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Proceedings Paper

Apple lesion recognition based on Fisherapples
Author(s): Yu Meng; Cheng Cai; Huan Hao; Xiang Qin; Wei Song; Lin Huang
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Paper Abstract

A derivative of Fisher's Linear Discriminant Analysis (FLDA), named Fisherapples for the recognition of apple lesions which is not sensitive to large variations in illumination is proposed in this paper. We make use of the linear projection that is orthogonal to the within-class scatter of the apple images from a high-dimensional image space to a considerably low-dimensional image space. It separates the data-cases well, projecting away variations in lighting. Our approach maximizes the ratio of between-class scatter to that of within-class scatter of apple lesions, i.e., we can get maximal between-class distances and minimal within-class distances after projection. This implies that the gap between the classes becomes bigger and ensures optimal separability in the new space. Besides, we take advantage of Principal Component Analysis (PCA) to project the set of apple images to a lower dimensional space in order to overcome the complication of the singular within-class scatter matrix. After that, the resulting within-class scatter becomes nonsingular and subsequently we can use standard FLDA to reduce the dimension further. Consequently, it is effortless for the computer to calculate the result. Experimental results demonstrate that Fisherapples performs better in apple lesion recognition than PCA.

Paper Details

Date Published: 10 July 2009
PDF: 8 pages
Proc. SPIE 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, 74890N (10 July 2009); doi: 10.1117/12.836883
Show Author Affiliations
Yu Meng, Northwest A&F Univ. (China)
Cheng Cai, Northwest A&F Univ. (China)
Huan Hao, Northwest A&F Univ. (China)
Xiang Qin, Northwest A&F Univ. (China)
Wei Song, Northwest A&F Univ. (China)
Lin Huang, Northwest A&F Univ. (China)

Published in SPIE Proceedings Vol. 7489:
PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering
Honghua Tan; Qi Luo, Editor(s)

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