Image Segmentation and Selective Smoothing Based on Variational Framework
Journal of Signal Processing Systems
Detection of unexpected multi-part objects from segmented contour maps
Pattern Recognition
Optimal contour closure by superpixel grouping
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Ridge linking using an adaptive oriented mask applied to plant root images with thin structures
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Edge Drawing: A combined real-time edge and segment detector
Journal of Visual Communication and Image Representation
Optimal Image and Video Closure by Superpixel Grouping
International Journal of Computer Vision
Perceptual grouping using superpixels
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
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This paper introduces a new edge-grouping method to detect perceptually salient structures in noisy images. Specifically, we define a new grouping cost function in a ratio form, where the numerator measures the boundary proximity of the resulting structure and the denominator measures the area of the resulting structure. This area term introduces a preference towards detecting larger-size structures and, therefore, makes the resulting edge grouping more robust to image noise. To find the optimal edge grouping with the minimum grouping cost, we develop a special graph model with two different kinds of edges and then reduce the grouping problem to finding a special kind of cycle in this graph with a minimum cost in ratio form. This optimal cycle-finding problem can be solved in polynomial time by a previously developed graph algorithm. We implement this edge-grouping method, test it on both synthetic data and real images, and compare its performance against several available edge-grouping and edge-linking methods. Furthermore, we discuss several extensions of the proposed method, including the incorporation of the well-known grouping cues of continuity and intensity homogeneity, introducing a factor to balance the contributions from the boundary and region information, and the prevention of detecting self-intersecting boundaries.