A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Detection by Constrained Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust and Efficient Detection of Salient Convex Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting Salient Curves from Images: An Analysis of the Saliency Network
International Journal of Computer Vision
Contour grouping with prior models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Contour grouping is an important issue in computer vision. However, traditional ways tackling the problem usually fail to provide as satisfying results as human vision can do. One important feature of human vision mechanism is that human vision tends to group together edges that are not only geometrically and topologically related, but also similar in their appearances – the appearances of image patches around them including their brightness, color, texture cues, etc. But in traditional grouping approaches, after edges or lines have been detected, the appearances of image patches around them are seldom considered again, leading to the results that edges belonging to boundaries of different objects are sometimes falsely grouped together. In this paper, we introduce an appearance feature to describe the appearance of an image patch around a line segment, and incorporate this appearance feature into a saliency measure to evaluate contours on an image. The most salient contour is found by optimizing this saliency measure using a genetic algorithm. Experimental results prove the effectiveness of our approach.