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| September 8
| September 8
| Vishal Patel
| Vishal Patel
|
| Wavelets with Composite Dilations
|-
|-
| September 15
| September 15
==Talk Abstracts Fall 2011==
==Talk Abstracts Fall 2011==
===Wavelets with Composite Dilations===
Speaker: Vishal Patel -- Date: September 8, 2011
Sparse representation of visual information lies at the foundation of many image processing applications, such as image restoration and compression. It is well known that wavelets provide a very sparse representation for a large class of signals and images. For instance, from a continuous perspective, wavelets can be shown to sparsely represent one-dimensional signals that are smooth away from point discontinuities. Unfortunately, separable wavelet transforms have some limitations in higher dimensions. For this reason, in recent years there has been considerable interest in obtaining directionally-oriented image decompositions. Wavelets with composite dilations offer a general and especially effective framework for the construction of such representations. In this talk, I will discuss the theory and implementation of several recently introduced multiscale directional transforms. Then, I will present a new general scheme for creating an M-channel directional filter bank. An advantage of an M-channel directional filter bank is that it can project the image directly onto the desired basis. Applications in image denoising, deconvolution and image enhancement will be presented.
This talk will present a brief overview of random field models for computer vision. Markov Random Field (MRF) models have been most popular class of models
This talk will present a brief overview of random field models for computer vision. Markov Random Field (MRF) models have been most popular class of models
for computer vision applications. Recently, new class of models, Conditional Random Fields (CRF), has been introduced. Although CRFs were first introduced for labeling 1D sequences, they have also been incorporated for 2D images for applications such as labeling and object recognition. Another model, Discriminative Random Field (DRF) model, inspired by CRF, has been applied successfully for image denoising and labeling. In this talk, the key differences between MRF and CRF/DRF will be highlighted. The main diff erence between the two classes of models can be best understood on the basis of generative vs .discriminative probabilistic models based on graphs. Hence, graphical models will also be briefly discussed in the talk.
for computer vision applications. Recently, new class of models, Conditional Random Fields (CRF), has been introduced. Although CRFs were first introduced for labeling 1D sequences, they have also been incorporated for 2D images for applications such as labeling and object recognition. Another model, Discriminative Random Field (DRF) model, inspired by CRF, has been applied successfully for image denoising and labeling. In this talk, the key differences between MRF and CRF/DRF will be highlighted. The main diff erence between the two classes of models can be best understood on the basis of generative vs .discriminative probabilistic models based on graphs. Hence, graphical models will also be briefly discussed in the talk.
