Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
An Introduction to 3D Computer Vision Techniques and Algorithms
An Introduction to 3D Computer Vision Techniques and Algorithms
Segmentation-based adaptive support for accurate stereo correspondence
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Local stereo matching using geodesic support weights
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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In this paper, we present a novel method for improving the speed and accuracy of the initial disparity estimation of the stereo matching algorithms. These algorithms are widely investigated, but fast and precise estimation of a disparity map still remains a challenging problem. Recent top ranking stereo matching algorithms usually utilize a window-based approach and mean shift based clustering. We propose an algorithm inspired by a top-down approach exploiting these two steps. By using the mean shift algorithm, we transform the input images into the attractor space and then perform the matching on the attractor sets. In contrast to the state-of-the-art algorithms, where matching is done on the basis of pixel intensities, grouped according to the results of mean shift algorithm, we perform the matching between the attractor sets of both input images. In this way we are able to acquire fast disparity estimates for whole segments.