Black & White HDR

http://thehdrimage.com/tag/black-white-hdr/
http://www.denzomag.com/2010/09/black-and-white-hdr-photography-amazing-examples/

Goal: Develop a math theory of B&W HDR image formation.

Image Mosaic

Goal: Simplify a given image by a different sampling.

Image Super-resolution

Goal: Super resolution is one of the most important tasks in computer vision and computer graphics. Students will design an image super-resolution technique and study it’s theoretical property and/or a fast implementation of it.

Reference:

http://cgm.technion.ac.il/Computer-Graphics-Multimedia/Undergraduate-Projects/2009/SuperResolution/SuperResolution.html

Image Inpainting using PDE

Goal: http://www.math.ucla.edu/~bertozzi/papers/CH.pdf

Radon Transform and Its Inverse Transform

Goal: The project aims to study the Radon transform for formation of MRI images.

References:

http://www.cis.upenn.edu/~cjtaylor/RESEARCH/projects/ImageMatching/ImageMatching.html.

Image Matching Using Convex Optimization

(http://www.cis.upenn.edu/~cjtaylor/RESEARCH/projects/ImageMatching/ImageMatching.html)

Goal: The project studies approaches to recasting many classic image matching problems including, stereopsis, motion estimation, image registration and 3D volumetric matching as convex optimization problems that can be solved effectively using convex optimization methods.

Related Publications

• Solving Image Registration Problems Using Interior Point Methods. C. J. Taylor and A. Bhusnurmath. European Conference on Computer Vision, October 2008
• Graph Cuts via $\ell_1$ Norm Minimization. A. Bhusnurmath and C. J. Taylor. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol: 30, No: 10, Pgs: 1866-1871, October 2008
• Solving Stereo Matching Problems Using Interior Point Methods. A. Bhusnurmath and C. J. Taylor. Fourth International Symposium on 3D Data Processing, Visualization and Transmission, 3DPVT, Pgs: 321-329, June 2008
• Applying Convex Optimization Techniques to Energy Minimization Problems in Computer Vision. A. Bhusnurmath. PhD Thesis, Computer and Information Science Department, University of Pennsylvania, 2008

Iterative Closest Point Method

Goal: The project aims to determine numerical method to register between two point cloud images.

References:

http://en.wikipedia.org/wiki/Iterative_closest_point

http://www.mathworks.com/matlabcentral/fileexchange/12627