An iterative multi-scale tensor voting scheme for perceptual grouping of natural shapes in cluttered backgrounds
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摘要
Grouping processes, which “organize” a given data by eliminating the irrelevant items and sorting the rest into groups, each corresponding to a particular object, can provide reliable pre-processed information to higher level computer vision functions, such as object detection and recognition. In this paper, we consider the problem of grouping oriented segments in highly cluttered images. In this context, we have developed a general and powerful method based on an iterative, multiscale tensor voting approach. Segments are represented as second-order tensors and communicate with each other through a voting scheme that incorporates the Gestalt principles of visual perception. The key idea of our approach is removing background segments conservatively on an iterative fashion, using multi-scale analysis, and re-voting on the retained segments. We have performed extensive experiments to evaluate the strengths and weaknesses of our approach using both synthetic and real images from publicly available datasets including the Williams and Thornber’s fruit-texture dataset [L. Williams, Fruit and texture images. Available from:
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论文评审过程:Received 1 July 2007, Accepted 29 July 2008, Available online 26 August 2008.
论文官网地址:https://doi.org/10.1016/j.cviu.2008.07.011