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Proceedings Paper

Selective visual attention in object recognition and scene analysis
Author(s): Adilson Gonzaga; Evelina M. de Almeida Neves; Annie France Frere
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Paper Abstract

An important feature of human vision system is the ability of selective visual attention. The stimulus that reaches the primate retina is processed in two different cortical pathways; one is specialized for object vision (`What') and the other for spatial vision (`Where'). By this, the visual system is able to recognize objects independently where they appear in the visual field. There are two major theories to explain the human visual attention. According to the Object- Based theory there is a limit on the isolated objects that could be perceived simultaneously and by the Space-Based theory there is a limit on the spatial areas from which the information could be taken up. This paper deals with the Object-Based theory that states the visual world occurs in two stages. The scene is segmented into isolated objects by region growing techniques in the pre-attentive stage. Invariant features (moments) are extracted and used as input of an Artificial Neural Network giving the probable object location (`Where'). In the focal-stage, particular objects are analyzed in detail through another neural network that performs the object recognition (`What'). The number of analyzed objects is based on a top-down process doing a consistent scene interpretation. With Visual Attention is possible the development of more efficient and flexible interfaces between low sensory information and high level process.

Paper Details

Date Published: 1 October 1998
PDF: 11 pages
Proc. SPIE 3460, Applications of Digital Image Processing XXI, (1 October 1998); doi: 10.1117/12.323179
Show Author Affiliations
Adilson Gonzaga, Univ. de Sao Paulo (Brazil)
Evelina M. de Almeida Neves, Univ. de Sao Paulo (Brazil)
Annie France Frere, Univ. de Sao Paulo (Brazil)

Published in SPIE Proceedings Vol. 3460:
Applications of Digital Image Processing XXI
Andrew G. Tescher, Editor(s)

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