Publication: Fuzzy multi-criteria decision making in stereovision matching for fish-eye lenses in forest analysis
Loading...
Full text at PDC
Publication Date
2009
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Springer-Verlag Berlin
Citations
Exportar
Abstract
This paper describes a novel stereovision matching approach based on 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 the volume of wood in the imaged area. The interest is focused oil the trunks of the trees. Due to 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 each stereo pair analysed. The final decision about the matched pixels is taken based on a well tested FUZZY Multi-Criteria Decision Making approach, where the attributes determine the criteria and the potential matches in one image of the stereo pair for a given pixel in the other one determine the alternatives. The application of this decision making approach makes, the main finding of the paper. The full procedure is based on the application of three well known matching constraints. The proposed approach is compared favourably against the usage of simple features.
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 contract with the first author. Also to the Dra. I. Cañellas and F. Montes from the Forest Research Centre for his support and the material supplied. To the DPI2006-15661-C02-01 project.
International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2009) (10th. sep 23-26, 2009. Burgos, España)
UCM subjects
Unesco subjects
Keywords
Citation
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 Analysis and Machine Intelligence 14(10), 981–994 (1992)
3. Tang, L., Wu, C., Chen, Z.: Image dense matching based on region growth with adaptive window. Pattern Recognit. Letters 23, 1169–1178 (2002)
4. 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)
5. Abraham, S., Förstner, W.: Fish-eye-stereo calibration and epipolar rectification. Photogrammetry and Remote Sensing 59, 278–288 (2005)
6. Schwalbe, E.: Geometric modelling and calibration of fisheye lens camera systems. In: Proc. 2nd Panoramic Photogrammetry Workshop, Int. Archives of Photogrammetry and Remote Sensing, Part 5/W8, vol. 36 (2005)
7. Barnea, D.I., Silverman, H.F.: A class of algorithms for fast digital image registration. IEEE Trans. Computers 21, 179–186 (1972)
8. Pajares, G., de la Cruz, J.M.: Visión por Computador: Imágenes digitales y aplicaciones. RA-MA (2008)
9. Chen, C.T.: Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems 114, 1–9 (2000)
10. Wang, W., Fenton, N.: Risk and confidence analysis for fuzzy multi criteria decision making. Knowledge Based Systems 19, 430–437 (2006)