Neurocomputing and probabilistic propagation in computer vision
One of the earliest (also well-studied) research areas in artificial intelligence is the
study of visual perception, and the study of neurons of the brain using connectivist
models or neurocomputing. Where cognitive and mathematical psychology,
and neuroscience studied how the brain and perception works in their own
paradigms, artificial intelligence provided tools from theoretical and applied computer
science to study the aforementioned areas using digital computers.
This study focuses on examining two sides of neurocomputing, namely
probabilistic graphical models and artificial neural networks in solving early perception,
or early vision and inference tasks. More specifically, the study examines
probabilistic propagation such as denoising tasks under similarity measures and
parallelization schemes. And finally, combining probabilistic graphical models
and artificial neural networks into a pipeline model for solving inference tasks
from a set of imaging measurements.
Keywords: Algorithms, Artificial intelligence, Inverse problems, scientific computing.
...
Publisher
Jyväskylän yliopistoISBN
978-951-39-8467-0ISSN Search the Publication Forum
2489-9003Keywords
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- JYU Dissertations [870]
- Väitöskirjat [3599]
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