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dc.contributor.author Zuñiga-Olea, Juan Apolinar
dc.date.accessioned 2010-01-29T20:51:05Z
dc.date.available 2010-01-29T20:51:05Z
dc.date.issued 2009-12
dc.identifier.uri http://hdl.handle.net/10139/952
dc.description.abstract In the 1950s, scientists predicted it would take ten years to develop robots capable of performing most human tasks. Today, 50 years later, scientists are still predicting it will take ten years. The primary obstacle to building such robots is the problem of interpreting visual data, or image processing. Computers process images as grids of grey-level values. The structures in those values are the structures in the image. In other words, image processing requires mathematical analysis of images, where the images are represented as grids of numbers. At the most basic level, images consist of shapes and textures. These textures range from completely regular (e.g., wallpaper) to completely random (e.g., white noise). The mathematical qualities of regular textures are significantly different from the qualities of random textures because of the high degree of structure present in regular textures and absent in random ones. This project examines structured textures, those close enough to regular textures to be viewed as deformed versions of regular textures (e.g., Figure 1.1). In particular, we are interested in learning how to relate properties of the regular texture with properties of the structured texture (i.e., after deformation). Ultimately, we want to answer the question: Given an unknown structured texture, can we recover a regular texture and the associated deformation? en_US
dc.language.iso en_US en_US
dc.rights All rights reserved to author and California State University Channel Islands
dc.subject Regular textures en_US
dc.subject Autocorrelation en_US
dc.subject Thin-plate spline en_US
dc.subject Mathematics thesis en_US
dc.title Mathematical Modeling for Structured Textures en_US
dc.type Thesis en_US

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