Titlebook: Computer Vision - ACCV 2007; 8th Asian Conference Yasushi Yagi,Sing Bing Kang,Hongbin Zha Conference proceedings 2007 Springer-Verlag Berli

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Sports Classification Using Cross-Ratio Histogramse proposed approach uses invariant nature of a cross-ratio under projective transformation to develop a robust classifier. For a given image, cross-ratios are computed for the points obtained from the intersection of lines detected using Hough transform. These cross-ratios are represented by a histo

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Efficient Graph Cuts for Multiclass Interactive Image Segmentation segmentation is foreground/background segmentation based on user specified brush labellings. The problem can be formulated within the binary Markov Random Field (MRF) framework which can be solved efficiently via graph cut [1]. However, no attempt has yet been made to handle segmentation of multipl

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Feature Subset Selection for Multi-class SVM Based Image Classificationselection criterion for the multi-class SVMs. By minimizing this criterion, the scale factors assigned to each feature in a kernel function are optimized to identify the important features. This minimization problem can be efficiently solved by gradient-based search techniques, even if hundreds of f

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Evaluating Multi-class Multiple-Instance Learning for Image Categorization. Typical current MIL schemes rely on binary one-versus-all classification, even for inherently multi-class problems. There are a few drawbacks with binary MIL when applied to a multi-class classification problem. This paper describes Multi-class Multiple-Instance Learning (McMIL) to image categoriz

MAIZE

TransforMesh : A Topology-Adaptive Mesh-Based Approach to Surface Evolutionmetrization, while allowing for an accurate surface representation, suffers from the inherent problems of not being able to reliably deal with self-intersections and topology changes. As a consequence, an important number of methods choose implicit representations of surfaces, e.g. level set methods

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