A fundamental problem in machine vision is the classifcation of objects which may have unknown position, orientation, or a combination of these and other transformations. The massive amount of data required to accurately form an appearance-based model of an object under all values of shift and rotation transformations has discouraged the incorporation of the combination of both transformations into a single model representation. This Master's Thesis documents the theory and implementation of a hierarchical classifier, named the Information Theoretic Decision Tree system, which has the demonstrated ability to form appearance-based models of objects which are shift and rotation invariant which can be searched with a great reduction in evaluations over a linear sequential search. Information theory is utilized to obtain a measure of information gain in a feature space recursive segmentation algorithm which positions hyperplanes to local information gain maxima. This is accomplished dynamically through a process of local optimization based on a conjugate gradient technique enveloped by a simulated annealing optimization loop. Several target model training strategies have been developed for shift and rotation invariance, notably the method of exemplar grouping, in which any combination of rotation and translation transformations of target object views can be simulated and folded into the appearance-based model. The decision tree structure target models produced as a result of this process effciently represent the voluminous training data, according rapid test-time classification of objects.
| Identifer | oai:union.ndltd.org:wpi.edu/oai:digitalcommons.wpi.edu:etd-theses-1806 |
| Date | 11 May 1999 |
| Creators | Andrews, Michael J. |
| Contributors | Michael A. Gennert, Committee Member, David Cyganski, Advisor, Denise W. Nicoletti, Committee Member |
| Publisher | Digital WPI |
| Source Sets | Worcester Polytechnic Institute |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses (All Theses, All Years) |
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