Distributed Computer Vision in Camera Networks


I am particularly interested in computer vision problems that occur in networks of a large number (tens to hundreds) of cameras dispersed throughout an environment. Traditional computer vision methods are often poorly-suited to such networks, because they assume a small, fixed number of stationary cameras that can be centrally, simultaneously processed. We have developed distributed solutions for determining visual overlap and camera calibration in large dynamic camera networks, and are planning to study high-level vision problems (e.g. change detection, multi-object tracking, image-based query, or view synthesis). This work is supported by an NSF CAREER award.

Computer Vision, Machine Learning, and Optimization for IMRT


Through my affiliation with CenSSIS, I support several undergraduate and graduate projects in biomedical image processing. I am particularly interested in computer vision and machine learning problems related to intensity modulated radiotherapy (IMRT), an exciting new technology for cancer treatment. One project involves developing computer vision algorithms to aid in the automatic segmentation of organs from 3-D CT scans acquired immediately prior to radiation treatment. A second project investigates the relationship between a patient's body/organ geometry and the multiple radiation beams that are used to treat their cancer. We have investigated both the breast and prostate. These projects include collaborations with medical physicists at Memorial Sloan-Kettering Cancer Center and Massachusetts General Hospital.

Change Detection and Understanding


In collaboration with Badri Roysam, I am interested in change detection as well as change understanding in image and video sequences. We recently undertook a comprehensive survey of pixel-level change detection algorithms. We are more broadly interested in leveraging pixel-level change detection algorithms, along with domain-specific models for objects and behaviors of interest, to produce semantic change understanding algorithms that can help interpret and annotate image sequences the same way an expert observer would. To date, we have demonstrated this capability in the context of biomedical image sequences (e.g. time-lapse video of neurons and stem cells) to quickly and accurately summarize megabytes of image sequence data.

3D Modeling and Tracking from Distributed, Mobile Sensors


Chuck Stewart, Daniel Freedman, and I collaborate on research involving 3D data representation, registration, and integration of range and visual imagery, sponsored by the US Army Intelligence and Security Command. I am particularly interested in change detection in range imagery, a critical problem in many homeland security applications.


For a more detailed description of my research contributions and interests, please take a look at this document.

Biography


I joined the Electrical, Computer, and Systems Engineering department at Rensselaer Polytechnic Institute in August, 2001, where I am now an Associate Professor. I have a dual B.A. degree in math and computational and applied math from Rice University, an M.A. in computational and applied math from Rice University, and M.A. and Ph.D. degrees in electrical engineering from Princeton University. I was an intern at the Mathworks, developing numerical linear algebra and signal processing routines. During my Ph.D. I investigated several estimation problems in digital video, including the efficient estimation of projective transformations and the synthesis of photorealistic "virtual video", in collaboration with IBM's Tokyo Research Laboratory.


My current research interests include deformable registration and segmentation of three- and four-dimensional biomedical volumes, machine learning for radiotherapy applications, distributed computer vision problems on large camera networks, and modeling 3D environments with visual and range imagery. At Rensselaer, I am associated with the NSF ERC for Subsurface Sensing and Imaging Systems (CenSSIS) as well as the Center for Next Generation Video (CNGV) and the Center for Pervasive Computing and Networking (CPCN). I received an NSF CAREER award in March 2003. I am a member of the 2007 DARPA Computer Science Study Group.


Here is a brief Curriculum Vita.

Graduate Students


Current

Juda Becker, Ph.D. (co-advised with Chuck Stewart)
Anil Cheriyadat, Ph.D.
Siqi Chen, Ph.D.
David Doria, Ph.D.
Linda Rivera, Ph.D.
Eric Smith, Ph.D. (co-advised with Chuck Stewart)


Graduated

Omar Al-Kofahi, Ph.D. (2005, co-advised with Badri Roysam; now with American Science and Engineering)
Srinivas Andra, M.S. (2003)
Zhaolin Cheng, M.S. (2006, now with Captira Analytics)
Haeyong Chung, M.S. (2005)
Dhanya Devarajan, Ph.D. (2006, now with Immersive Media, Portland, OR)
Yongwon Jeong, Ph.D. (2006; now with Samsung, South Korea)
Chao Ling, M.S. (2006, co-advised with Paul Schoch)
Renzhi Lu, Ph.D. (2007, now with Bloomberg, NYC)


For Prospective Students

I receive many e-mails from prospective students asking me if I am hiring students into my group or if I will consider their applications based on an attached resume. I unfortunately cannot respond to each of these individually. You should know that I do not make decisions regarding admission or financial aid, and I only consider prospective students who have gone through the formal application process to our department. If you refer to me in your statement of purpose as someone with whom you are interested in working, I will review your application for a possible fit with my group. I hire students with strong image processing and computer vision backgrounds; if your background is in computer hardware or networking, I am probably not an appropriate advisor for you. I am much more likely to respond to your e-mail if you show that you are actually well-informed about my research (e.g. have read some of my papers) instead of expressing a generic interest in computer vision.

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