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A Novel Plausible Model for Visual Perception
- Source :
- IEEE ICCI
- Publication Year :
- 2006
- Publisher :
- IEEE, 2006.
-
Abstract
- Traditionally, how to bridge the gap between low-level visual features and high-level semantic concepts has been a tough task for researchers. In this article, we propose a novel plausible model, namely cellular Bayesian networks (CBNs), to model the process of visual perception. The new model takes advantage of both the low-level visual features, such as colors, textures, and shapes, of target objects and the interrelationship between the known objects, and integrates them into a Bayesian framework, which possesses both firm theoretical foundation and wide practical applications. The novel model successfully overcomes some weakness of traditional Bayesian Network (BN), which prohibits BN being applied to large-scale cognitive problem. The experimental simulation also demonstrates that the CBNs model outperforms purely Bottom-up strategy 6% or more in the task of shape recognition. Finally, although the CBNs model is designed for visual perception, it has great potential to be applied to other areas as well.
- Subjects :
- Visual perception
Computer science
Process (engineering)
business.industry
Bayesian probability
Bayesian network
Cognition
Bayesian inference
Object (computer science)
Machine learning
computer.software_genre
Bridge (nautical)
Object detection
Task (project management)
Human-Computer Interaction
Image texture
Artificial Intelligence
Human visual system model
Structural information theory
Artificial intelligence
business
computer
Software
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2006 5th IEEE International Conference on Cognitive Informatics
- Accession number :
- edsair.doi.dedup.....113bf0f9e45d2f503d8eede3b9d7d794