Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks
IEEE Transactions on Pattern Analysis and Machine Intelligence
A rule-based approach to binocular stereopsis
Analysis and interpretation of range images
Detecting the dislocations in metal crystals from microscopic images
Pattern Recognition
The nature of statistical learning theory
The nature of statistical learning theory
Intensity- and Gradient-Based Stereo Matching Using Hierarchical Gaussian Basis Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Computer Vision by Cooperative Focus and Stereo
Active Computer Vision by Cooperative Focus and Stereo
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Learning and Feature Selection in Stereo Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Learning Applied to Stereovision Matching
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Stereo matching using Hebbian learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The non-parametric Parzen's window in stereo vision matching
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy Cognitive Maps for stereovision matching
Pattern Recognition
Expert Systems with Applications: An International Journal
Supervised learning based stereo matching using neural tree
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
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This paper presents an approach to the local stereovision matching problem using edge segments as features with four attributes. In this paper we design a Support Vector Machine classifier for solving the stereovision matching problem. We obtain a matching decision function to classify a pair of features as a true or false match. The use of such classifier makes up the main finding of the paper. A comparative analysis among other existing approaches is included to show that this finding can be justified theoretically. From these investigations, we conclude that the performance of the proposed method is appropriate for this task.