing real biometric data. We created multiple datasets of virtual faces based on two public datasets (CFD and MEAD), while also applying transformations on a few selected virtual faces, such as age progression, skin tone variation, and emotional expression changes. Our experiments, using Adam’s face recognition algorithm, show that even modified virtual faces consistently match their real counterparts with high accuracy.
Guidelines for Modeling Expressivity in Virtual Patients for Healthcare Training
Virtual patients are increasingly being used in healthcare education to support the development of communication and social skills. However, their effectiveness depends on the integration of expressive realism across verbal and nonverbal communication modalities. This paper proposes a set of guidelines for modeling emotional expressivity in virtual patients by combining facial expressions, body posture, and vocal prosody. Grounded in established affective science literature—including Ekman’s basic emotions, the Facial Action Coding System (FACS), the Body Action and Posture (BAP) framework, and GeMAPS acoustic parameters—these guidelines synthesize expressive features for six core emotions: joy, sadness, anger, fear, disgust, and surprise. Our goal is to support the development of emotionally expressive virtual patients for medical simulations and virtual reality training.
Prostate Cancer Histopathology Classification Using Multi-Instance Learning and Vision Transformers
Prostate cancer is the most frequently diagnosed malignancy among men worldwide and remains a major public health concern. Although histopathological evaluation of prostate biopsies is the diagnostic gold standard, it faces limitations such as sampling errors, inter-observer variability, and limited access to specialized pathologists, underscoring the need for computational support. In this study, we propose a computer vision framework for automated classification of prostate histopathology using a multiple instance learning (MIL) approach built upon DINOv2-based foundation model embeddings. The system was trained and validated on a large dataset comprising prostate tissue whole-slide images from TCGA, GTEx, and a private Brazilian laboratory (ARGOS Patologia). Data were divided into training, testing, and an independent validation set, with patientlevel separation to prevent data leakage across subsets. The proposed model achieved an accuracy and sensitivity of 94% and an area under the receiver operating characteristic curve (AUC) of 98% on the independent validation set, demonstrating robust generalization across heterogeneous real-world samples. These results highlight the potential of MIL combined with foundation model representations to support reliable and scalable prostate cancer diagnosis.
Segmentation is better when shared: a review of public H&E histological images datasets
The evaluation of histological images is a key step in cancer diagnosis, but it is a time-consuming and subjective process. To overcome these challenges, computer-aided diagnosis systems have emerged to offer a faster and more accurate analysis. Among the steps of these systems, image segmentation plays a crucial role by isolating regions of interest for further examination. In this context, this systematic review investigates the use of publicly available datasets in histological image segmentation analysis using Hematoxylin-Eosin (H&E) staining. The review addresses 15 guiding questions, covering various aspects, including the most common segmentation techniques, evaluation metrics, and existing limitations in the literature.
Micro-FTIR Hyperspectral Imaging Classification for Oral Cavity Histopathology Analysis
Hyperspectral imaging (HSI) has emerged as a promising tool for integrating spatial and biochemical information in computational pathology analysis. While most studies on oral cavity cancer have employed reflectance-based HSI or Raman spectroscopy in the visible and near-infrared ranges, the potential of mid-infrared micro–Fourier transform infrared (micro-FTIR) spectroscopy remains largely unexplored. This study investigates the feasibility of using micro-FTIR hyperspectral data for the classification of oral cavity tissues. Mid-IR spectra provide detailed biochemical information, including protein, lipid, and nucleic acid signatures, which may be clinically relevant for early diagnosis and characterization of tumor margins. Tissue samples were imaged using micro-FTIR spectroscopy, and voxel-level spectra were preprocessed and classified using a fully-connected neural network. The proposed model achieved an accuracy of 88.41%, sensitivity of 87.64%, and area under the receiver operating characteristic curve (AUC) of 96.51%, demonstrating that micro-FTIR–based HSI can successfully differentiate between healthy and malignant oral cavity tissues. These findings provide the first systematic evidence supporting the clinical potential of mid-infrared spectroscopy in oral oncology.
Can few-shot learning methods segment global-sclerotic glomeruli in WSI?
Glomeruli are central to the analysis of whole slide images (WSIs) in kidney biopsies, as they are affected by a wide range of lesions that reflect both causes and consequences of renal diseases. In automated WSI analysis using machine learning, glomeruli are typically the first regions to be segmented or detected, enabling subsequent diagnostic tasks. The Bowman’s capsule (BC) usually serves as the primary anatomical marker, delineating the glomerulus from the surrounding interstitium. While this boundary is preserved in normal and partially sclerotic glomeruli, globally sclerotic glomeruli often lose the BC, appearing visually borderless and posing a major challenge for automatic detection. In recent years, several studies have addressed glomerulus segmentation; however, few have focused on the specific challenge of globally sclerotic glomeruli, often analyzing them only in isolated per-cropped images. In this work, we present a comparative evaluation of four few-shot semantic segmentation (FSS) methods: DMACA, VAT, HSNet, and PMNet. These approaches aim to learn from only a few labeled examples, addressing the data scarcity of globally sclerotic glomeruli. These methods were applied to three classes of glomeruli: those with well-defined borders, partially borderless glomeruli, and globally sclerotic borderless glomeruli, using the Dice metric. Our results highlight the intrinsic difficulty of segmenting globally sclerotic glomeruli from WSIs, with a mean Dice score across all the evaluated methods of only 0.02 when evaluated at the wholeslide level. In contrast, per-crop evaluations yielded markedly higher performance, with mean Dice scores reaching 0.93 for globally sclerotic glomeruli.
