Abstract
We address the problem of automatic model building for further recognition of objects. Our initial data are a set of images of an object. In a first stage, these images are put into correspondence using quasi-invariants, epipolar geometry and an approximation of the apparent motion by an homography. The different aspects of the objects may thus be computed and each aspect gives raise to a partial model of the object. In a second stage, these models and indexed in a data base which is used for recognition. This work is based on the idea that aspect graphs may (should?) be learned from examples rather than computed from CAD models, and that a planar representation associated with geometric quasi-invariants is a relevant tool for object recognition.
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References
E. Arbogast and R. Mohr. 3D structures inference from images sequences. International Journal of Pattern Recognition and Artificial Intelligence, 5(5):749, 1991.
P. Anandan. A computational framework and an algorithm for the measurement of visual motion. International journal of Computer Vision, 2:283–310, 1989.
P.J. Besl. Active optical range imaging sensors. Springer-Verlag, New York, USA, 1988.
B. Bhanu. Guest editor's introduction. Computer (Special Issue on CAD-Based Robot Vision), August 1987.
K. Bowyer. Why aspect graphs are not (yet) practical for computer vision. In Proceedings of the IEEE workshop on Direction on automated Cad-based Vision, Maui, Hawaii, USA, pages 97–104,1991.
J.H. Connell and M. Brady. Generating and generalizing models of visual objects. Artificial Intelligence, 31:159–183, 1987.
J.L. Crowley and P. Stelmazyk. Measurement and integration of 3D structures by tracking edges lines. In O. Faugeras, editor, Proceedings of the 1st European Conference on Computer Vision, Antibes, France, pages 269–280. Springer-Verlag, April 1990.
R. Deriche and O. Faugeras. Tracking line segments. In Proceedings of the 1st European Conference on Computer Vision, Antibes, France, pages 259–267. Springer-Verlag, April 1990.
P.J. Flynn and A.K. Jain. CAD-based computer vision: from CAD models to relational graphs. IEEE Transactions on PAMI, 13(2):114–132, February 1991.
P. Fua. A parallel stereo algorithm that produces dense depth maps and preserves image features. Machine Vision and Applications, 1990.
L. Hérault, R. Horaud, F. Veillon, and J.J. Niez. Symbolic image matching by simulated annealing. In Proceedings of the British Machine Vision Conference, Oxford, England, pages 319–324, September 1990.
R. Horaud, T. Skordas, and F. Veillon. Finding geometric and relationnal structures in an image. In Proceedings of the 1st European Conference on Computer Vision, Antibes, France, pages 374–384. Springer-Verlag, April 1990.
J. Koenderink and A. V. Doorn. The internal representation of solid shape with respect to vision. Biological Cybernetics, 32:211–216, 1979.
R. Mohr, F. Veillon, and L. Quan. Relative 3D reconstruction using multiple uncalibrated images. In Proceedings of the Conference on Computer Vision and Pattern Recognition, New York, USA, pages 543–548, June 1993.
D. Nitzan. Three-dimensional vision structure for robot applications. IEEE Transactions on PAMI, 10(3):291–309, 1988.
S. Petitjean, J. Ponce, and D.J. Kriegman. Computing exact aspect graphs of curved objects: algebraic surfaces. International Journal of Computer Vision, 9(3):231–255, 1992.
C.A. Rothwell, A. Zisserman, D.A. Forsyth, and J.L. Mundy. Fast recognition using algebraic invariants. In J.L. Mundy and A. Zisserman, editors, Geometric Invariance in Computer Vision, chapter 20, pages 398–407. MIT Press, 1992.
H. Sossa and R. Horaud. Model indexing: the graph-hashing approach. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Urbana-Champaign, Illinois, USA, June 1992.
I. Weiss. Noise-resistant projective and affine invariants. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Urbana-Champaign, Illinois, USA, pages 115–121, June 1992.
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© 1995 Springer-Verlag Berlin Heidelberg
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Gros, P. (1995). Using quasi-invariants for automatic model building and object recognition: An overview. In: Hebert, M., Ponce, J., Boult, T., Gross, A. (eds) Object Representation in Computer Vision. ORCV 1994. Lecture Notes in Computer Science, vol 994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60477-4_4
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DOI: https://doi.org/10.1007/3-540-60477-4_4
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