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<title>EE 596, Wavelets, Fall 2006</title>
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<h1>EE 596, Wavelets, Fall 2006</h1>
  <a href="http://sipi.usc.edu/%7Eortega"> Antonio Ortega</a> 
<address> <a href="http://sipi.usc.edu/"> Signal and Image Processing Institute<br>
  </a> <a href="http://www.usc.edu/dept/imsc/"> Integrated Media Systems
Center <br>
  </a> University of Southern California<br>
  3740 McClintock Ave., EEB 436<br>
  Los Angeles, CA 90089-2564 
<p> Tel: (213) 740-2320<br>
  Fax: (213) 740-4651<br>
  Email: antonio DOT ortega AT sipi DOT usc DOT edu</a><br>
  <li><b>Lectures</b> Tuesday and Thursday, 11:00-12:20pm, OHE 100C  </li>
    <li> <b>Office hours</b> Tuesday and Thursday, 1:30-3pm, EEB 436, and by
    <li> <b>Teaching Assistant</b> Ivy Tseng, hsinyits AT usc Dot edu,
  </li> TA Office Hours - Mon 10am-noon, Wed 1-3pm, EEB 441.
    <li> <b>Grader </b> Ozlem Kalinli
- Grader office hours: F 2-4pm, EEB 427.
    <li><b>Midterm 1 </b> Oct 10, 2006 (in class) </li>
    <li><b>Midterm 2 </b> Nov 14, 2006 (in class) </li>
    <li><b>Final</b> There will be no final exam </li>
<p> Each midterm will account for 30% of the grade. The remaining 40% will
be based on homeworks and a project. There will be around 4 homeworks and
the project will be due at the end of the semester. </p>
<p> </p>
<h3>DEN Access</h3>
<p> 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
<a href="http://den.usc.edu">  DEN Webpage</a>.
<p> <i> EE 483, Introduction to Digital Signal Processing</i>, 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. <br>
<h3>Recommended preparation</h3>
<p> <i>MATH 599, Introduction to Wavelets,</i> and <i>EE 569, Introduction
to Digital Image Processing</i>. None of these courses is required.  </p>
<h3>Texbooks </h3>
    <li><a href="http://lcavwww.epfl.ch/%7Evetterli/">Martin Vetterli</a>
and  <a href="http://www.andrew.cmu.edu/user/jelenak/"> Jelena Kovacevic</a>,
  <a href="http://www.andrew.cmu.edu/user/jelenak/Book/index.html"> <i>Wavelets and Subband
Coding</i></a>, Prentice Hall, 1995.  </li>
      <li> <a href="http://www.mathworks.com/wavelet.html">      Matlab
Wavelet  Toolbox</a>, This toolbox is available on the student
accounts.  <br>
  <li> <a href="http://www-math.mit.edu/%7Egs/">Gilbert Strang</a> and <a
href="http://www.engr.wisc.edu/ece/faculty/nguyen_truong.html">Truong Q.
Nguyen</a>,    <a
and Filter Banks</i></a>,  Wellesley-Cambridge Press, 1995 </li>
      <li><a href="http://www.systems.caltech.edu/EE/Faculty/PPV.html">P.
P. Vaidyanathan</a>, <a
href="http://www.prenhall.com/013/605717/60571-7.html">    <i>Multirate
Systems and Filter Banks</i>    ,</a> Prentice Hall, 1993<br>
  <h3>Some useful pointers</h3>
    <li> General links                   
  <a href="http://www.mathsoft.com/wavelets.html">Wavelet page at Mathsoft</a>
<li> <a href="http://www.amara.com/current/wavelet.html"> Amara's Wavelet
Page</a> </li>
          <li> <a href="http://www.math.wustl.edu/wavelet/"> Washington Univ.
