Sanjiv Kumar

I have joined Google Research as a Research Scientist starting September 2005. But I am still maintaining this page.

New contact email: sanjivk AT google.com

PhD (2000 – 2005)

3110, Newell Simon Hall

The Robotics Institute

School of Computer Science

Carnegie Mellon University

5000 Forbes Avenue

Pittsburgh PA 15213, USA

Advisor: Martial Hebert

Research Interests

Statistical Learning, Graphical Models, Computer Vision, Medical Imaging, Robotics

More About Me

Recent Publications [ All Publications ]

. A. Talwalkar, S. Kumar and H. A. Rowley

Large-Scale Manifold Learning

IEEE Computer Vision and Pattern Recognition (CVPR), 2008.

[pdf]

. M. Kim, S. Kumar, V. Pavlovic and H. A. Rowley

Face Tracking and Recognition with Visual Constraints in Real-World Videos

IEEE Computer Vision and Pattern Recognition (CVPR), 2008.

[pdf]

. S. Kumar and H. A. Rowley

Classification of Weakly-Labeled Data with Partial Equivalence Relations

IEEE International Conference on Computer Vision (ICCV), 2007.

[pdf]

Some additional results and parts of the video and retrieval datasets used in this work can be seen here.

· S. Kumar and M. Hebert

Discriminative Random Fields

International Journal of Computer Vision (IJCV), 68(2), 179-201, 2006.

[pdf]

· S. Kumar, J. August and M. Hebert

Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study

Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2005.

[pdf]

This paper is an extended and revised version of the earlier work presented in Snowbird Learning Workshop, 2004.

· S. Kumar

Models for Learning Spatial Interactions in Natural Images for Context-Based Classification

PhD Thesis, The Robotics Institute, School of Computer Science, Carnegie Mellon University, September 2005.

[pdf] [ps]

Revised October 2005.

· S. Kumar and M. Hebert

A Hierarchical Field Framework for Unified Context-Based Classification

IEEE International Conference on Computer Vision (ICCV), 2005.

[pdf] [ps]

Revised October 2005.

· C. Rother, S. Kumar, V. Kolmogorov and A. Blake

Digital Tapestry

IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June, 2005.

[pdf]

· S. Kumar and M. Hebert

Approximate Parameter Learning in Discriminative Fields

Snowbird Learning Workshop, Utah, 2004.

[pdf] [ps]

The synthetic dataset used for learning and inference experiments can be obtained from here.

· S. Kumar and M. Hebert

Multiclass Discriminative Fields for Parts-Based Object Detection

Snowbird Learning Workshop, Utah, 2004.

[pdf]

· S. Kumar and M. Hebert

Discriminative Fields for Modeling Spatial Dependencies in Natural Images

Advances in Neural Information Processing Systems, NIPS 16, 2004.

[pdf] [ps]

The binary denoising synthetic dataset used for training and testing can be obtained from here.

· B. Nabbe, S. Kumar, and M. Hebert

Path Planning with Hallucinated Worlds

In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2004.

[pdf]

· S. Kumar and M. Hebert

Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification

IEEE International Conference on Computer Vision (ICCV), 2003.

[pdf] [ps]

· S. Kumar and M. Hebert

Man-Made Structure Detection in Natural Images using a Causal Multiscale Random Field

IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2003.

[pdf]

Some more example results and comparisons.

The structure detection database used for training and testing can be obtained from here.

· S. Kumar, A. C. Loui, and M. Hebert

An Observation-Constrained Generative Approach for Probabilistic Classification of Image Regions

Image and Vision Computing, 21, pp. 87-97, 2003.

[pdf]

A shorter version of this paper appeared in the following workshop:

· S. Kumar, A. C. Loui, and M. Hebert

Probabilistic Classification of Image Regions using an Observation-Constrained Generative Approach

ECCV Workshop on Generative Models based Vision (GMBV), 2002.

[pdf]