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Kaushik Mitra

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Kaushik Mitra
Kaushik Mitra
Department of Electrical and Computer Engineering
2050 Duncan Hall,
Rice University, Houston,
Texas 77025, USA
Research
Interests
Academic
Experience
Publications
Cell Phone: +1(301)741-3953
Email: [email protected]
Web: http://www.ece.rice.edu/ km23/
Computational Imaging, Computer Vision, Machine Learning and Optimization Techniques
Postdoctoral Research Associate
Aug 2011 -
Electrical and Computer Engineering
Rice University, Houston
Research Area: Computational Imaging
Advisor: Prof. Ashok Veeraraghavan
M.S. and Ph.D.
Aug 2004 - Aug 2011
Electrical and Computer Engineering
University of Maryland, College Park
Research Area: Computer Vision
Advisor: Prof. Rama Chellappa
M.E.
Aug 2001 - Jan 2003
Electrical Communication Engineering
Indian Institute of Science, Bangalore, India
Research Area: Computer Network
Advisor: Prof. Anurag Kumar
B.Tech.
Aug 1998 - Aug 2001
Radiophysics and Electronics
University of Calcutta, Kolkata, India
Project: Analytic Study of mm-Wave Oscillator
Advisor: Prof. Subal Kar
Book Chapters
• Recognition of Motion Blurred Faces, K. Mitra, P. Vageeswaran and R. Chellappa,
Motion Deblurring: Algorithms and Systems, Cambridge University Press, A. N.
Rajagopalan and R. Chellappa (Editors), 2014.
Journal Publications
• Generalized Assorted Camera Arrays: Robust Cross-channel Registration and Applications, J. Holloway, K. Mitra, S. Koppal, A. Veeraraghavan, in preparation
for submission to IEEE Transactions on Image Processing (TIP), to be submitted
in March 2014.
• Compressive Epsilon Photography for Post-Capture Control in Digital Imaging,
A. Ito, S. Tambe, K. Mitra, A. Sankaranarayanan, A. Veeraraghavan, accepted
in SIGGRAPH (ACM Transactions on Graphics), 2014.
• A Framework for Analysis of Computational Imaging Systems: Role of Signal
Prior, Sensor Noise and Multiplexing, K. Mitra, O. Cossairt and A. Veeraraghavan, accepted in IEEE Transactions on Pattern Analysis and Machine Learning
(TPAMI), 2014.
• Towards Compressive Camera Networks, K. Mitra, A. Veeraraghavan, A. Sankaranarayanan and R. G. Baraniuk, IEEE Computer, May 2014.
• Blur and Illumination Robust Face Recognition via Set-Theoretic Characterization, P. Vageeswaran, K. Mitra and R. Chellappa, IEEE Transactions on Image
Processing (TIP), 2013.
• Analysis of Sparse Regularization Based Robust Regression Algorithms, K. Mitra,
A. Veeraraghavan and R. Chellappa, IEEE Transactions on Signal Processing
(TSP), 2013.
Conference Publications
• Can we Beat Hadamard Multiplexing? Data-driven Design and Analysis for Computational Imaging Systems, K. Mitra, O. Cossairt and A. Veeraraghavan, IEEE
International Conference on Computational Photography (ICCP), 2014.
• Improving Resolution and Depth-of-Field of Light Field Cameras Using a Hybrid Imaging System, V. Boominathan, K. Mitra and A. Veeraraghavan, IEEE
International Conference on Computational Photography (ICCP), 2014.
• To Denoise or Deblur: Parameter Optimization for Imaging Systems, K. Mitra,
O. Cossairt and A. Veeraraghavan, SPIE Electronic Imaging, 2014.
• Performance Bounds for Computational Imaging, O. Cossairt, A. Veeraraghavan,
K. Mitra and M. Gupta, Imaging and Applied Optics Technical Papers, OSA,
2013.
• Performance Limits for Computational Photography, O. Cossairt, K. Mitra and
A. Veeraraghavan, International Workshop on Advanced Optical Imaging and
Metrology, Springer, 2013.
• Light Field Denoising, Light Field Superresolution and Stereo Camera Based Refocussing using a GMM Light Field Patch Prior, K. Mitra and A. Veeraraghavan,
CVPR Workshop on Computational Cameras and Displays, 2012.
• A Hierarchical Approach For Human Age Estimation, P. Thukral, K. Mitra and
R. Chellappa, IEEE International Conference on Acoustics, Speech, and Signal
Processing (ICASSP) 2012.
• Large-Scale Matrix Factorization with Missing Data under Additional Constraints,
K. Mitra, S. Sheorey, R. Chellappa, Advances in Neural Information Processing
Systems (NIPS) 2010.
• Robust RVM Regression Using Sparse Outlier Model, K. Mitra, A. Veeraraghavan
and R. Chellappa, IEEE Conference on Computer Vision and Pattern Recognition
(CVPR) 2010.
• Robust Regression Using Sparse Learning for High Dimensional Parameter Estimation Problem, K. Mitra, A. Veeraraghavan and R. Chellappa, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2010.
• A Scalable Projective Bundle Adjustment Algorithm using the L∞ Norm, K. Mitra
and R. Chellappa, Proceedings of the Indian Conference on Computer Vision,
Graphics and Image Processing (ICVGIP) 2008.
Research
Experience
Rice University, Houston, TX.
