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Image Recognition: Visual Grouping, Recognition, and Learning

Joachim M. Buhmann, Jitendra Malik and Pietro Perona
Proceedings of the National Academy of Sciences of the United States of America
Vol. 96, No. 25 (Dec. 7, 1999), pp. 14203-14204
Stable URL: http://www.jstor.org/stable/121380
Page Count: 2
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Abstract

Vision extracts useful information from images. Reconstructing the three-dimensional structure of our environment and recognizing the objects that populate it are among the most important functions of our visual system. Computer vision researchers study the computational principles of vision and aim at designing algorithms that reproduce these functions. Vision is difficult: the same scene may give rise to very different images depending on illumination and viewpoint. Typically, an astronomical number of hypotheses exist that in principle have to be analyzed to infer a correct scene description. Moreover, image information might be extracted at different levels of spatial and logical resolution dependent on the image processing task. Knowledge of the world allows the visual system to limit the amount of ambiguity and to greatly simplify visual computations. We discuss how simple properties of the world are captured by the Gestalt rules of grouping, how the visual system may learn and organize models of objects for recognition, and how one may control the complexity of the description that the visual system computes.

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