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Optical Engineering

Application of balanced neural tree for classifying tentative matches in stereo vision
Author(s): Sanjeev Kumar; Asha Rani; Christian Micheloni; Gian L. Foresti
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Paper Abstract

Here, we present a new application of the supervised learning based classifier in stereo matching. In particular, the chosen classifier is a balanced neural tree. The tentative matches are obtained using the speeded-up robust feature (SURF) matching. The feature vector corresponding to each tentative match is formed based on a similarity measure between SURF descriptors and their neighborhoods in the two stereo images, and these feature vectors are classified into inlier or outlier classes. Further, accuracy of the obtained results have been evaluated in terms of stereovision applications such as in the estimation of the homography and rectification matrices between two stereo images. The experiments based on these stereo estimates show the applicability of the proposed application in the stereo vision.

Paper Details

Date Published: 6 August 2012
PDF: 9 pages
Opt. Eng. 51(8) 087202 doi: 10.1117/1.OE.51.8.087202
Published in: Optical Engineering Volume 51, Issue 8
Show Author Affiliations
Sanjeev Kumar, Indian Institute of Technology Roorkee (India)
Asha Rani, Indian Institute of Technology Roorkee (India)
Christian Micheloni, Univ. degli Studi di Udine (Italy)
Gian L. Foresti, Univ. degli Studi di Udine (Italy)

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