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Multizoom Activity Recognition Using Machine Learning, Raymond Smith
Multizoom Activity Recognition Using Machine Learning, Raymond Smith
Electronic Theses and Dissertations
In this thesis we present a system for detection of events in video. First a multiview approach to automatically detect and track heads and hands in a scene is described. Then, by making use of epipolar, spatial, trajectory, and appearance constraints, objects are labeled consistently across cameras (zooms). Finally, we demonstrate a new machine learning paradigm, TemporalBoost, that can recognize events in video. One aspect of any machine learning algorithm is in the feature set used. The approach taken here is to build a large set of activity features, though TemporalBoost itself is able to work with any feature set …
Pattern Recognition Via Machine Learning With Genetic Decision-Programming, Carl C. Hoff
Pattern Recognition Via Machine Learning With Genetic Decision-Programming, Carl C. Hoff
Browse all Theses and Dissertations
In the intersection of pattern recognition, machine learning, and evolutionary computation is a new search technique by which computers might program themselves. That technique is called genetic decision-programming. A computer can gain the ability to distinguish among the things that it needs to recognize by using genetic decision-programming for pattern discovery and concept learning. Those patterns and concepts can be easily encoded in the spines of a decision program (tree or diagram). A spine consists of two parts: (1) the test-outcome pairs along a path from the program's root to any of its leaves and (2) the conclusion in that …