Edge detection in the feature space

Authors:
Zhengchun Lin;Jinshan Jiang;Zhiyan Wang
Affiliations:
Department of Research and Development, Foshan Institute of Standards Technology, Foshan, Guangdong, 528000, People's Republic of China;School of Sciences, South China University of Technology, Guangzhou, Guangdong, 510640, People's Republic of China;School of Computer and Engineering, South China University of Technology, Guangzhou, Guangdong, 510006, People's Republic of China
Venue:
Image and Vision Computing
Year:
2011

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Abstract

To build a consistent image representation model which can process the non-Gaussian distribution data, a novel edge detection method (KPCA-SCF) based on the kernel method is proposed. KPCA-SCF combines kernel principal component analysis and kernel subspace classification proposed in this paper to extract edge features. KPCA-SCF was tested and compared with linear PCA, nonlinear PCA and conventional methods such as Sobel, LOG, Canny, etc. Experiments on synthetic and real-world images show that KPCA-SCF is more robust under noisy conditions. KPCA-SCF's score of F-measure (0.44) ranks 11th in the Berkeley segmentation dataset and benchmark, it (0.54) ranks 10th tested on a noised image.