Minimum Cost Aspect Classification: A module of a vision algorithm compiler
Abstract
The authors present the design of a vision algorithm compiler module for object localization that is used to construct efficient vision programs for a subtask of object localization called aspect classification. Intuitively, an aspect is a representative appearance, and can be associated with a range of viewing positions. In object localization, an object is first classified into an aspect in order to obtain a rough estimate of an object's configuration; this is followed by a numerical minimization procedure to locate the object precisely. The compiler module generates an optimal strategy for aspect classification, in the sense that the average cost of classification is minimal. The performance of the module is illustrated with several examples.
BibTeX
@conference{Hong-1990-13123,author = {Ki Sang Hong and Katsushi Ikeuchi and Keith D. Gremban},
title = {Minimum Cost Aspect Classification: A module of a vision algorithm compiler},
booktitle = {Proceedings of 10th International Conference on Pattern Recognition (ICPR '90)},
year = {1990},
month = {June},
volume = {1},
pages = {65 - 69},
}