For animals with advanced nervous systems, survival and reproduction can depend upon accurate perception of the environment. To understand how a perceptual system should solve a perception task, it is important to consider designs for an ideal observer, a theoretical system that solves a perception task in an optimal way given specific constraints. I studied three specific classification tasks related to the problem of identifying and segmenting leaves in foliage-rich images. In order to derive the ideal observers for these tasks, I created a database of hand-segmented leaves which served to define the ground-truth for these tasks. I also created a new method that uses the ground-truth as a basis for performing statistical inference (classification) in a nearly optimal way. This made it possible for me to approximate ideal observers by approximating an optimal classifier for each task. I also conducted psychophysical experiments to measure human performance in these tasks. The results provide information about how the human visual system should and does interpret foliage-rich images. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2010-05-784 |
| Date | 21 September 2010 |
| Creators | Ing, Almon David |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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