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Welcome to my home page. I'm a fifth year PhD candidate in CBCL at the Massachusetts Institute of Technology. I am working under prof. Tomaso Poggio. My research interests include computational neuroscience (visual attention, object recognition in particular), computer vision, machine learning and biometrics. 

Contents

  1. 1 Links ResumeCVCode Computer vision blog
  2. 2 Links
  3. 3 Patents
  4. 4 Research Projects
    1. 4.1 A Computational Model of Visual Attention
    2. 4.2 Biologically Inspired Speech Processing
    3. 4.3 Computer vision techniques for leaf identification
    4. 4.4 Cancelable Biometrics
    5. 4.5 Fingerprint Image enhancement
    6. 4.6 Fingerprint Recognition
    7. 4.7 Biometrics
  5. 5 Other Projects
    1. 5.1 Attention and Object recognition in Videos
    2. 5.2 Speaker Recognition and Identification
    3. 5.3 Recipes for Gist Computation
    4. 5.4 Optical Character Recognition using Wavelets and Neural Networks

Links

Research Projects

A Computational Model of Visual Attention

Technical Reports
Abstracts/Posters
  • Chikkerur, S., Serre, T., Tan, C, Poggio, T. The role of top-down feature-based and contextual guidance mechanisms in complex natural visual search. Society for Neuroscience annual meeting, Washington, DC, Nov.(2008).
  • Tan, C., Serre, T., Chikkerur, S., & Poggio, T. Feature-based and contextual guidance mechanisms in complex natural visual search [Abstract]. Journal of Vision, 9(8):1189, 1189a, http://journalofvision.org/9/8/1189/,doi:10.1167/9.8.1189.
  • Chikkerur, S., Serre, T., Tan, C, Poggio, T  Bayesian inference theory predicts the physiological properties of attention. Workshop on normative electrophysiology, NIPS 2009
  • Chikkerur, S., Serre, T., Tan, C, Poggio, T  Bayesian inference theory predicts the physiological properties of attention. Workshop on bounded-rational analysis of human cognition, NIPS 2009

Biologically Inspired Speech Processing

Technical Reports
  • Rifkin, R., J. Bouvrie, K. Schutte, S. Chikkerur, M. Kouh, T. Ezzat and T. Poggio. Phonetic Classification Using Hierarchical, Feed-forward, Spectro-temporal Patch-based Architectures, CBCL Paper #267/AI Technical Report #2007-019, Massachusetts Institute of Technology, Cambridge, MA, March, 2007.

Computer vision techniques for leaf identification

Abstract/Poster presentations
  • Peter Wilf, Sharat Chikkerur, Stefan Little, Scott Wing, Thomas Serre. Leaf Identification Automated Using a Computational Model of the Primate Vision System. Botany and Mycology conference, 2009

Cancelable Biometrics

Publications

Fingerprint Image enhancement

Publications
  • Sharat Chikkerur, Alexander N. Cartwright, Venu Govindaraju: Fingerprint enhancement using STFT analysis. Pattern Recognition 40(1): 198-211 (2007)
  • Sharat Chikkerur, Venu Govindaraju, Alexander N. Cartwright: Fingerprint Image Enhancement Using STFT Analysis. ICAPR (2) 2005: 20-29

Fingerprint Recognition

Publications
  • Sharat Chikkerur, Alexander N. Cartwright, Venu Govindaraju: K-plet and Coupled BFS: A Graph Based Fingerprint Representation and Matching Algorithm. ICB 2006: 309-315
  • Sharat Chikkerur, Sharath Pankanti, Alan Jea, Nalini K. Ratha, Ruud M. Bolle: Fingerprint Representation Using Localized Texture Features. ICPR (4) 2006: 521-524
  • Sharat Chikkerur, Nalini K. Ratha: Impact of Singular Point Detection on Fingerprint Matching Performance. AutoID 2005: 207-212

Biometrics

Publications
  • Sharat Chikkerur, Chaohang Wu, Venu Govindaraju: A Systematic Approach for Feature Extraction in Fingerprint Images. ICBA 2004: 344-350
  • Karthik Sridharan, Sankalp Nayak, Sharat Chikkerur, Venu Govindaraju: A Probabilistic Approach to Semantic Face Retrieval System. AVBPA 2005: 977-986
  • Amit Mhatre, Sharat Chikkerur, Venu Govindaraju: Indexing Biometric Databases Using Pyramid Technique. AVBPA 2005: 841-849
  • Shamalee Deshpande, Sharat Chikkerur, Venu Govindaraju: Accent Classification in Speech. AutoID 2005: 139-143

Other Projects

Attention and Object recognition in Videos

Recognition and localization of multiple objects in cluttered visual scenes is a difficult problem for biological as well as 
machine vision systems. Computer vision techniques rely upon scanning the image at all positions and scales to detect 
objects in large scenes. In contrast, the biological visual system deals with scene complexity with the help of visual 
attention, the ability to focus(’attend’) to behaviorally relevant stimuli while ignoring clutter. 
In this work, we provide experimental results demonstrating that a biological approach that does not require scanning is a 
practical solution. The system consists of a general purpose, biologically-motivated bottom-up attentional front-end (Itti et al' 01)  
together with a hierarchical feed-forward recognition algorithm (Serre et al '07).

Speaker Recognition and Identification

Recipes for Gist Computation

Optical Character Recognition using Wavelets and Neural Networks