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During the image placement onto the compositing surface (mosaic), stitching algorithms try to minimize visual inconsistencies (texture discontinuities), seam induced color gradients, and blurry image regions. These problems are classically processed separately. In this contribution, we describe a two step graph-cut algorithm that combines these issues. In the first step, optimal seam locations are...
In many real-world applications, different mis-classification errors will cause different costs. However, cost-sensitive learning only applied in classification phase and not in the feature selection phase to address this problem. In this paper, we study cost-sensitive feature selection and propose a framework which incorporates a cost matrix into traditional feature selection methods. And we developed...
Recently, l1 graph based analysis using sparse representation has received much attention in pattern recognition and related communities. In this paper, motivated by the success of l1 graph in dimensionality reduction, we extend it to feature selection and propose a novel filter-type method called Sparsity Score (SS) which ranks features according to their respective sparsity preserving capability...
This paper provides a generic framework of component analysis (CA) methods introducing a new expression for scatter matrices and Gram matrices, called Generalized Pairwise Expression (GPE). This expression is quite compact but highly powerful: The framework includes not only (1) the standard CA methods but also (2) several regularization techniques, (3) weighted extensions, (4) some clustering methods,...
This paper presents a new approach to image-thresholding-based segmentation. It considerably improves existing methods by efficiently modeling non-Gaussian and multi-modal class-conditional distributions. The proposed approach seamlessly: 1) extends the Otsu's method to arbitrary numbers of thresholds and 2) extends the Kittler and Illingworth minimum error thresholding to non-Gaussian and multi-modal...
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