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dc.contributor.authorPal, Christopher Josephen 14:26:28 (GMT) 14:26:28 (GMT)
dc.description.abstractThis thesis describes a probabilistic approach to imagesegmentation and interpretation. The focus of the investigation is the development of a systematic way of combining color, brightness, texture and geometric features extracted from an image to arrive at a consistent interpretation for each pixel in the image. The contribution of this thesis is thus the presentation of a novel framework for the fusion of extracted image features producing a segmentation of an image into relevant regions. Further, a solution to the sub-pixel mixing problem is presented based on solving a probabilistic linear program. This work is specifically aimed at interpreting and digitizing multi-spectral aerial imagery of the Earth's surface. The features of interest for extraction are those of relevance to environmental management, monitoring and protection. The presented algorithms are suitable for use within a larger interpretive system. Some results are presented and contrasted with other techniques. The integration of these algorithms into a larger system is based firmly on a probabilistic methodology and the use of statistical decision theory to accomplish uncertain inference within the visual formalism of a graphical probability model.en
dc.format.extent1323657 bytes
dc.publisherUniversity of Waterlooen
dc.rightsCopyright: 2000, Pal, Chris. All rights reserved.en
dc.subjectComputer Visionen
dc.subjectImage Segmentationen
dc.subjectImage Interpretationen
dc.subjectGraphical Probability Modelsen
dc.titleA Probabilistic Approach to Image Feature Extraction, Segmentation and Interpretationen
dc.typeMaster Thesisen
dc.pendingfalseen Mathematicsen
uws-etd.degreeMaster of Mathematicsen

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