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Edward Adelson. Blau, David A Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2008-04-23T14:35:17Z 2008-04-23T14:35:17Z 2007 2007 http://hdl.handle.net/1721.1/41246 213331468 Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007. Includes bibliographical references (p. 89). Using low level video data, features can be extracted from images to predict search time and statistical saliency in a way that models the human visual system. The statistical saliency model helps explain how visual search and attention systems direct eye movement when presented with an image. The statistical saliency of a target object is defined as distance in feature space of the target to its distractors. This thesis presents a real-time, full through-put, parallel processing implementation design for the statistical saliency model, utilizing the stability and parallelization of programmable circuits. Discussed are experiments in which real-time saliency analysis suggests the addition of temporal features. The goal of this research is to achieve accurate saliency predictions at real-time speed and provide a framework for temporal and motion saliency. Applications for real-time statistical saliency include live analysis in saliency research, guided visual processing tasks, and automated safety mechanisms for use in automobiles. by David A. Blau. M.Eng. 89 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Real-time statistical saliency using high throughput circuit design and its applications in psychophysical study Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="4b1081eb-26a9-4e11-8274-d32af3303a2a"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Real-time statistical saliency using high throughput circuit design and its applications in psychophysical study</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2007</PublicationDate> <Authors> <Author> <DisplayName>Blau, David A</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Using low level video data, features can be extracted from images to predict search time and statistical saliency in a way that models the human visual system. The statistical saliency model helps explain how visual search and attention systems direct eye movement when presented with an image. The statistical saliency of a target object is defined as distance in feature space of the target to its distractors. This thesis presents a real-time, full through-put, parallel processing implementation design for the statistical saliency model, utilizing the stability and parallelization of programmable circuits. Discussed are experiments in which real-time saliency analysis suggests the addition of temporal features. The goal of this research is to achieve accurate saliency predictions at real-time speed and provide a framework for temporal and motion saliency. Applications for real-time statistical saliency include live analysis in saliency research, guided visual processing tasks, and automated safety mechanisms for use in automobiles.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>