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
Image Matching Based On The Co-occurrence Matrix
Image Matching Based On The Co-occurrence Matrix
Adaptive Support-Weight Approach for Correspondence Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cross-based local stereo matching using orthogonal integral images
IEEE Transactions on Circuits and Systems for Video Technology
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Accurate estimation of disparity is one of the most active research area in computer vision. In the last few decades numerous algorithms have been invented to find disparity precisely. However, these inventions throws problem in selecting most appropriate one for the required application. A detailed analysis is mandatory to solve this kind of problem. The main objective of this paper is to empirically evaluate a set of well known correlation based stereo matching algorithms. A qualitative and quantitative analysis results will be useful for selecting the most appropriate algorithm for the given application. The presented analysis is mainly focused on the evaluation of errors, robustness to change in illumination and the computation cost required for each algorithm.