Postdoctoral Research Associate, 2011 I am working in the new and emerging field of Computational Imaging (CI). Traditionally, imaging/camera design and computer vision (inference from imaging data)
are considered as independent fields. However, significant advantage can be obtained
by designing the imaging devices and inference algorithms in a unified framework. Recently, many CI systems have been proposed for tackling the problem of motion blurring
(for example Flutter Shutter), focus blurring (for example coded aperture cameras and
extended depth of field cameras) , light field capture, etc. Given that there are so
many CI systems, it is imperative to compare and characterize their performance. I
proposed a framework for analysis of CI systems that characterizes the performance of
the systems by taking into account the effect of optical coding, image prior and sensor
noise (TPAMI 2014). I then used this framework to find optimal camera parameters for
conventional camera (SPIE 2014) and to design optimal optical codes for CI systems
(ICCP 2014).
I am also interested in designing novel CI systems. Current light field cameras (such
as Lytro) have very poor spatial resolution. I have proposed a hybrid imaging system,
consisting of a standard light field camera and a high resolution conventional camera,
to generate high resolution light fields (ICCP 2014). I have also proposed a general
framework for light field processing tasks such as denoising and (angular and spatial)
superresolution (CVPR workshop 2012). Recently, I have proposed compressive epsilon
photography, a technique for achieving post-capture control of focus, iso, exposure
and aperture in a traditional camera by acquiring a carefully selected set of images
and computationally reconstructing images corresponding to other focus and aperture
settings (under review in SIGGRAPH, 2014).
University of Maryland, College Park, MD.
Graduate Research Assistant, 2004 - 2011
I proposed robust and efficient machine learning algorithms for a large class of computer vision problems. Many computer vision problems can be formulated as learning
problems such as regression and matrix factorization. However, because of variations
due to viewpoint, occlusion, shadows, etc., the visual data suffers from significant outliers and missing data, and this makes the learning problems very challenging. Towards
solving the problem of outliers, I have proposed robust regression algorithms, based on
sparse regularization and Bayesian techniques, that can handle large amount of outliers
(CVPR 2010, ICASSP 2010). I have performed analysis of the robust algorithms to
provide estimates of the fraction of outliers in a dataset that the proposed algorithms
can successfully handle (TSP 2013). Towards solving the problem of missing data
in matrix factorization, I have formulated it as a low-rank semidefinite programming
problem with the advantage that it can handle large-scale vision datasets (NIPS 2010).
I have also worked in the traditional vision problems of Structure from Motion (SfM)
and face recognition. In SfM I have proposed a scalable algorithm for projective bundle
adjustment (ICVGIP 2008) and in face recognition I have proposed an algorithm to
identify blurred and poorly illuminated faces (TIP 2013).
Indian Institute of Science, Bangalore, India.
Master’s thesis research, 2002
I worked on queuing models for HTTP traffic under the supervision of Prof. Anurag
Kumar.
Teaching
Experience
Rice University, Houston, TX.
Designed assignments and projects for graduate-level computer vision course (Elec
547). Delivered guest lectures in computer vision (Elec 345, Elec 547) and computation
photography (Elec 549) courses.
University of Maryland, College Park, MD.
Delivered guest lectures in the graduate-level image understanding course (ENEE 731).
Work
Experience
Samsung India Software Operations, Bangalore, India.
Senior Software Engineer, 2003-2004
I worked on the multimedia software (audio, video, streaming players) of the Samsung
mobile phone platform.
Honors
• Ranked Second in M.E. (IISc, Bangalore).
• All India Rank Second in GATE (Admission test for graduate studies in Engineering).
• Ranked First in B.Tech (University of Calcutta).
Talks and Poster
Presentations
• To Denoise or Deblur: Parameter Optimization for Imaging Systems, SPIE Electronic Imaging, San Francisco January 2014.
• Light Field Denoising, Light Field Superresolution and Stereo Camera Based Refocussing using a GMM Light Field Patch Prior, CVPR Workshop on Computational Cameras and Displays, Providence, Rhode Island, June 2012.
• Handling Outliers and Missing Data in Statistical Data Models, talk at Electronics and Communication Sciences Unit (ECSU), Indian Statistical Institute (ISI),
Kolkata, India,January 2011.
• Large-Scale Matrix Factorization with Missing Data under Additional Constraints,
poster presentation at Advances in Neural Information Processing Systems (NIPS),
Vancouver, B.C., Canada, December 2010.
• Fitting Models to Data: Handling Outliers and Missing Data, talk at Mitsubishi
Electric Research Laboratories (MERL), Cambridge, July 2010.
• Fitting Models to Data: Handling Outliers and Missing Data, talk at MIT Media
Lab, Camera Culture Lab, July 2010.
• Robust RVM Regression Using Sparse Outlier Model, poster presentation at the
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San
Francisco,June 2010.
• Robust Regression Using Sparse Learning for High Dimensional Parameter Estimation Problem, poster presentation at the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Dallas, March 2010.
• Scalable Bayesian Robust Regression for High Dimensional Applications, talk at
Statistics Department, Florida State University, March 2009.
• A Scalable Projective Bundle Adjustment Algorithm using the L∞ Norm, talk
at the Indian Conference on Computer Vision, Graphics and Image Processing
(ICVGIP), Bhubaneswar, India, December 2008.
References
Rama Chellappa
ECE Department
University of Maryland
College Park, MD 20742
Phone: 301-405-4526
[email protected]
Ashok Veeraraghavan
ECE Department
Rice University
Houston, Tx 77005
Phone: 713-348-5104
[email protected]
Richard Baraniuk
ECE Department
Rice University
Houston, Tx 77005
Phone: 713-348-5132
[email protected]
B. S. Manjunath
ECE Department
University of California
Santa Barbara, CA 93106
Phone: 805-893-7112
[email protected]
Amit Roy-Choudhury
EE Department
University of California
Riverside, CA 92521
Phone: 951-827-7886
[email protected]
Oliver Cossairt
EECS Department
Northwestern University
Evanston, IL 60208
Phone: 847-491-0895
[email protected]
Fly UP