Thu Jun 7, 2012
In this talk we will present our recent work on 3D LIDAR point clouds
compression. The new algorithm is based on the idea of compression by
classification. It utilizes the unique height function simplicity as well
as the local spatial coherence and linearity of the aerial LIDAR data and
can automatically compress the data to the desired...
Allyson Butler and Grant Martin
Wed Mar 28, 2012
The topics covered will include:
Working with files & live sources
Blob/point detection, feature extraction, and matching techniques
Video Motion analysis with Optical flow, and block matching
Video stabilization and stereo image rectification
Classification algorithms to recognize image content
Thu Mar 8, 2012
In this talk, I will present two deblurring methods, one exploits the spatial interactions in images, i.e. the self-similarity; and the other explicitly takes into account the sparse characteristics of natural images and does not entail solving a numerically ill-conditioned backward-diffusion.
In particular, the self-similarity is defined by a...
Thu Feb 16, 2012
Magneto-Resonance (MR) images are believed to have Rician distributed noise. In this talk, we propose two variational models involving total variation (TV) regularization to denies images corrupted by Rician distributed noise. For the first model, we implement the L2 and Sobolev H1 gradient descent methods in our numerical simulations on...
Tue Nov 2, 2010
In this talk we will discuss various aniosotropic PDEs. We will then discuss integro-differential
equations inspired from (BV, L2) and (BV, L1) decompositions. Although the original motivation came from a variational approach, the resulting IDEs can be extended using standard techniques from PDE-based image processing. We use filtering, edge...