The neurophysiological approach to visual perception; Marr's theory of visual perception, Hypotheses and work of Gregory and Gibson, Model and functions of the Retina, Primary visual cortex, temporal aspects of vision, motion and depth perception, neuro-dynamics of visual search; Visual stimulus-reward association, emotion and motivation; Computational models for visual cognition and visual cortex; shape from X; Shape-based recognition, Recognition by a combination of views; Models of invariant object recognition, Generic Object Recognition; Imaging Geometry - Camera Models and calibration; MuIti-view vision; Model based vision, spatial perception; multi-Feature and decision fusion.
Combination of recent concepts and methods in Visual Cognition, Computer Vision, Neuro - and soft-computing models, Pattern Recognition and Virtual Reality to solve complex problems such as: Generic object recognition, Automatic Target Recognition; Visual Surveillance; multi-modal biometrics, Content Based Image and Video Retrieval; Video object representation in MPEG-IV and VII (CSS space); Image and video based Scene Rendering; Scene Modeling from Registered and unregistered Images, Affine and Euclidean View Synthesis, Multisensor data fusion, Super-resolution image reconstruction from low-resolution images; design of man-machine interaction and visual knowledge representation.
Pre-Requisites
None
Parameters
Credits
Type
Date of Introduction
4-0-0-0-8-12
Elective
Oct 2011
Previous Instances of the Course
Jan 2016 - May 2016 Teaching Assistants : Samik Banerjee.
