Kosorl Thourn

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In this paper, the principal component analysis (PCA)for multi-view shape recognition is proposed. Our algorithm presents the signed enclosed area signature as the shape representation. In our method, the barycenter contour is used for decomposing the shape boundary into multiscale level. At each scale level, the signed enclosed area signatures are(More)
—To achieve a good performance for shape classification, it requires both shape representation and classifier. In this paper, the so-called Eigen Barycenter Contour (EBcC) and Fisher Barycenter Contour (FBcC) techniques are presented for 2D shape classification. The representation utilizes the area of triangles at different scale level of Barycenter Contour(More)
In this paper, the algorithm for 2D shape matching and retrieval is developed by using Fisher Barycenter Contour (FBcC). First, the shape is represented into 3D format using the signed enclosed area at each scale level of Barycenter Contour (BcC). Because of high dimension of the feature representation, the Eigen Barycenter Contour (EBcC) is applied for(More)
In this paper, we present a one dimensional descriptor for the two dimensional object silhouettes associated with each level of barycenter contour for multiple views shape matching and retrieval. Firstly, the barycenter contour is applied onto the shape contour. Then the averaging multi-triangle area representation (AMTAR) at each level of barycenter(More)
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