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Proceedings Paper

Walnut shell and meat classification using texture analysis and SVMs
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Paper Abstract

The classification of walnuts shell and meat has a potential application in industry walnuts processing. A dark-field illumination method is proposed for the inspection of walnuts. Experiments show that the dark-field illuminated images of walnut shell and meat have distinct text patterns due to the differences in the light transmittance property of each. A number of rotation invariant feature analysis methods are used to characterize and discriminate the unique texture patterns. These methods include local binary pattern operator, wavelet analysis, circular Gabor filters, circularly symmetric gray level co-occurrence matrix and the histogram-related features. A recursive feature elimination method (SVM-RFE), is used to remove uncorrelated and redundant features and to train the SVM classifier at the same time. Experiments show that, by using only the top six ranked features, an average classification accuracy of 99.2% can be achieved.

Paper Details

Date Published: 12 October 2007
PDF: 12 pages
Proc. SPIE 6761, Optics for Natural Resources, Agriculture, and Foods II, 67610Q (12 October 2007); doi: 10.1117/12.730907
Show Author Affiliations
Fenghua Jin, Univ. of Maryland, College Park (United States)
Lei Qin, Univ. of Maryland, College Park (United States)
Xiuqin Rao, Zhejiang Univ. (China)
Yang Tao, Univ. of Maryland, College Park (United States)


Published in SPIE Proceedings Vol. 6761:
Optics for Natural Resources, Agriculture, and Foods II
Yud-Ren Chen; George E. Meyer; Shu-I Tu, Editor(s)

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