Oral Squamous Cell Carcinoma Detection: A Comparison of CNNs in Histopathological Images
Oral Squamous Cell Carcinoma (OSCC) is the most prevalent form of oral cancer, accounting for approximately 90% of cases worldwide. Early and accurate diagnosis is critical for improving patient outcomes, yet traditional histopathological analysis remains subjective and time-consuming. This study presents a systematic benchmark comparing the performance of eleven convolutional neural network (CNN) architectures for the automated classification of OSCC in histopathological images. Using a publicly available dataset, all models were trained and evaluated under uniform conditions. Metrics such as accuracy, recall, precision, F1-score, and AUC-ROC were employed to assess performance. Results demonstrated that EfficientNetB3 achieved the highest accuracy (94.12%) and overall robustness, outperforming other architectures. This study provides a comprehensive comparative analysis of CNNs for OSCC detection, highlighting the potential of modern architectures like EfficientNet to enhance diagnostic precision. The standardized benchmark established here facilitates future research and clinical integration of deep learning tools for histopathological cancer diagnosis.
Semi-Supervised Learning for Glomerular Crescent Classification
Glomeruli, the fundamental structures of the kidney responsible for blood filtration, are susceptible to various pathologies, including glomerular crescents. This work investigates the application of semi-supervised deep learning (SSL) for the classification of histopathological images containing these lesions. We evaluate several configurations of a deep learning model for classifying crescent versus normal glomeruli. Our investigation specifically compares the impact of varying the ratio of labeled to unlabeled data and the effect of different input perturbations (e.g., Random Flip + Random Crop) on model performance. Based on accuracy, precision, sensitivity, and F1-score, the model trained with four labeled folds and using Random Flip + Random Crop perturbations emerged as the most effective configuration. In a separate experiment, we expanded the classification task to distinguish crescent from non-crescent glomeruli by adding images of three other lesion types to the dataset. Our results show that a combined training approach using Mean-Teacher and Supervised Contrastive Learning (SCL) did not yield significant performance improvements compared to the model trained solely with the Mean-Teacher method.
Classification of prostate histological images using Vision Transformers: an analysis with stain normalization and ensemble learning
Prostate cancer is the second most common cancer in men worldwide, diagnosed via histopathological evaluation of H&E-stained images. Gleason grading, however, is subjective and prone to inter-observer variability. Deep learning-based computer-aided diagnosis systems offer promising support, but stain color variations pose a challenge, motivating normalization algorithms. This study evaluates color normalization on H&E prostate cancer image classification using a pre-trained ViT and eight classifiers, including a majority voting ensemble. Binary classification on a public dataset compared benign and malignant cases across two normalization methods (SW-CCN and BKSVD) and original images. Results showed original images yielded superior ViT and classifier performance, despite more malignant cases being misclassified as benign. SVM with ViT feature extraction achieved the best overall performance, surpassing both the ensemble and ViT classifier.
Hanna Beatriz Couto Monteiro Fernandes de Castro
Ensemble-Based CNN Approach for Gastric Cancer Classification in Histopathological Images
Gastric cancer is one of the leading causes of cancer-related deaths worldwide and is often diagnosed at advanced stages. Histopathological analysis is essential for diagnosis but heavily relies on the specialist’s expertise and is prone to human error. This study presents the development of a model based on Convolutional Neural Networks (CNNs) for identifying gastric cancer in histopathological images. The approach incorporates transfer learning, data augmentation, cross-validation, and an ensemble combining ResNet101, MobileNet, and EfficientNetB3. The model achieved an accuracy of 95.09% and a Kappa score of 89.72%. The results highlight the model’s strong potential for practical application in supporting medical diagnosis.
Quantitative Morphometric Analysis of Oral Epithelial Dysplasia using Deep Learning-Based Nuclear Segmentation
Oral Epithelial Dysplasia (OED) is a histopathological diagnosis for potentially malignant lesions with a variable risk of malignant transformation. The conventional grading of OED relies on morphological assessment, a process known for significant inter-observer variability, which limits its prognostic reliability. This study leverages computational pathology to address this challenge by employing a deep learning-based tool, Cellpose, for the precise segmentation of epithelial cell nuclei. The objective was to facilitate the objective extraction and analysis of key nuclear morphometric features, such as area, perimeter, and compactness, from digitized histological images of OED. We utilized 30 H&E-stained images from a spectrum of diagnostic categories, including normal oral mucosa, dysplasias of varying grades, and oral squamous cell carcinoma (OSCC). The results demonstrated that quantitative features, particularly the variance in nuclear area (anisonucleosis), serve as a robust differentiator between diagnostic grades, with the most significant changes observed in OSCC. By providing a reproducible and quantitative framework, this deep learning-driven approach represents a significant step towards a standardized diagnostic support system for OED, with future potential for accurately predicting malignant transformation risk.
Domain-Specific Seed Removal for Biomedical Image Segmentation using SICLE
Superpixels through Iterative CLEarcutting (SICLE) is an efficient framework for image segmentation that operates on an iterative principle of seed removal to refine the final result. While effective for general purposes, its reliance on standard seed removal criteria limits its performance in specialized domains. This is particularly evident in biomedical analysis, where the goal is often to isolate a single object of interest using a low number of superpixels, a task where generic criteria often fail. To address this limitation, this work proposes two seed removal criteria: (i) a Position-Based Criterion that leverages prior anatomical knowledge to guide segmentation in medical images; (ii) a Color-Based Criterion specialized for identifying targets in pathological images based on their distinct color signature. By replacing generic heuristics with these domain-specific functions, we demonstrate that SICLE can be transformed into a more robust and specialized tool for targeted biomedical image analysis, significantly improving object delineation accuracy, including situations with a small quantity of superpixels.