CS 671 Advances in Visual Perception

Course Contents

1. The neurophysiological approach to visual perception



2. Marr- theory of visual perception, Hypotheses and work of Gregory and Gibson, Model and functions of the Retina



3. Primary visual cortex, Temporal aspects of vision, Motion and depth perception



4. Neuro-dynamics of visual attention, Computational models of visual attention and visual search



5. Visual stimulus-reward association, emotion and motivation



6. Computational models for visual cognition and visual cortex



7. Shape from X



8. Shape-based recognition, Recognition by a combination of views



9. Models of invariant object recognition, Generic Object Recognition



10. Imaging Geometry - Camera Models and calibration



11. Multi-view vision



12. Model based vision, spatial perception



13. Multi-Feature and decision fusion



14. 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 :
    1. Generic object recognition
    
    
    2. Automatic Target Recognition
    
    
    3. Visual Surveillance
    
    
    4. Multi-modal biometrics
    
    
    5. Content Based Image and Video Retrieval
    
    
    6. Video object representation in MPEG-IV and -VII (CSS space)
    
    
    7. Image and video based Scene Rendering
    
    
    8. Scene Modeling from Registered and unregistered Images
    
    
    9. Affine and Euclidean View Synthesis
    
    
    10. Multisensor data fusion
    
    
    11. Super-resolution image reconstruction from low-resolution images
    
    
    12. Visual knowledge representation



15. Design of man-machine interaction

TextBooks

1. Theories of Visual Perception; by Ian E. Gordon; Psychology Press (Taylor and Francis Group), 2004


2. Computational Neuroscience of Vision; by Edmund Rolls, Gustavo Deco; Oxford University Press, 2002


3. Curvature Scale Space Representation: Theory, Applications and MPEG-7 Standardization; Farzin Mokhtarian and Miroslaw Bober; Kluwer Academic Publishers, 2003


4. Multiple View Geometry in Computer Vision; R. Hartley and A. Zisserman; Cambridge University press, 2003.


5. Machine Vision ; W. E. Snyder and H. QI; Cambridge University press, 2004.


6. Introduction to Statistical pattern recognition; K. Fukunaga, 2nd Ed. Academic Press, New York, 1990.

Journals

1. IEEE-T-PAMI ( IEEE Transactions on Pattern Analysis and Machine Intelligence)


2. ACM Transactions on Applied Perception


3. Nature - Neurosciences.


4. Vision research (Elsevier).

Seminar Schedule

Lecture Slides