Globally Optimal Regions and Boundaries as Minimum Ratio Weight Cycles
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Application of the Tensor Voting Technique for Perceptual Grouping to Grey-Level Images
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Segmentation of Multiple Salient Closed Contours from Real Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Perceptually Closed Paths in Sketches and Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Query definition using interactive saliency
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Robust and efficient object segmentation using pseudo-elastica
Pattern Recognition Letters
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Using a saliency measure based on the global property of contour closure, we have developed a method that reliably segments out salient contours bounding unknown objects from real edge images. The measure also incorporates the Gestalt principles of proximity and smooth continuity that previous methods have exploited. Unlike previous measures, we incorporate contour closure by finding the eigen-solution associated with a stochastic process that models the distribution of contours passing through edges in the scene. The segmentation algorithm utilizes the saliency measure to identify multiple closed contours by finding strongly- connected components on an induced graph. The determination of strongly connected components is a direct consequence of the property of closure. We report for the first time, results on large real images for which segmentation takes an average of about 10 secs per object on a general-purpose workstation. The segmentation is made efficient for such large images by exploiting the inherent symmetry in the task.