Abstract
The possibility of duplicating in a machine the ability of animals and man to interpret visual information has intrigued many investigators. While this problem has been treated with considerable success relative to automatic classification of two-dimensional objects, especially with regard to recognition of printed characters, comparatively little has been achieved in automatic identification of three-dimensional objects. Generally speaking, such successes as have been attained in the latter area can be grouped under two broad headings: 1) scene analysis in which various specialized procedures are used to determine spatial relationships between simple objects such as solid polyhedra, cylinders, cones, etc., and 2) statistical pattern recognition in which certain numerical features of an image of an unknown object are used to classify it and perhaps to estimate its position and orientation [1]. This paper is addressed entirely to the second approach and is concerned with recognition of complex man- made objects rather than natural objects or simple solids.
Research on this problem is supported at Ohio State University, by the United States Air Force Office of Scientific Research under Grant AF-AFOSR-71–2048.
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© 1974 Plenum Press, New York
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McGhee, R.B. (1974). Automatic Recognition of Complex Three-Dimensional Objects from Optical Images. In: Fu, K.S., Tou, J.T. (eds) Learning Systems and Intelligent Robots. Springer, Boston, MA. https://doi.org/10.1007/978-1-4684-2106-4_17
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DOI: https://doi.org/10.1007/978-1-4684-2106-4_17
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