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|Title: ||On the application of a modified self-organizing neural network to estimate stereo disparity|
|Authors: ||Venkatesh, Y.V.|
Raja Kumar, S.
Jaya Kumar, A.
|Keywords: ||Correspondence problem|
self-organizing map (SOM)
stereo disparity estimation
|Issue Date: ||Oct-2007 |
|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.|
|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 email@example.com. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.|
|Appears in Collections:||Research Papers (SCM)|
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