CIMSIVP 2017 - 2017 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (IEEE CIMSIVP'17)
Topics/Call fo Papers
Constructive understanding of computational principles of image, multimedia, signal and visual information processing, perception and cognition is one of the most fundamental challenges of contemporary science. Deeper insight into such computational intelligence helps to advance intelligent systems research to achieve robust performance.
Implementing integrated principles in artificial systems may help us achieve better, faster and more efficient intelligent systems. The Symposium will address theory and applications of non-traditional computational intelligence approaches in image, multimedia, signal and vision processing.
Topics
Bio-inspired Vision
Neuronal mechanisms of visual, multimedia and signal processing
Low level vision and its relationship to biological machinery
Artificial learning systems for image, multimedia and information processing and evidential reasoning for recognition
Intelligent search in communications networks
Modeling and agent-oriented vision architectures
Perception of shape, shadows, poses, color, illumination and motion in object recognition
Tracking for inferring shapes and 3D motions
Active visual perception, attention and robot vision
Emerging learning algorithms including deep learning in computer vision, multimedia and signal processing
Functional Magnetic Resonance Imaging (fMRI) studies of visual segmentation and perception
Application of computational vision in areas of
Automated target identification and acquisition systems in defense and industry
Biomedical imaging
3D photography
Face recognition
Learning to segment camouflaged objects
Motor actions and robotics
Image databases and indexing
Multimedia signal analysis
Hardware implementation of computational vision, multimedia and signal analysis
Any other topics related to biological approaches in computer vision, multimedia and signal analysis
Implementing integrated principles in artificial systems may help us achieve better, faster and more efficient intelligent systems. The Symposium will address theory and applications of non-traditional computational intelligence approaches in image, multimedia, signal and vision processing.
Topics
Bio-inspired Vision
Neuronal mechanisms of visual, multimedia and signal processing
Low level vision and its relationship to biological machinery
Artificial learning systems for image, multimedia and information processing and evidential reasoning for recognition
Intelligent search in communications networks
Modeling and agent-oriented vision architectures
Perception of shape, shadows, poses, color, illumination and motion in object recognition
Tracking for inferring shapes and 3D motions
Active visual perception, attention and robot vision
Emerging learning algorithms including deep learning in computer vision, multimedia and signal processing
Functional Magnetic Resonance Imaging (fMRI) studies of visual segmentation and perception
Application of computational vision in areas of
Automated target identification and acquisition systems in defense and industry
Biomedical imaging
3D photography
Face recognition
Learning to segment camouflaged objects
Motor actions and robotics
Image databases and indexing
Multimedia signal analysis
Hardware implementation of computational vision, multimedia and signal analysis
Any other topics related to biological approaches in computer vision, multimedia and signal analysis
Other CFPs
- 2017 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS'17)
- 2017 IEEE Symposium on Computational Intelligence in Robotic Rehabilitation and Assistive Technologies (IEEE CIR2AT'2017)
- 2017 IEEE Symposium on Computational Intelligence for Security and Defense Applications (IEEE CISDA'17)
- 2017 IEEE Symposium on Computational Intelligence in Scheduling and Network Design (IEEE CISND'17)
- 2017 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (IEEE CIVTS' 17)
Last modified: 2017-07-19 16:35:45