کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
527137 | 869293 | 2011 | 13 صفحه PDF | دانلود رایگان |
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.
Research Highlights
► Proposed an edge detection method in the feature space.
► The method is insensitive to two kinds of noise.
► The method ranks 11th in the Berkeley segmentation dataset and benchmark.
Journal: Image and Vision Computing - Volume 29, Issues 2–3, February 2011, Pages 142–154