A Probabilistic Approach to Image Feature Extraction, Segmentation and Interpretation

[摘要] This 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.

[发布机构] University of Waterloo

[效力级别] Computer Vision

[关键词] Mathematics;Computer Vision;Image Segmentation;Image Interpretation;Graphical Probability Models