Wavlet NetCare </a>      </li>
      <li> Tutorials                   
  <li> <a href="http://www.amara.com/IEEEwave/IEEEwavelet.html">An Introduction
to Wavelets </a> </li>
          <li> <a
The Wavelet tutorial by Robi Polikar</a>      </li>
      <li> Software                   
  <li> <a href="http://www-dsp.rice.edu/software/RWT/">Rice Wavelet Toolbox
for Matlab</a> </li>
          <li> <a href="http://www-stat.stanford.edu/%7Ewavelab/"> Wavelab
at Stanford </a> </li>
          <li> <a href="http://www.mathworks.com/products/wavelet/"> The
Mathworks  Matlab Wavelet Toolbox</a> </li>
          <li> <a
Wavelet Packet Lab </a> </li>
          <li> <a href="http://www.wavelsoftware.com/">WaveL Software</a>
          <li> <a
Imager Wavelet Library at UBC </a> </li>
          <li> <a
href="http://www.cs.dartmouth.edu/~sp/liftpack/lift.html"> Liftpack</a>
      <li> People                   
  <li> <a href="http://iaks-www.ira.uka.de/home/klappi/people.html"> People
          </a>      </li>
  <h3> Material covered (Note: based on the material covered in Fall'04,
subject to change)</h3>
<b> Weeks 1 and 2 </b>
Introduction and Motivation. Signal representation using bases.
Hilbert spaces. Orthogonal, bi-orthogonal basis and overcomplete
Example: representing finite energy continuous signals using Haar basis.
Example of construction of Haar basis
      <li> <b> Week 3</b>
  Bases for discrete signals. Finite and infinite dimensional spaces.
      <li> <b> Week 4</b> Overcomplete expansions. Searching for the
  best representation. Matching pursuits and variations. Compressed
      <li> <b> Weeks 5 and 6</b>
  Multirate signal processing. Filterbanks
  and discrete wavelet transforms. Time domain, frequency domain
  and polyphase domain representations.
      <li> <b> Week 7-8</b> 2-Channel orthogonal filterbanks. Iterated
  filterbanks. Bi-orthogonal filterbanks. Lifting
  factorizations. Multichannel filterbanks. Modulated filterbanks.
      <li> <b> Weeks 9 and 10</b> Multidimensional wavelets. Edgelets,
  bandlets, ridgelets and other extensions. Lifting for video
      <li> <b> Week 11</b> Continuous time wavelets. Series
  expansions of continuous signals. Haar, Sinc, Meyer, Daubechies
  and Spline wavelets. Mallat algorithm. </li>
      <li> <b> Weeks 12 and 13</b> Applications. Compression. Classification. Graphics.  </li>
  <li> Project requirements:                 
        <li> Projects should be done individually.      </li>
          <li> 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.      </li>
          <li> The Matlab toolbox or C libraries can be used for the project.
C libraries are available at <a
href="http://www.cs.dartmouth.edu/%7Egdavis/wavelet/wavelet.html"> Dartmouth
          </a> and   <a
href="http://math.rutgers.edu/%7Eojanen/wavekit/"> Rutgers. </a>.     
          <li> 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.)    </li>
      <li> Reporting requirements: a final report and a class presentation.
      <li> <a href="./Projects596.html"> Project descriptions and references
      </a> </li>
      <li>  Test data for the projects </li>
      <li> <a href="./ee596_wavelet_toolbox.html"> Software packages</a>
        <p> Examples of coding using JPEG and the latest version of JPEG 2000
(provided by Christos Chrysafis, HP Labs)      </p>
        <li><a href="./Images/original.gif"> Original Image </a>      </li>
          <li><a href="./Images/jpeg_40_1.gif"> JPEG Coded at 0.2 bpp (40:1
compression) </a>      </li>
          <li><a href="./Images/jpeg2000_40_1.gif"> JPEG2000 Coded at 0.2
bpp (40:1 compression)</a>      </li>
          <li><a href="./Images/jpeg_70_1.gif"> JPEG Coded at 0.11 bpp (70:1
compression)</a>      </li>
          <li><a href="./Images/jpeg2000_70_1.gif"> JPEG2000 Coded at 0.11
bpp (70:1 compression) </a>      </li>
      <p> Demos on the web      </p>
<li>  <a href="http://www.andrew.cmu.edu/user/jelenak/"> Jelena Kovacevic's webpage</a> contains numerous pointers to books, projects, demos, applets, etc. </li>
        <li><a href="http://www.math.sc.edu/%7Esjohnson/wvlib/demo/"> Wavelet
Library Demo at South Carolina </a>      </li>
          <li><a href="http://cm.bell-labs.com/cm/ms/who/wim/cascade/"> Bell
Labs: Wim Sweldens' Wavelet Cascade Applet </a>      </li>
href="http://bigwww.epfl.ch/demo/fractsplines/demoprep.html"> Biomedical
Group at EPFL - Fractional Splines Demo </a>      </li>
          <li><a href="http://www.ics.forth.gr/%7Eliapis/demo/"> Texture
Classification  Demo </a> </li>
          <li><a href="http://www-db.stanford.edu/IMAGE/">  SIMPLIcity Content
Based Image Retrieval - Search </a> </li>
Wavelet-Based View Synthesis </a>      </li>
href="http://telin.rug.ac.be/%7Efrooms/links/wavelets.html"> More links...
