Abstract
The use of invariants quickly gained impetus in the computer vision community. The paper recapitulates why this strand of research has become so influential by summarizing the traditional advantages often highlighted in the context of object recognition. Then, however, the paper moves on to corroborate the importance of certain geometrical entities, called “fixed structures”. It is argued that looking at these entities forms a core idea, that can be considered central to several applications. These include as diverse subjects as grouping and three-dimensional scene reconstruction. A careful and systematic study of fixed structures, the corresponding subgroups, and their invariants is advocated.
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References
M. Armstrong, A. Zisserman, and P. Beardsley, Euclidean structure from uncalibrated images, 5th BMVC, 1994
O. Faugeras, What can be seen in three dimensions from an uncalibrated stereo rig, Proc. 2nd ECCV, pp. 321–334, 1992
R. Hartley, Estimation of relative camera positions for uncalibrated cameras, proc. 2nd ECCV, pp. 579–587, 1992
T. Kanade, Recovery of the 3-dimensional shape of an object from a single view, Artificial Intelligence, Vol. 17, pp. 75–116, 1981
D. Lowe, Perceptual organisation and visual recognition, Kluwer Academic Publishers, 1985
M. Pollefeys, L. Van Gool, and M. Proesmans, Euclidean 3D reconstruction from image sequences with variable focal lengths, Proc. European Conf. Computer Vision, Vol.I, pp. 31–42, 1996
J. Ponce, On characterising ribbons and finding skewed symmetries, Proc. Int. Conf. Robotics and Automation, pp. 49–54, 1989
C. Rothwell, Recognition using projective invariance, PhD Thesis, Univ. Oxford, 1993
J. Semple and G. Kneebone, Algebraic projective geometry, Oxford Univ. Press, 1952
L. Van Gool, T. Moons, M. Proesmans, and M. Van Diest, Affine reconstruction from perspective image pairs obtained by a translating camera, Proc. Int. Conf. Pattern Recognition, Jerusalem, pp. A/290–A/294, oct. 1994
L. Van Gool, M. Proesmans, and T. Moons, Groups for grouping, SPIE Int. Symp. on Optical Science, Appl. of Digital Image Processing XVIII, Vol.2564, pp.402–413, 1995
L. Van Gool, T. Moons, E. Pauwels, and A. Oosterlinck, Vision and Lie's approach to invariance, Image and Vision Computing, Vol. 13, No. 4, pp. 259–277, may 1995
L. Van Gool, T. Moons, and M. Proesmans, Mirror and point symmetry under perspective skewing, to be published in prod. CVPR, 1996
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© 1996 Springer-Verlag Berlin Heidelberg
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Van Gool, L. (1996). Invariants and fixed structures lead the way to change. In: Perner, P., Wang, P., Rosenfeld, A. (eds) Advances in Structural and Syntactical Pattern Recognition. SSPR 1996. Lecture Notes in Computer Science, vol 1121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61577-6_23
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DOI: https://doi.org/10.1007/3-540-61577-6_23
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