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Proceedings Paper

Neural network approach to object recognition and image partitioning within a resolution hierarchy
Author(s): Joachim Utans; Gene R. Gindi
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Paper Abstract

Object recognition is a complex task involving simultaneous problems in grouping, segmentation, and matching. Previous work involved an objective function formulation of the problem, resulting in a uniform method of addressing problems in object recognition that have heretofore been approached by heterogenous complex vision systems. The complexity of our objective functions resulted in numerous optimization failures, not unexpectedly. Here we propose to prime the system with estimates of the objects parameters at a coarse, more abstract, scale. We discuss how this might be done. These initial values are expected to bring the state of the system closer to good minima.

Paper Details

Date Published: 16 September 1992
PDF: 9 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140000
Show Author Affiliations
Joachim Utans, Yale Univ. (United States)
Gene R. Gindi, Yale Univ. (United States)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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