Real-time statistical saliency using high throughput circuit design and its applications in psychophysical study
Name
213331468-MIT.pdf
Description
Full printable version
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3.65 MB
Format
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Checksum (MD5)
004cea66ef205dbfc492039064960935
Author(s)
Blau, David A
Advisor(s)
Edward Adelson.
Date Issued
2007
Publisher
Massachusetts Institute of Technology
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.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 89).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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