Publication: Choquet Fuzzy Integral Applied to Stereovision Matching for Fish-Eye Lenses in Forest Analysis
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2009
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Springer-Verlag Berlin
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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 on 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 the stereo pair. The final decision about the matched pixel is taken based on the Choquet Fuzzy Integral paradigm, which is a technique well tested for combining classifiers. The use and adjusting of this decision approach to our specific stereo vision matching problem makes the main finding of the paper. The procedure is based on the application of three well known matching constraints. The proposed approach is compared favourably against the usage of simple features and other fuzzy strategy that combines the simple ones.
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© 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 Dra. Isabel Cañellas and Fernando Montes from the Forest Research Centre (CIFOR, INIA) for his support and the material supplied.
International Workshop on Advanced Computational Intelligence (2º. oct 08-09, 2009. Mexico)
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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. 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-stero 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, vol. 36, Part 5/W8 (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. Kuncheva, L.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, Chichester (2004)
10. Yager, R.R.: On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans. System Man and Cybernetics 18(1) (1988)