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Proceedings Paper

Beef quality grading using machine vision
Author(s): S. Jeyamkondan; N. Ray; Glenn A. Kranzler; Nisha Biju
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Paper Abstract

A video image analysis system was developed to support automation of beef quality grading. Forty images of ribeye steaks were acquired. Fat and lean meat were differentiated using a fuzzy c-means clustering algorithm. Muscle longissimus dorsi (l.d.) was segmented from the ribeye using morphological operations. At the end of each iteration of erosion and dilation, a convex hull was fitted to the image and compactness was measured. The number of iterations was selected to yield the most compact l.d. Match between the l.d. muscle traced by an expert grader and that segmented by the program was 95.9%. Marbling and color features were extracted from the l.d. muscle and were used to build regression models to predict marbling and color scores. Quality grade was predicted using another regression model incorporating all features. Grades predicted by the model were statistically equivalent to the grades assigned by expert graders.

Paper Details

Date Published: 29 December 2000
PDF: 11 pages
Proc. SPIE 4203, Biological Quality and Precision Agriculture II, (29 December 2000); doi: 10.1117/12.411743
Show Author Affiliations
S. Jeyamkondan, Oklahoma State Univ. (United States)
N. Ray, Oklahoma State Univ. (United States)
Glenn A. Kranzler, Oklahoma State Univ. (United States)
Nisha Biju, Oklahoma State Univ. (United States)


Published in SPIE Proceedings Vol. 4203:
Biological Quality and Precision Agriculture II
James A. DeShazer; George E. Meyer, Editor(s)

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