          </a><a> </a></li>
href="http://www.google.com/search?q=Wavelet+Press+Releases"> A measure
of Wavelet popularity?</a></li>
  <h3>Sample Project Topics (from Fall'01) - Organized by areas </h3>
    <li> Coding                   
  <li>  Implementation of a Pyramidal Image Coder  </li>
          <li> Compression of finite-length discrete-time signals using flexible
adaptive wavelet packets&lt; </li>
          <li> Wavelet Descriptors for Planar Curves  </li>
          <li> Sinusoidal Modeling of Audio Signals Using Frame-Based Perceptually
Weighted Matching Pursuits     </li>
          <li>  Low Complexity Motion Estimation Algorithm for Long-term
Memory  Motion Compensation Using Hierarchical Motion Estimation      </li>
          <li>  Global/Local Motion Compensation for 3D Video Coding Based
on Lifting Techniques      </li>
      <li> Classification/Recognition                   
  <li> Shift Invariant Texture Classification by Using Wavelet Frame </li>
          <li> Texture Feature Extraction with Non-Separable Wavelet Transforms
          <li>  Comparison of Two Wavelet-Based Image Watermarking Techniques
          <li> Application of Wavelet Transform in Analysis of Fractal Signals
          <li>  Human-Face Detection and Location in Color Images Using Wavelet
      Decomposition    </li>
          <li>  Music/Speech Classifier using Wavelets       </li>
          <li> Wavelet Decomposition for the Analysis of Heart Rate Variability
          <li> Wavelet-based fMRI dynamic activation detection </li>
          <li> Wavelet analysis of evoked potentials </li>
          <li>  Detection of Microcalcifications in Mammograms using Wavelet
Transforms     </li>
          <li>  Wavelet-based Tone Classification for Thai      </li>
      <li> Denoising                     
  <li> Comparison of Denoising via Block Weiner Filtering in Wavelet Domain
with Existing Ad-hoc Linear and Non-linear Denoising Techniques  </li>
          <li> Wavelet-domain filtering of data with Poisson noise  </li>
          <li> Contrast Enhancement and De-noising using Wavelets </li>
          <li>  Wavelet Denoising Applied to Time Delay Estimation  </li>
          <li> Comparison of image denoising using Wavelet Shrinkage vs.
MMSE  using an exponential decay autocorrelation model </li>
          <li> Threshold Denoising Effects on Covariance Matrices  </li>
          <li>  Comparing Performance of Different  Wavelet De-noising algorithms
with Basic Noise Removal  Techniques  </li>
          <li> Information Driven Denosing of MEG data in the Wavelets Domain
          <li>  Two Methods for Image Enhancement<a
href="./ChongKim.html">  </a>      </li>
      <li> Watermarking/Halftoning                     
  <li> Introduction of IWT to wavelet-based watermarking and its effect
on performance </li>
          <li> Inverse Halftoning using Wavelets      </li>
      <li> Communications                     
  <li> Wavelets Based MC-CDMA System </li>
          <li>    MMSE Estimation Multi-user detection for CDMA System based
on Wavelet Transform<a href="EE596_Wu.htm">  </a>      </li>
<li>  &copy;1996-2006 Antonio Ortega.&nbsp;</li>
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Last modified: Wed Aug 30 09:50:32 PDT 2006
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Revision as of 16:01, 19 August 2008