Edge detection in the feature space
Image and Vision Computing
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Kernel Principal Component Analysis (KPCA) has gained much attention for capturing nonlinear image features which is particulary important for clustering high-dimensional multi-class features. We introduce KPCA in this paper for detecting and classifying moving vehicles from its viewpoint images. The KPCA extracts non-linear features of multi-class moving vehicles by mapping input space to a higher dimensional feature space through a non-linear map. The results provide us a clustered feature space of the car and non-car for classifying them by separating the dimensional space or eigenvector. The experimental results show the robustness of the KPCA's feature separation in our car database that lead to the cars' classification. Extended experiments on various vehicles detection have also shown the remarkable performance of the proposed method.