Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
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
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Stereo with Multiple Windowing
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Robust Real-Time Face Detection
International Journal of Computer Vision
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Locally Adaptive Support-Weight Approach for Visual Correspondence Search
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Local Stereo Matching with Segmentation-based Outlier Rejection
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach
International Journal of Computer Vision
Evaluation of Stereo Matching Costs on Images with Radiometric Differences
IEEE Transactions on Pattern Analysis and Machine Intelligence
IMVIP '09 Proceedings of the 2009 13th International Machine Vision and Image Processing Conference
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
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
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
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Fast cost-volume filtering for visual correspondence and beyond
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Fast Cost-Volume Filtering for Visual Correspondence and Beyond
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo matching by using the global edge constraint
Neurocomputing
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In recent years, local stereo matching algorithms have again become very popular in the stereo community. This is mainly due to the introduction of adaptive support weight algorithms that can for the first time produce results that are on par with global stereo methods. The crux in these adaptive support weight methods is to assign an individual weight to each pixel within the support window. Adaptive support weight algorithms differ mainly in the manner in which this weight computation is carried out. In this paper we present an extensive evaluation study. We evaluate the performance of various methods for computing adaptive support weights including the original bilateral filter-based weights, as well as more recent approaches based on geodesic distances or on the guided filter. To obtain reliable findings, we test these different weight functions on a large set of 35 ground truth disparity pairs. We have implemented all approaches on the GPU, which allows for a fair comparison of run time on modern hardware platforms. Apart from the standard local matching using fronto-parallel windows, we also embed the competing weight functions into the recent PatchMatch Stereo approach, which uses slanted sub-pixel windows and represents a state-of-the-art local algorithm. In the final part of the paper, we aim at shedding light on general points of adaptive support weight matching, which, for example, includes a discussion about symmetric versus asymmetric support weight approaches.