Abstract
Approaches to visual object recognition can be divided into model-based and non model-based schemes. In this paper we establish some limitations on non model-based recognition schemes. We show that a consistent non model-based recognition scheme for general objects cannot discriminate between objects. The same result holds even if the recognition function is imperfect, and is allowed to mis-identify each object from a substantial fraction of the viewing directions. We then consider recognition schemes restricted to classes of objects. We define the notion of the discrimination power of a consistent recognition function for a class of objects. The function's discrimination power determines the set of objects that can be discriminated by the recognition function. We show how the properties of a class of objects determine an upper bound on the discrimination power of any consistent recognition function for that class.
This article was processed using the LATEX macro package with ECCV92 style.
Chapter PDF
Similar content being viewed by others
References
Bolles, R.C. and Cain, R.A. 1982. Recognizing and locating partially visible objects: The local-features-focus method. Int. J. Robotics Research, 1(3), 57–82.
Brooks, R.A. 1981. Symbolic reasoning around 3-D models and 2-D images, Artificial Intelligence J., 17, 285–348.
Burns, J. B., Weiss, R. and Riseman, E.M. 1990. View variation of point set and line segment features. Proc. Image Understanding Workshop, Sep., 650–659.
Cannon, S.R., Jones, G.W., Campbell, R. and Morgan, N.W. 1986. A computer vision system for identification of individuals. Proc. IECON 86 0, WI., 1, 347–351.
Clemens, D.J. and Jacobs, D.W. 1990. Model-group indexing for recognition. Proc. Image Understanding Workshop, Sep., 604–613.
Forsyth, D., Mundy, L., Zisserman, A., Coelho, C., Heller A. and Rothwell, C. 1991. Invariant Descriptors for 3-D object Recognition and pose. IEEE Trans. on PAMI. 13(10), 971–991.
Grimson, W.E.L. and Lozano-Pérez, T. 1984. Model-based recognition and localization from sparse data. Int. J. Robotics Research, 3(3), 3–35.
Grimson, W.E.L. and Lozano-Pérez, T. 1987. Localizing overlapping parts by searching the interpretation tree. IEEE Trans. on PAMI. 9(4), 469–482.
Horn B. K.P. 1977. Understanding image intensities, Artificial Intelligence J., 8(2), 201–231
Huttenlocher, D.P. and Ullman, S. 1987. Object recognition using alignment. Proceeding of ICCV Conf., London, 102–111.
Kanade, T. 1977. Computer recognition of human faces. Birkhauser Verlag. Basel and Stuttgart.
Lowe, D.G. 1985. Three dimensional object recognition from single two-dimensional images. Robotics research Technical Report 202, Couraant Inst. of Math. Sciences, N. Y. University.
Moses, Y, and Ullman S. 1991. Limitations of non model-based recognition schemes. AI MEMO No 1301, The Artificial Intelligence Lab., M.I.T.
Phong, B.T. 1975. Illumination for computer generated pictures. Communication of the ACM, 18(6), 311–317.
Poggio T., and Edelman S. 1990. A network that learns to recognize three dimensional objects. Nature, 343, 263–266.
Ullman S. 1977. Transformability and object identity. Perception and Psychophysics, 22(4), 414–415.
Ullman S. 1989. Alignment pictorial description: an approach to object recognition. Cognition, 32(3), 193–254.
Wong, K.H., Law, H.H.M. and Tsang P.W.M, 1989. A system for recognizing human faces, Proc. ICASSP, 1638–1642.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1992 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moses, Y., Ullman, S. (1992). Limitations of non model-based recognition schemes. In: Sandini, G. (eds) Computer Vision — ECCV'92. ECCV 1992. Lecture Notes in Computer Science, vol 588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55426-2_94
Download citation
DOI: https://doi.org/10.1007/3-540-55426-2_94
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-55426-4
Online ISBN: 978-3-540-47069-4
eBook Packages: Springer Book Archive