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Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/3668

Title: On the application of a modified self-organizing neural network to estimate stereo disparity
Authors: Venkatesh, Y.V.
Raja, S. Kumar
Jaya Kumar, A.
Keywords: Correspondence problem
nonepipolar
occlusion
self-organizing map (SOM)
stereo disparity estimation
stereo-pair analysis
Issue Date: Oct-2007
Publisher: IEEE
Citation: IEEE Transactions on Image Processing, 2007, Vol.16, p2822
Abstract: We propose a modified self-organizing neural network to estimate the disparity map from a stereo pair of images. Novelty consists of the network architecture and of dispensing with the standard assumption of epipolar geometry. Quite distinct from the existing algorithms which, typically, involve area- and/or feature-matching, the network is first initialized to the right image, and then deformed until it is transformed into the left image, or vice versa, this deformation itself being the measure of disparity. Illustrative examples include two classes of stereo pairs: synthetic and natural (including random-dot stereograms and wire frames) and distorted. The latter has one of the following special characteristics: one image is blurred, one image is of a different size, there are salient features like discontinuous depth values at boundaries and surface wrinkles, and there exist occluded and half-occluded regions. While these examples serve, in general, to demonstrate that the technique performs better than many existing algorithms, the above-mentioned stereo pairs (in particular, the last two) bring out some of its limitations, thereby serving as possible motivation for further work.
Description: Open Access.
URI: http://hdl.handle.net/2289/3668
ISSN: 1057-7149
Alternative Location: http://dx.doi.org/10.1109/TIP.2007.906772
Copyright: 2007 IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Raman Research Institute's's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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