|
The areas of Pattern Recognition and Machine Learning encompasses a wide variety of topics that are essential for most visual information processing problems. Our primary areas of interest include classifier design and systems that learn from large collections of data.
Specific activities in this area include:
* Design of highly accurate and efficient classifiers
* Learning from large document collections
* Learning from user feedback and weaker supervision.
Related Papers:
* Ilayaraja Prabhakaran, Neeba N.V., and C.V. Jawahar "Efficient Implementation of SVM for Large Class Problems" in Proc. of the 19th International Conferenc eon Pattern Recognition(ICPR 08), Dec. 8-11,2008, Florida, USA.
* M. N. S. S. K. Pavan Kumar and C. V. Jawahar, "Configurable Hybrid Architectures for Character Recognition Applications" in Proceedings of Eighth International Conference on Document Analysis and Recognition(ICDAR), Seoul, Korea 2005, Vol 1, pp 1199-1203
* Ranjeeth Kumar and C.V. Jawahar "Kernel Approach to Autoregressive Modeling" in Proc. of The Thirteen National Conference on Communications(NCC 2007), Kanpur, 2007 .
* Ranjeeth Kumar, S.Manikandan and C.V.Jawahar "Task Specific Factors for Video Characterization" in 5th Indian Conference on Computer Vision, Graphics and Image Processing, Madurai, India, LNCS 4338 pp.376-387, 2006.
Related Thesis:
* Design of Hierarchical Classifiers for Efficient and Accurate Pattern Classification
MNSSK Pavan Kumar, Year of Completion : 2005
* Kernel Methods and Factorization for Image and Video Analysis
Ranjeeth Kumar, Year of Completion : 2007
* A Probabilistic Learning Approach for Modelling Human Activities
Ravi Kiran Sarvadevabhatla, Year of Completion : 2004
Faculty
- C V Jawahar
- Anoop M Namboodiri
- P J Narayanan
(Head)
|