12 January 1999 Color image classification systems for poultry viscera inspection
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Proceedings Volume 3544, Pathogen Detection and Remediation for Safe Eating; (1999) https://doi.org/10.1117/12.335770
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States
Abstract
A neuro-fuzzy based image classification system that utilizes color-imaging features of poultry viscera in the spectral and spatial domains was developed in this study. Poultry viscera of liver and heart were separated into four classes: normal, airsacculitis, cadaver, and septicemia. Color images for the classified poultry viscera were collected in the poultry process plant. These images in RGB color space were segmented and statistical analysis was performed for feature selection. The neuro-fuzzy system utilizes hybrid paradigms of fuzzy interference system and neural networks to enhance the robustness of the classification processes. The results showed that the accuracy for separation of normal from abnormal livers were 87.5 to 92.5% when two classes of validation data were used. For two-class classification of chicken hearts, the accuracies were 92.5 to 97.5%. When neuro-fuzzy models were employed to separate chicken livers into three classes (normal, airsacculitis, and cadaver), the accuracy was 88.3% for the training data and 83.3% for the validation data. Combining features of chicken liver and heart, a generalized neuro-fuzzy model was designed to classify poultry viscera into four classes (normal, airsacculitis, cadaver, and septicemia). The classification accuracy of 86.3% was achieved for the training data and 82.5% accuracy for the validation.
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Kevin Chao, Kevin Chao, Yud-Ren Chen, Yud-Ren Chen, Howard Early, Howard Early, Bosoon Park, Bosoon Park, } "Color image classification systems for poultry viscera inspection", Proc. SPIE 3544, Pathogen Detection and Remediation for Safe Eating, (12 January 1999); doi: 10.1117/12.335770; https://doi.org/10.1117/12.335770
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