Stereo vision enabling precise border localization within a scanline optimization framework
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
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
A robust road profile estimation method for low texture stereo images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Outlier removal in stereo reconstruction of orbital images
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Review article: A 1D approach to correlation-based stereo matching
Image and Vision Computing
Accurate real-time neural disparity MAP estimation with FPGA
Pattern Recognition
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Hardware design considerations for edge-accelerated stereo correspondence algorithms
VLSI Design - Special issue on VLSI Circuits, Systems, and Architectures for Advanced Image and Video Compression Standards
Secrets of adaptive support weight techniques for local stereo matching
Computer Vision and Image Understanding
A hybrid genetic approach for stereo matching
Proceedings of the 15th annual conference on Genetic and evolutionary computation
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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We present a new window-based stereo matching algorithm which focuses on robust outlier rejection during aggregation. The main difficulty for window-based methods lies in determining the best window shape and size for each pixel. Working from the assumption that depth discontinuities occur at colour boundaries, we segment the reference image and consider all window pixels outside the image segment that contains the pixel under consideration as outliers and greatly reduce their weight in the aggregation process. We developed a variation on the recursive moving average implementation to keep processing times independent from window size. Together with a robust matching cost and the combination of the left and right disparity maps, this gives us a robust local algorithm that approximates the quality of global techniques without sacrificing the speed and simplicity of window-based aggregation.