Stereovision matching through support vector machines
| dc.contributor.author | Pajares Martínsanz, Gonzalo | |
| dc.contributor.author | Cruz García, Jesús Manuel de la | |
| dc.date.accessioned | 2023-06-20T10:43:39Z | |
| dc.date.available | 2023-06-20T10:43:39Z | |
| dc.date.issued | 2003-11 | |
| dc.description | Part of the work has been performed under project CICYT TAP94-0832-C02-01. | |
| dc.description.abstract | 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. | |
| dc.description.department | Sección Deptal. de Arquitectura de Computadores y Automática (Físicas) | |
| dc.description.faculty | Fac. de Ciencias Físicas | |
| dc.description.refereed | TRUE | |
| dc.description.sponsorship | CICYT | |
| dc.description.status | pub | |
| dc.eprint.id | https://eprints.ucm.es/id/eprint/25769 | |
| dc.identifier.doi | 10.1016/S0167-8655(03)00102-8 | |
| dc.identifier.issn | 0167-8655 | |
| dc.identifier.officialurl | http://dx.doi.org/10.1016/S0167-8655(03)00102-8 | |
| dc.identifier.relatedurl | http://www.sciencedirect.com/science/ | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14352/51088 | |
| dc.issue.number | 15 | |
| dc.journal.title | Pattern Recognition Letters | |
| dc.language.iso | eng | |
| dc.page.final | 2583 | |
| dc.page.initial | 2575 | |
| dc.publisher | Elsevier Science BV | |
| dc.relation.projectID | TAP94-0832-C02-01 | |
| dc.rights.accessRights | open access | |
| dc.subject.cdu | 004 | |
| dc.subject.keyword | Algorithm | |
| dc.subject.keyword | Vision | |
| dc.subject.ucm | Informática (Informática) | |
| dc.subject.unesco | 1203.17 Informática | |
| dc.title | Stereovision matching through support vector machines | |
| dc.type | journal article | |
| dc.volume.number | 24 | |
| dcterms.references | Baillard, C., Dissard, O., 2000. A stereo matching algorithm for urban digital elevation models. Photogrammet. Eng. Remote Sensing 66 (9), 1119–1128. Cherkassky, V., Mulier, F., 1998. Learning from Data: Concepts, Theory and Methods. Wiley, New York. Huertas, A., Medioni, G., 1986. Detection of intensity changes with subpixel accuracy using Laplacian–Gaussian masks. IEEE Trans. Pattern Anal. Machine Intell. 8 (5), 651-664. Kim, Y.S., Lee, J.J., Ha, Y.H., 1997. Stereo matching algorithm based on modified wavelet decomposition process. Pattern Recognition 30(6), 929–952. Krotkov, E.P., 1989. Active Computer Vision by Cooperative Focus and Stereo. Springer-Verlag, Berlin. Lee, S.H., Leou, J.J., 1994. A dynamic programming approach to line segment matching in stereo vision. Pattern Recognition 27, 961–986. Leu, J.G., Yau, H.L., 1991. Detecting the dislocations in metal crystals from microscopic images. Pattern Recognition 24(1), 41–56. Lew, M.S., Huang, T.S., Wong, K., 1994. Learning and feature selection in stereo matching. IEEE Trans. Pattern Anal. Machine Intell. 16 (9), 869–881. Marr, D., Poggio, T., 1979. A computational theory of human stereovision. Proc. Roy. Soc. London B 207, 301–328. Medioni, G., Nevatia, R., 1985. Segment based stereo matching. Comput. Vision Graphics Image Process. 31, 2–18. Nevatia, R., Babu, K.R., 1980. Linear feature extraction and description. Comput. Vision Graphics Image Process. 13, 26–257. Pajares, G., Cruz, J.M., 2002. The non-parametric Parzen's window in stereovision matching. IEEE Trans. Systems Man Cybernet. 32 (2), 225–230. Pajares, G., Cruz, J.M., López, J.A., 1998a. Pattern recognition learning applied to stereovision matching. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (Eds.), Advances in Pattern Recognition. Springer-Verlag, Berlin, pp. 997–1004. Pajares, G., Cruz, J.M., Aranda, J., 1998b. Relaxation by Hopfield network in stereo image matching. Pattern Recognition 31 (5), 561–574. Pajares, G., Cruz, J.M., López-Orozco, J.A., 1998c.Improving stereovision matching through supervised learning. Pattern Anal. Applicat. 1, 105–120. Pajares, G., Cruz, J.M., López-Orozco, J.A., 1999. Stereo matching using Hebbian learning. IEEE Trans. Systems Man Cybernet. 29 (4), 553–559. Platt, J., 2000. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers. MIT Press,Cambridge, MA. Pollard, S.B., Mayhew, J.E.W., Frisby, J.P., 1981. PMF: a stereo correspondence algorithm using a disparity gradient limit. Perception 14, 449–470. Tanaka, S., Kak, A.C., 1990. A rule-based approach to binocular stereopsis. In: Jain, R.C., Jain, A.K. (Eds.), Analysis and Interpretation of Range Images. Springer-Verlag, Berlin. Trucco, E., Verri, A., 1998. Introductory Techniques for 3-D Computer Vision. Prentice-Hall, Upper Saddle River. Vapnik, V.N., 2000. The Nature of Statistical Learning Theory. Springer-Verlag, New York. Wei, G.Q., Brauer, W., Hirzinger, G., 1998. Intensity- and gradient-based stereo matching using hierarchical Gaussian basis functions. IEEE Trans. Patt. Anal. Machine Intell. 20 (11), 1143–1160. Zhang, Z., Blum, R.S., 1999. A categorization of multiscaledecomposition-based image fusion schemes with a performance study for a digital camera application. Proc. IEEE 87 (8), 1315–1325. | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 878e090e-a59f-4f17-b5a2-7746bed14484 | |
| relation.isAuthorOfPublication.latestForDiscovery | 878e090e-a59f-4f17-b5a2-7746bed14484 |
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