Walnut shell and meat classification using texture analysis and SVMs

Fenghua Jin; Lei Qin; Xiuqin Rao; Yang Tao

doi:10.1117/12.730907

12 October 2007 Walnut shell and meat classification using texture analysis and SVMs

Fenghua Jin, Lei Qin, Xiuqin Rao, Yang Tao

Proceedings Volume 6761, Optics for Natural Resources, Agriculture, and Foods II; 67610Q (2007) https://doi.org/10.1117/12.730907
Event: Optics East, 2007, Boston, MA, United States

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.

Citation Download Citation

Fenghua Jin, Lei Qin, Xiuqin Rao, and Yang Tao "Walnut shell and meat classification using texture analysis and SVMs", Proc. SPIE 6761, Optics for Natural Resources, Agriculture, and Foods II, 67610Q (12 October 2007); https://doi.org/10.1117/12.730907

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