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The perception of material qualities in real-world images

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dc.contributor.advisor Edward H. Adelson. en_US
dc.contributor.author Sharan, Lavanya en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.date.accessioned 2010-04-28T17:14:04Z
dc.date.available 2010-04-28T17:14:04Z
dc.date.copyright 2009 en_US
dc.date.issued 2009 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/54644
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 135-146). en_US
dc.description.abstract One can easily tell if a sidewalk is slippery, if food is fresh, if a spoon is made of plastic or stainless steel, or if a suspicious looking mole warrants a trip to the doctor. This ability to visually identify and discriminate materials is known as material perception and little is known about it. We have measured human material judgments on a wide range of complex, real world materials. We have gathered several diverse image databases and made use of them to conduct psychophysical studies. We asked observers to classify surfaces and objects as being made of fabric, paper, plastic or other common material categories. In the first part of this thesis, we present experiments that establish that observers can make these judgments of material category reliably, quickly and in challenging conditions of rapid presentation. We find that categorization performance cannot be explained by simple, low-level cues like color or high spatial frequencies. In the second part of the thesis, we explore judgments beyond those of common material categories. Observers judged many dimensions of material appearance such as matte vs. glossy, opaque vs. translucent, rigid vs. nonrigid, soft vs. rough to touch, and even genuine vs. fake for familiar object categories like flowers, fruits and dessert. Observers were surprisingly accurate, even in 40 millisecond presentations. In the final part of this thesis, we compare the performance of state-of-art computer vision techniques with human performance on our images and tasks and find current techniques to be severely lacking. en_US
dc.description.abstract (cont.) Taken together, our findings indicate that material perception is a distinct mechanism and can be as fast and flexible as object recognition or scene perception. When recognizing materials, low-level image information is of limited use for both humans and computer vision systems. We conclude that material recognition is a rich and challenging problem domain and there is much ground to be covered in both visual perception and computer vision. en_US
dc.description.statementofresponsibility by Lavanya Sharan. en_US
dc.format.extent 146 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights 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. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Electrical Engineering and Computer Science. en_US
dc.title The perception of material qualities in real-world images en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.identifier.oclc 606579568 en_US


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