DocumentCode :
2585710
Title :
Learning 3D object recognition strategies
Author :
Draper, Bruce A. ; Riseman, Edward M.
Author_Institution :
Dept. of Comput. & Inf. Sci., Massachusetts Univ., Amherst, MA, USA
fYear :
1990
fDate :
4-7 Dec 1990
Firstpage :
320
Lastpage :
324
Abstract :
The problem of automatically learning knowledge-directed control strategies is considered. In particular, the authors address the problem of learning object-specific recognition strategies from object descriptions and sets of interpreted training images. A separate recognition strategy is developed for every object in the domain. The goal of each recognition strategy is to identify any and all instances of the object in an image, and give the 3-D position (relative to the camera) of each instance. The goal of the learning process is to build a strategy that minimizes the expected cost of recognition, subject to accuracy constraints imposed by the user
Keywords :
computer vision; computerised pattern recognition; computerised picture processing; knowledge based systems; learning systems; 3-D position; 3D object recognition strategies; accuracy constraints; automatic learning; camera; interpreted training images; knowledge-directed control strategies; object descriptions; Automatic control; Cameras; Computer vision; Contracts; Costs; Decision trees; Image recognition; Information science; Object recognition; Shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision, 1990. Proceedings, Third International Conference on
Conference_Location :
Osaka
Print_ISBN :
0-8186-2057-9
Type :
conf
DOI :
10.1109/ICCV.1990.139541
Filename :
139541
Link To Document :
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