Simulating Visual Attention

Sandon, Peter A.

doi:10.1162/jocn.1990.2.3.213

Cited by 49 publications

(13 citation statements)

References 46 publications

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“…Recently; Sandon (1990) developed a neural network, based on a connectionist model, to simulate selective visual attention. The model is hierarchical, as it contains multiple processing layers that enable features to be extracted, focused, and then extracted again (Grossberg, 1988, with permission) is among the first neural-network-computational frameworks to incorporate attentional considerations.…”

Section: Simulations Of Visual Attentionmentioning

confidence: 99%

The Neuropsychology of Attention

Cohen

1993

Critical Issues in Neuropsychology

210

118

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A Continuation Order Plan is available for this series. A continuation order will bring delivery of each new volume immediately upon publication. Volumes are billed only upon actual shipment. For further information please contact the publisher.Originally published by Plenum Press, New York in 1993 Softcover reprint of the hardcover 1st edition 1993All rights reserved No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording, or otherwise, without written permission from the Publisher An unfortunate case of "hemispatial" inattention. ("M Babinet prevenu par sa portiere de Ia visite de Ia comete." From Le Charivari, 1858, by Honore Daumier. Babcock Bequest; Courtesy of Museum of Fine Arts, Boston, Massachusetts.) PrefaceAt the beginning of this project, I was struck by several paradoxes regarding the study of attentional phenomena within neuropsychology. On one hand, the concept of attention has been central to the historical development of psychology. Attention was considered a fundamental and inescapable aspect of human experience by Wilhelm Wundt, William James, and the other founders of modern psychology. On the other hand, for much of the 20th century, psychologists tried desperately to avoid acknowledging the need for an attentional construct. Because attention has an intangible quality and cannot be specified as a unitary process, many behavioral scientists considered the construct of attention theoretically incoherent. Some of their concerns regarding the nature of attentional phenomena were clearly justified. It is also apparent, however, that explanations of behavior and cognition that fail to account for attentional phenomena cannot fully capture human experience.Recently, there has been a shift in zeitgeist, and attention has received more favorable treatment within the cognitive neurosciences. Humans cannot handle an infinite amount of simultaneous information. Therefore, cognitive processes must exist that direct our focus to information that is important, and that permit the selection of stimuli and responses from the large universe of alternatives. Regardless of whether one advocates the existence of a discrete attentional process, it is necessary to account for the occurrence of those phenomena that we normally label as aspects of attention. Consideration of the mechanisms underlying the selection of information and the control of stimulus and response processes is critical if we hope to understand human cognitive experience.The necessity of studying the mechanisms underlying attentional phenomena is particularly evident within neuropsychology. Patients with brain disorders frequently do not perform at optimal levels, even when task variables are held constant. They may fail to detect an object in the environment, even though it can be demonstrated that they have adequate perceptual capability. Reference to performance deficits stemming from attentional dysfunction is common in cl...

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Section: Simulations Of Visual Attentionmentioning

confidence: 99%

The Neuropsychology of Attention

Cohen

1993

Critical Issues in Neuropsychology

210

118

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“…However, those systems served as source of inspiration for practical solutions for real images once machine learning techniques like neural networks had grown mature enough. In [206], image processing operators were combined with the attentive models to make it applicable to more realistic images. He applies a Laplacian-of-Gaussians (LoG) like operator to feature maps to model the receptive fields and enhance the interesting events.…”

Section: Feature Detection As Part Of the Pre-attentive Stagementioning

confidence: 99%

Local Invariant Feature Detectors: A Survey

Tuytelaars

Mikolajczyk

2007

FNT in Computer Graphics and Vision

1,126

573

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In this survey, we give an overview of invariant interest point detectors, how they evolved over time, how they work, and what their respective strengths and weaknesses are. We begin with defining the properties of the ideal local feature detector. This is followed by an overview of the literature over the past four decades organized in different categories of feature extraction methods. We then provide a more detailed analysis of a selection of methods which had a particularly significant impact on the research field. We conclude with a summary and promising future research directions.

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“…In this way, the scale space theory (Lindeberg, n.d.;Witkin, 1983) can be used towards accelerating visual processing, generally on a coarse to fine approach. Several works use this approach based on multi-resolution (Itti et al, 1998;Sandon, 1990;1991;Tsotsos et al, 1995) for allowing vision tasks to be executed in computers. Other variants, as the Laplacian pyramid (Burt, 1988), have been also integrated as a tool for visual processing, mainly in attention tasks (Tsotos, 1987;Tsotsos, 1987).…”

Section: Related Workmentioning

confidence: 99%