Digital elevation model production by stereo-matching spot image-pairs: a comparison of algorithms
Image and Vision Computing
A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Finding the Largest Unambiguous Component of Stereo Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Prediction Error as a Quality Metric for Motion and Stereo
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Learning image structures for optimizing disparity estimation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
3D geometry from uncalibrated images
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Complex correlation statistic for dense stereoscopic matching
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
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The knowledge of stereo matching algorithm properties and behaviour under varying conditions is crucial for the selection of a proper method for the desired application. In this paper we study the behaviour of four representative matching algorithms under varying signal-to-noise ratio in six types of error statistics. The errors are focused on basic matching failure mechanisms and their definition observes the principles of independence, symmetry and completeness. A ground truth experiment shows that the best choice for view prediction is the Graph Cuts algorithm and for structure reconstruction it is the Confidently Stable Matching.