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Detection of outliers and relevant features are the most important process before classification. In this paper, a novel semi-supervised k-means clustering is proposed for outlier detection in mammogram classification. Initially the shape features are extracted from the digital mammograms, and k-means clustering is applied to cluster the features, the number of clusters is equal with the number of...
This paper presents an innovative approach for localizing and segmenting duplicate objects for industrial applications. The working conditions are challenging, with complex heavily-occluded objects, arranged at random in the scene. To account for high flexibility and processing speed, this approach exploits SIFT keypoint extraction and mean shift clustering to efficiently partition the correspondences...
This paper presents a template-based facial caricature generation approach. Since the major difficulty in generating facial caricature automatically is the uniqueness of individuals and the polymorphism of features, a modified active shape model (ASM) is first designed to locate key feature points accurately by using 2D local grey-level structures. Then, extracted facial components are classified...
To improve the correctness and real-time performance in the process of image matching, this paper proposed a fast matching algorithm based on image K-gray-degree clustering. Given a template with irregular shape, this algorithm divides the image into certain size blocks called R_block, and calculates their mean gray value, then clusters original templates to K-degree templates according to gray distribution...
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