Apple lesion recognition based on Fisherapples

Yu Meng; Cheng Cai; Huan Hao; Xiang Qin; Wei Song; Lin Huang

doi:10.1117/12.836883

10 July 2009 Apple lesion recognition based on Fisherapples

Yu Meng, Cheng Cai, Huan Hao, Xiang Qin, Wei Song, Lin Huang

Proceedings Volume 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering; 74890N (2009) https://doi.org/10.1117/12.836883
Event: International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2009), 2009, Zhangjiajie, China

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.

Citation Download Citation

Yu Meng, Cheng Cai, Huan Hao, Xiang Qin, Wei Song, and Lin Huang "Apple lesion recognition based on Fisherapples", Proc. SPIE 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, 74890N (10 July 2009); https://doi.org/10.1117/12.836883

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KEYWORDS

Principal component analysis

Detection and tracking algorithms

Databases

Antimony

Image classification

Light sources and illumination

Image processing

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