IEEE Symposium on Biological Vision Inspired Intelligence in Computer Vision (IEEE BVICV)
The 2019 IEEE Symposium on Biological Vision Inspired Intelligence in Computer Vision (BVICV’19) will be held in a very beautiful port city, Xiamen, China, during December 6-9, 2019. BVICV’19 is part of the 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2019, http://www.ssci2019.org/), a flagship annual international conference of the IEEE Computational Intelligence Society (CIS).
With the rapid advance of visual neuroscience and cognitive science, increasing efforts have been attracting to understand the computing principles of BVSs better and then to reproduce comparative intelligence, especially the appealing ability of adaptive processing when facing the complicated dynamic external environment around us. On the one hand, many computational models have been built to simulate the BVSs at multiple scales to explore and understand how the BVSs work to perceive the external environment effectively and effortlessly. On the other hand, as a representative kind of human-inspired intelligence, biological vision inspired models and systems have been increasingly developed and successfully applied in many computer vision applications by mimicking the information processing mechanisms of various biological visual systems (BVSs) including primate and non-primate species. This symposium aims to provide an important platform for researchers across all related fields to exchange ideas for pushing forward the success of biological vision inspired intelligence in computer vision applications, and for targeting the next generation of brain-inspired or even brain-like intelligence.
This symposium welcomes original, unpublished contributions from authors. Topics include (but not limited):
- Models for the neurons of various visual levels
- Neural coding and decoding of visual information
- Neural networks for local visual circuit
- Visual mechanism inspired deep neural networks
- Visual models for image processing
- Visual mechanism inspired models for computer vision applications
- Hardware implementations of visual models
- Artificial vision related software and hardware