Viable Yeast Identification using Bag of Visual Words in Colored images - Souza, Junior; Weber, Vanessa; Gonçalves, Ariadne; Alvarez, Marco; Cereda, Marney; Gonçalves, Wesley; Odakura, Valguima; Pistori, Hemerson (Anais do Workshop de Visão Computacional (WVC), 2020)
In this research it is reported a system to automate the process of identification of viable yeasts whose population control is a crucial task in the ethanol production process. The identification and counting of yeasts made by human vision under a light microscope, is repetitive and susceptible to errors. We used computer vision techniques such as BoVW, Color Coherence Vectors (CCV), Color Moments (CM), Bag-of-Color (BoC) and Opponent Color (OpC) were applied for extracting characteristics that were classified by the Naive Bayes, KNN, SVM and J48 algorithms in 2614 images of yeasts separated into three classes: viable, non-viable and background. The results were analyzed using software R, which in the ANOVA test resulted in a p value equal to 2e􀀀16 indicating a significant difference between the techniques. The OPC with SVM classifier showed the highest performance using the PCC Percent Correct Classification metric, about 95% compared to other techniques.