Portland State University. Department of Computer Science
Date of Publication
Doctor of Philosophy (Ph.D.) in Computer Science
Computer vision, Neural networks (Computer science), Machine learning
1 online resource (xx, 131 pages)
I conduct a study of learning in HMAX-like models, which are hierarchical models of visual processing in biological vision systems. Such models compute a new representation for an image based on the similarity of image sub-parts to a number of specific patterns, called prototypes. Despite being a central piece of the overall model, the issue of choosing the best prototypes for a given task is still an open problem. I study this problem, and consider the best way to increase task performance while decreasing the computational costs of the model. This work broadens our understanding of HMAX and related hierarchical models as tools for theoretical neuroscience, while simultaneously increasing the utility of such models as applied computer vision systems.
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Thomure, Michael David, "The Role of Prototype Learning in Hierarchical Models of Vision" (2014). Dissertations and Theses. Paper 1665.