Poultry skin tumor detection in hyperspectral reflectance images by combining classifiers

Authors:
Chengzhe Xu;Intaek Kim;Moon S. Kim
Affiliations:
Department of Communication Engineering, Myongji University, Kyonggido, South Korea;Department of Communication Engineering, Myongji University, Kyonggido, South Korea;USDA ARS, BA, ANRI, ISL, Beltsville, MD
Venue:
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Year:
2007

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Abstract

This paper presents a new method for detecting poultry skin tumors in hyperspectral reflectance images. We employ the principal component analysis (PCA), discrete wavelet transform (DWT), and kernel discriminant analysis (KDA) to extract the independent feature sets in hyperspectral reflectance image data. These features are individually classified by a linear classifier and their classification results are combined using product rule. The final classification result based on the proposed method shows the better performance in detecting tumors compared with previous works.