EE 596, Wavelets, Fall 2003
Instructor
Antonio Ortega
Signal and Image Processing Institute
Integrated Media Systems
Center
University of Southern California
3740 McClintock Ave., EEB 436
Los Angeles, CA 90089-2564
Tel: (213) 740-2320
Fax: (213) 740-4651
Email: ortega AT sipi DOT usc DOT edu
Schedule
- Lectures Tuesday and Thursday, 11:00-12:20pm, OHE 100
- Office hours Tuesday and Thursday, 1:30-3:30pm, EEB 436
- Teaching Assistant Sangeetha Somayajula - email:
somayaju AT usc DOT edu - TA Office Hours: 3-5pm Mo-We, EEB 106, Phone 213-740-3487
- Grader Hua Xie - email huaxie AT sipi DOT usc DOT edu
- Midterm November 6th, 2003, 11am-12:30pm
- Final There will be no final
Grading
Grading will be based on a midterm (40%), several homeworks (20%), and
a project (40%). The homeworks will include both computer assignments and
solution of textbook problems. The computer assignments will be based on
Matlab or C. We will provide pointers to existing software that can be used
in these computer assignments.
DEN Access
This semester I will use the Blackboard system offered by
DEN to post assignments and solutions, as well as grades.
Please register with DEN and create your DEN profile
as soon as possible by following
the instructions on the
DEN Webpage.
Prerequisites
EE 483, Introduction to Digital Signal Processing, or equivalent
course. Please note that the course will assume some knowledge of standard
DSP concepts as well as of some basic linear algebra. If you took these
two courses some time ago it would be a good idea to review some of the
key material early in the semester.
Recommended preparation
MATH 599, Introduction to Wavelets, and EE 569, Introduction
to Digital Image Processing. None of these courses is required.
Texbooks
Required
Recommended
Some useful pointers
- General links
- Tutorials
- Software
- People
Rough Outline
- Introduction and Review (2 weeks)
- Discrete Wavelet Transform and Filterbanks (4 weeks)
- Continuous Wavelet Transform (2 weeks)
- Midterm
- Applications and projects (5 weeks)
Material covered (Note: based on the material covered in Fall'01,
subject to change, it will be updated as we move along)
Projects
- Project requirements:
- Projects should be done individually.
- Each project must involve using the wavelet transform as a
tool. A signal is analyzed/classified, etc by computing its wavelet
transform and then the required task (e.g. denoising/classification) is
performed in the transform domain.
- The Matlab toolbox or C libraries can be used for the project.
C libraries are available at Dartmouth
and Rutgers. .
- Whichever method is used, the source code will have to be
made available along with the project report (only for the routines that
you write, which could call those available in matlab or C.)
- Reporting requirements: a final report and a class presentation.
- Project descriptions and references
- Test data for the projects
- Software packages
Sample Project Topics (from Fall'01) - Organized by areas
- Coding
- Implementation of a Pyramidal Image Coder
- Compression of finite-length discrete-time signals using flexible
adaptive wavelet packets<
- Wavelet Descriptors for Planar Curves
- Sinusoidal Modeling of Audio Signals Using Frame-Based Perceptually
Weighted Matching Pursuits
- Low Complexity Motion Estimation Algorithm for Long-term
Memory Motion Compensation Using Hierarchical Motion Estimation
- Global/Local Motion Compensation for 3D Video Coding Based
on Lifting Techniques
- Classification/Recognition
- Shift Invariant Texture Classification by Using Wavelet Frame
- Texture Feature Extraction with Non-Separable Wavelet Transforms
- Comparison of Two Wavelet-Based Image Watermarking Techniques
- Application of Wavelet Transform in Analysis of Fractal Signals
- Human-Face Detection and Location in Color Images Using Wavelet
Decomposition
- Music/Speech Classifier using Wavelets
- Wavelet Decomposition for the Analysis of Heart Rate Variability
- Wavelet-based fMRI dynamic activation detection
- Wavelet analysis of evoked potentials
- Detection of Microcalcifications in Mammograms using Wavelet
Transforms
- Wavelet-based Tone Classification for Thai
Denoising
- Comparison of Denoising via Block Weiner Filtering in Wavelet Domain
with Existing Ad-hoc Linear and Non-linear Denoising Techniques
- Wavelet-domain filtering of data with Poisson noise
- Contrast Enhancement and De-noising using Wavelets
- Wavelet Denoising Applied to Time Delay Estimation
- Comparison of image denoising using Wavelet Shrinkage vs.
MMSE using an exponential decay autocorrelation model
- Threshold Denoising Effects on Covariance Matrices
- Comparing Performance of Different Wavelet De-noising algorithms
with Basic Noise Removal Techniques
- Information Driven Denosing of MEG data in the Wavelets Domain
- Two Methods for Image Enhancement
Watermarking/Halftoning
- Introduction of IWT to wavelet-based watermarking and its effect
on performance
- Inverse Halftoning using Wavelets
Communications
- Wavelets Based MC-CDMA System
- MMSE Estimation Multi-user detection for CDMA System based
on Wavelet Transform
Homeworks
©1996,1998,1999, 2001, 2003 Antonio Ortega.
Last modified: Wed Sep 10 11:23:54 PDT 2003
