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Proceedings Paper

Can machine vision be helped from insights into human vision?
Author(s): Theo Pavlidis
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Paper Abstract

Machine vision has exhibited rather slow progress over the last 25 years compared to other areas of Computer Technology and an interesting question is whether more progress will be made by continuing intensively the current approaches in research or instead by searching for new directions. I will present a thesis that more research along some of the prevailing lines will lead to best to marginal advances. For example, current edge detectors are very good at finding step edges. The problem is that their major objective, finding object outlines, is not equivalent to step edge finding. It seems that people find object outlines by a process of simultaneous interpretation and low level processing of the image. Such integration should be contrasted to one of the prevailing models in machine vision which assumes a linear sequence of a few distinct processes from low level to high level vision. (When researches talk about 'interpretation guide segmentation' they usually refer to the labeling of already obtained features using high level models of the scene.) If the levels interact strongly, then optimizing the processing techniques for each level separately (for example edge detection for low level vision) is not going to be fruitful. Since human vision is the reason that we believe that machine vision is even possible, a deeper examination of the human or animal vision recognition process is essential to the further progress of machine vision.

Paper Details

Date Published: 27 August 1992
PDF: 6 pages
Proc. SPIE 1666, Human Vision, Visual Processing, and Digital Display III, (27 August 1992); doi: 10.1117/12.135985
Show Author Affiliations
Theo Pavlidis, SUNY/Stony Brook (United States)

Published in SPIE Proceedings Vol. 1666:
Human Vision, Visual Processing, and Digital Display III
Bernice E. Rogowitz, Editor(s)

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