A Variable Window Approach to Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Real-Time Correlation-Based Stereo Vision with Reduced Border Errors
International Journal of Computer Vision
A Simple Stereo Algorithm to Recover Precise Object Boundaries and Smooth Surfaces
International Journal of Computer Vision
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sharp and Dense Disparity Maps Using Multiple Windows
PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Efficient Stereo with Multiple Windowing
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Near Real-Time Reliable Stereo Matching Using Programmable Graphics Hardware
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Adaptive Support-Weight Approach for Correspondence Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast correlation-based stereo matching with the reduction of systematic errors
Pattern Recognition Letters
A fast stereo matching algorithm suitable for embedded real-time systems
Computer Vision and Image Understanding
Real-time spatiotemporal stereo matching using the dual-cross-bilateral grid
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Fast variable window for stereo correspondence using integral images
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 new area-based stereo matching algorithm that computes dense disparity maps for a real time vision system. While many stereo matching algorithms have been proposed in recent years, correlation-based algorithms still have an edge due to speed and less memory requirements. The selection of appropriate shape and size of the matching window is a difficult problem for correlation-based algorithms. We use two correlation windows (one large and one small size) to improve the performance of the algorithm while maintaining its real-time suitability. Unlike other area-based stereo matching algorithms, our method works very well at disparity boundaries as well as in low textured image areas and computes a sharp disparity map. Evaluation on the benchmark Middlebury stereo dataset has been done to demonstrate the qualitative and quantitative performance of our algorithm.