Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Local Transforms for Computing Visual Correspondence
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Efficient Stereo with Multiple Windowing
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Maximum disparity threshold estimation for stereo imaging systems via variogram analysis
ICCS'03 Proceedings of the 1st international conference on Computational science: PartI
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
Comparison of nonparametric transformations and bit vector matching for stereo correlation
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
Fast block matching algorithm based on the winner-update strategy
IEEE Transactions on Image Processing
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The paper presents machine efficient area-based image matching method that is based on a concept of matching-regions that are adaptively adjusted to image contents. They are in a form of square windows which grow to convey enough information for reliable matching. This process is controlled by local image contents. The images, however, are transformed into nonparametric representation. Such a liaison of information-theoretic models with nonparametric statistics allows for compensation for noise and illumination differences in the compared images, as well as for better discrimination of compared regions. This leads to more reliable matching in effect. Machine efficient implementation is also discussed in the paper. Finally the experimental results and conclusions are presented.