Combination of attributes in stereovision matching for fish-eye lenses in forest analysis
| dc.book.title | Advanced Concepts for Intelligent Vision Systems, Proceedings | |
| dc.contributor.author | Cruz García, Jesús Manuel de la | |
| dc.contributor.author | Herrera Caro, Pedro Javier | |
| dc.contributor.author | Pajares Martínsanz, Gonzalo | |
| dc.contributor.author | Guijarro Mata-García, María | |
| dc.contributor.author | Ruz Ortiz, José Jaime | |
| dc.date.accessioned | 2023-06-20T13:39:48Z | |
| dc.date.available | 2023-06-20T13:39:48Z | |
| dc.date.issued | 2009 | |
| dc.description | © Springer-Verlag Berlin Heidelberg 2009. The authors wish to acknowledge to the Council of Education of the Autonomous Community of Madrid and the Social European Fund for the research contract with the first author. Also to Dr. Fernando Montes, from the Escuela Técnica Superior de Ingenieros de Montes at the Politechnical University of Madrid and to Dra. Isabel Cañellas from the Forest Research Centre (CIFOR, INIA) for his support and the imaged material supplied. To the DPI2006-15661-C02-01 project, a part of the research has been granted by it. Finally, to the anonymous three referees for their valuable comments and suggestions. International Conference on Advanced Concepts for Intelligent Vision Systems (11th. Sep 28-Oct 02, 2009. Bordeaux, Francia) | |
| dc.description.abstract | This paper describes a novel stereovision matching approach by combining several attributes at the pixel level for omni-directional images obtained with fish-eye lenses in forest environments. The goal is to obtain a disparity map as a previous step for determining distances to the trees and then the volume of wood in the imaged area. The interest is focused on the trunks of the trees. Because of the irregular distribution of the trunks, the most suitable features are the pixels. A set of six attributes is used for establishing the matching between the pixels in both images of the stereo pair. The final decision about the matched pixels is taken by combining the attributes. Two combined strategies are proposed: the Sugeno Fuzzy Integral and the Dempster-Shafer theory. The combined strategies, applied to our specific stereo vision matching problem, make the main finding of the paper. In both, the combination is based on the application of three well known matching constraints. The proposed approaches are compared among them and favourably against the usage of simple features. | |
| 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 | CIFOR | |
| dc.description.sponsorship | INIA | |
| dc.description.status | pub | |
| dc.eprint.id | https://eprints.ucm.es/id/eprint/22596 | |
| dc.identifier.isbn | 978-3-642-04696-4 | |
| dc.identifier.officialurl | http://link.springer.com/content/pdf/10.1007%2F978-3-642-04697-1_26.pdf | |
| dc.identifier.relatedurl | http://link.springer.com/ | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14352/53277 | |
| dc.language.iso | eng | |
| dc.page.final | 287 | |
| dc.page.initial | 277 | |
| dc.publisher | Springer-Verlag Berlin | |
| dc.relation.projectID | DPI2006-15661-C02-01 | |
| dc.rights.accessRights | open access | |
| dc.subject.cdu | 004 | |
| dc.subject.keyword | Algorithms | |
| dc.subject.ucm | Informática (Informática) | |
| dc.subject.unesco | 1203.17 Informática | |
| dc.title | Combination of attributes in stereovision matching for fish-eye lenses in forest analysis | |
| dc.type | book part | |
| dc.volume.number | 5807 | |
| dcterms.references | 1. Barnard, S., Fishler, M.: Computational Stereo. ACM Computing Surveys 14, 553–572(1982) 2. Cochran, S.D., Medioni, G.: 3-D Surface Description from binocular stereo. IEEE Trans. Pattern Anal. Machine Intell. 14(10), 981–994 (1992) 3. Scharstein, D., Szeliski, R.: A taxonomy and avaluation of dense two-frame stereo correspondence algorithms. Int. J. Computer Vision 47(1-3), 7–42 (2002), http://vision.middlebury.edu/stereo/ 4. Tang, L., Wu, C., Chen, Z.: Image dense matching based on region growth with adaptive window. Pattern Recognit. Lett. 23, 1169–1178 (2002) 5. Lew, M.S., Huang, T.S., Wong, K.: Learning and feature selection in stereo matching. IEEE Trans. Pattern Anal. Machine Intell. 16, 869–881 (1994) 6. Abraham, S., Förstner, W.: Fish-eye-stero calibration and epipolar rectification. Photogrammetry and Remote Sensing 59, 278–288 (2005) 7. Schwalbe, E.: Geometric Modelling and Calibration of Fisheye Lens Camera Systems. In: Proc. 2nd Panoramic Photogrammetry Workshop, Int. Archives of Photogrammetry and Remote Sensing, vol. 36, Part 5/W8 (2005) 8. Barnea, D.I., Silverman, H.F.: A Class of Algorithms for Fast Digital Image Registration. IEEE Trans. Computers 21, 179–186 (1972) 9. Koschan, A., Abidi, M.: Digital Color Image Processing. Wiley, Chichester (2008) 10. Klaus, A., Sormann, M., Karner, K.: Segmented-Based Stereo Matching Using Belief Propagation and Self-Adapting Dissimilarity Measure. In: Proc. of 18th Int. Conference on Pattern Recognition, vol. 3, pp. 15–18 (2006) 11. Pajares, G., de la Cruz, J.M.: Visión por Computador: Imágenes digitales y aplicaciones, RA-MA (2007) 12. Kuncheva, L.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, Chichester (2004) 13. Dempster, A.P.: A generalization of Bayesian inference. Journal of the Royal Statistical Society B 30, 205–247 (1968) 14. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976) | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 878e090e-a59f-4f17-b5a2-7746bed14484 | |
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| relation.isAuthorOfPublication | 59baddaa-b4d2-4f26-81a9-745602eb2b25 | |
| relation.isAuthorOfPublication.latestForDiscovery | d5518066-7ea8-448c-8e86-42673e11a8ee |
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