Reducing Computational Costs of an Embedded Classifier to Determine Leather Quality - Sampaio, Fausto; Silva, Lucas Costa da; Rebouças Filho, Pedro Pedrosa; Silva, Elias Teodoro (Anais do Simpósio Brasileiro de Engenharia de Sistemas Computacionais (SBESC), 2017)
Embedded computer vision applications have been incorporated in industrial automation, improving quality and safety of processes. Such systems involve pattern classifiers for specific functions that, many times, demand high memory footprint and processing time. This work suggests a strategy to choose GLCM (Gray Level Co-occurrence Matrix) features for an SVM classifier that can reduce computer resources utilization while preserving high classifier accuracy. Experimental results show a computing time of 3.18s, with an accuracy of 90%, to classify images of 69 × 92 pixels. This result will permit to embed computer vision applications in low-cost platforms.