M. O'Neill and M. Denos. Automated system for coarse-tofine pyramidal area correlation stereo matching. Image and Vision Computing, vol. 14, no. 3, pp. 225--236, 1996. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Benchmarking of Bootstrap Temporal Stereo Using.. - Crossley, Thacker, Seed (1998) (1 citation) (Correct)
....leading to extra outliers and missing some previous correctly matched data. 3 Coarse to Fine Bootstrap Algorithm The notion of using image data at various scales to overcome ambiguity and increase the reliability of stereo, in particular for correlation stereo algorithms, is not new. 12] [15] and [6] all illustrate stereo algorithms which established gross correspondences first, British Machine Vision Conference 348 moving onto finer scales to refine the result. 14] and [21] both use a translating camera(s) to provide a series of narrow to wide stereo baseline images, from which a ....
....correspondences first, British Machine Vision Conference 348 moving onto finer scales to refine the result. 14] and [21] both use a translating camera(s) to provide a series of narrow to wide stereo baseline images, from which a CTF estimation of disparity for the scene was made. According to [15]; a CTF approach allows exhaustive searching with the ability to find an optimal matching point. This seemed like the ideal solution for providing the disparity information required during the start up period for a temporal stereo vision system. In order to achieve the initial exhaustive search ....
O'Neill M. and Denos M. Automated System for Coarse to Fine Pyramidal Area Correlation Stereo Matching. Image and Vision Computing, 1996, Vol. 14, pp. 225-236.
Multi-Resolution Rectangular Subregioning Stereo Matching Using.. - Sun (1998) (2 citations) (Correct)
....kinds of 3D surfaces, the disparity gradient should be within a certain limit. Matching techniques can be divided mainly into area based, feature based image matching, or a combination of them. Area based methods have been applied successfully to aerial images, where the surfaces varies smoothly [1]. They have the advantage of directly generating dense disparity map but they tend to breakdown where there is lack of texture or where depth discontinuities occur [2] The feature based approaches match more abstract features, rather than matching texture regions in the two images [3, 4, 5] ....
M. O'Neill and M. Denos, "Automated system for coarse-to-fine pyramidal area correlation stereo matching," Image and Vision Computing, vol. 14, no. 3, pp. 225-- 236, 1996.
Using multiple-hypothesis disparity maps and image velocity .. - Demirdjian, Darrell (Correct)
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M. O'Neill and M. Denos. Automated system for coarse-tofine pyramidal area correlation stereo matching. Image and Vision Computing, vol. 14, no. 3, pp. 225--236, 1996.
A Comparative Analysis Of Cross-Correlation Matching.. - Roma, Santos-Victor.. (Correct)
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M. O'Neill, M. Denos, \Automated System For Coarse-To-Fine Pyramidal Area Correlation Stereo Matching", Image and Vision Computing, vol.14, 1996, pp. 225-236.
Fast Block Matching with Normalized Cross-Correlation using.. - Nillius, Eklundh (Correct)
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O' eill, M., Denos, M., Automated system for coarse-to-fine pyramidal area correlation stereo matching, Image and Vision Computing, 14(1996), pp 225-236
A Comparative Analysis of Cross-Correlation - Matching Algorithms Using (Correct)
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M. O'Neill, M. Denos, "Automated System For Coarse-To-Fine Pyramidal Area Correlation Stereo Matching", Image and Vision Computing, vol.14, 1996, pp. 225236.
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