Transactions of the ASAE · 2004 · 19 citations · 0 references
AgricultureAnimal SciencesComputer ScienceComputer VisionFood EngineeringMeat ScienceAgricultural Machine VisionImage AnalysisImage SegmentationMeat ClassificationMeat GradingMeat QualityAutomated GradingBeef GradingError ClassificationImage-based GradingImage-based Quality GradingMorphological TransformationQuality GradingVisual Meat Grading
A computer vision system was developed to support automation of beef quality grading. Images of beef steakswere acquired for algorithm development. Fat and lean were differentiated using a fuzzy c-means clustering algorithm. Segmentationof the longissimus dorsi (l.d.) muscle is required because experts assign quality grades based primarily on visualappraisal of the l.d. A robust segmentation algorithm was developed using convex hull procedures. The l.d. was segmentedfrom the steak using morphological operations of erosion and dilation. At the end of each iteration of erosion and dilation,a convex hull was fitted to the image, and compactness was measured. Iterations were continued to yield the most compactl.d. Classification error in segmentation was 1.97%. Average error pixel distance of segmentation by the computer visionsystem was 4.4 pixels.