Abstract
This paper is focused on the object recognition problem in computer vision under partial occlusion. The approach followed to carry out this goal is the alignment method described exhaustively in the literature. In this approach the recognition process is divided in two stages: in a first stage, the transformation in space between the viewed object and the model object is determined. In a second stage the model that best matches the viewed object is found. Given four points in the image, it is necessary to find the four corresponding points in the model. This problem involving combinatorial search is resolved by means of a genetic algorithm. The occlusion problem has been dealt with special attention, so a new method has been proposed consisting of three processes: identification, grouping and verification. The recognition algorithm proposed here has been tested in several examples obtaining good results.
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References
D.W. Murray, “Strategies in object recognition”, Gec Journal of Research, Vol. 6, No. 2, pp. 80–95, 1988.
I. Biederman, “Recognition-by-Components: A Theory of Human Image Understanding”, Psychological Review, Vol. 94, No 2, pp. 115–147, 1987.
K. Siddiqi and B.B. Kimia, “Parts of Visual Form: Computational Aspects”, IEEE Trans. Pattern Anal. Machine Intell., Vol. 17, No. 3, pp. 239–251, 1995.
C. Orrite and A. Alcolea, “Identifying Perceptually Salient Segments on Planar Curves”, Pattern Recognition and Image Analysis, Preprints of the VII National Symposium on Pattern Recognition and Image Analysis, pp. 419.424, Published by Centre de Visió per Computador, Universitat Autònoma de Barcelona, Spain, 1997.
S. Ullman, “Aligning pictorial descriptions: An approach to object recognition”, Cognition, Vol. 32, pp. 193–254, 1989.
N. Ayache and O.D. Faugeras, “HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, No. 1, pp. 44–54, January 1986.
A. Zisserman, D.A. Forsyth, J.L. Mundy and C.A. Rothwell, “Recognizing General Curved Objects Efficiently”, in book Geometric Invariance in Computer Vision, pp. 228–251, The MIT Press, 1992.
D.P. Huttenlocher and S. Ullman, “Object Recognition using alignment”, MIT AI Memo 937.
D. E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, Addison-Wesley Publishing Company, Inc. 1989.
M. Singh, A. Chatterjee and S. Chaudhury, “Matching Structural Shape Descriptions using Genetic Algorithms”, Pattern Recognition, Vol. 30, No. 9, pp. 1451–1462, 1997.
M. Srinivas and L.M. Patnaik, “Genetic Algorithms: A survey”, Computer, pp. 17–26, June 1994.
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© 1998 Springer-Verlag Berlin Heidelberg
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Orrite, C., Alcolea, A., Campo, A. (1998). Recognition of Partially Occluded Flat Objects. In: Coelho, H. (eds) Progress in Artificial Intelligence — IBERAMIA 98. IBERAMIA 1998. Lecture Notes in Computer Science(), vol 1484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49795-1_21
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DOI: https://doi.org/10.1007/3-540-49795-1_21
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