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dc.contributor.advisorEdward H. Adelson and William T. Freeman.en_US
dc.contributor.authorTappen, Marshall Friend, 1976-en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2007-07-18T13:02:25Z
dc.date.available2007-07-18T13:02:25Z
dc.date.copyright2006en_US
dc.date.issued2006en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/37878
dc.descriptionThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.en_US
dc.descriptionAlso issued in pages.en_US
dc.descriptionMIT Rotch Library copy: issued in pages.en_US
dc.descriptionIncludes bibliographical references (leaves 137-144).en_US
dc.description.abstractThe goal of computer vision is to use an image to recover the characteristics of a scene, such as its shape or illumination. This is difficult because an image is the mixture of multiple characteristics. For example, an edge in an image could be caused by either an edge on a surface or a change in the surface's color. Distinguishing the effects of different scene characteristics is an important step towards high-level analysis of an image. This thesis describes how to use machine learning to build a system that recovers different characteristics of the scene from a single, gray-scale image of the scene. The goal of the system is to use the observed image to recover images, referred to as Intrinsic Component Images, that represent the scene's characteristics. The development of the system is focused on estimating two important characteristics of a scene, its shading and reflectance, from a single image. From the observed image, the system estimates a shading image, which captures the interaction of the illumination and shape of the scene pictured, and an albedo image, which represents how the surfaces in the image reflect light. Measured both qualitatively and quantitatively, this system produces state-of-the-art estimates of shading and albedo images.en_US
dc.description.abstract(cont.) This system is also flexible enough to be used for the separate problem of removing noise from an image. Building this system requires algorithms for continuous regression and learning the parameters of a Conditionally Gaussian Markov Random Field. Unlike previous work, this system is trained using real-world surfaces with ground-truth shading and albedo images. The learning algorithms are designed to accommodate the large amount of data in this training set.en_US
dc.description.statementofresponsibilityby Marshall Friend Tappen.en_US
dc.format.extent144 leavesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleLearning continuous models for estimating intrinsic component imagesen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc124509737en_US


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