Difference between revisions of "EE 596 Wavelets"

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<title>EE 596, Wavelets, Fall 2006</title>
 
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<h1>EE 596, Wavelets, Fall 2006</h1>
 
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<h3>Instructor</h3>
 
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  <a href="http://sipi.usc.edu/%7Eortega"> Antonio Ortega</a> 
 
  
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<p></p>
 
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<address> <a href="http://sipi.usc.edu/"> Signal and Image Processing Institute<br>
 
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  </a> <a href="http://www.usc.edu/dept/imsc/"> Integrated Media Systems
 
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Center <br>
 
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  </a> University of Southern California<br>
 
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  3740 McClintock Ave., EEB 436<br>
 
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  Los Angeles, CA 90089-2564 
 
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<p> Tel: (213) 740-2320<br>
 
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  Fax: (213) 740-4651<br>
 
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  Email: antonio DOT ortega AT sipi DOT usc DOT edu</a><br>
 
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  </p>
 
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  </address>
 
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<h3>Schedule</h3>
 
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<ul>
 
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  <li><b>Lectures</b> Tuesday and Thursday, 11:00-12:20pm, OHE 100C  </li>
 
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    <li> <b>Office hours</b> Tuesday and Thursday, 1:30-3pm, EEB 436, and by
 
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appointment.
 
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  </li>
 
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    <li> <b>Teaching Assistant</b> Ivy Tseng, hsinyits AT usc Dot edu,
 
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  </li> TA Office Hours - Mon 10am-noon, Wed 1-3pm, EEB 441.
 
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    <li> <b>Grader </b> Ozlem Kalinli
 
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- Grader office hours: F 2-4pm, EEB 427.
 
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  </li>
 
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    <li><b>Midterm 1 </b> Oct 10, 2006 (in class) </li>
 
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    <li><b>Midterm 2 </b> Nov 14, 2006 (in class) </li>
 
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    <li><b>Final</b> There will be no final exam </li>
 
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</ul>
 
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<h3>Grading</h3>
 
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<p> Each midterm will account for 30% of the grade. The remaining 40% will
 
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be based on homeworks and a project. There will be around 4 homeworks and
 
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the project will be due at the end of the semester. </p>
 
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<p> </p>
 
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<h3>DEN Access</h3>
 
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<p> This semester I will use the Blackboard system offered by
 
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DEN to post assignments and solutions, as well as grades.
 
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Please register with DEN and create your DEN profile
 
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as soon as possible by following
 
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the instructions on the
 
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<a href="http://den.usc.edu">  DEN Webpage</a>.
 
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</p>
 
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<h3>Prerequisites</h3>
 
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<p> <i> EE 483, Introduction to Digital Signal Processing</i>, or equivalent
 
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course. Please note that the course will assume some knowledge of standard
 
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DSP concepts as well as of some basic linear algebra. If you took these
 
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two  courses some time ago it would be a good idea to review some of the
 
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key material early in the semester. <br>
 
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  </p>
 
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<h3>Recommended preparation</h3>
 
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<p> <i>MATH 599, Introduction to Wavelets,</i> and <i>EE 569, Introduction
 
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to Digital Image Processing</i>. None of these courses is required.  </p>
 
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<h3>Texbooks </h3>
 
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    <li>     
 
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  <h4>Required</h4>
 
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  <ul>
 
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    <li><a href="http://lcavwww.epfl.ch/%7Evetterli/">Martin Vetterli</a>
 
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and  <a href="http://www.andrew.cmu.edu/user/jelenak/"> Jelena Kovacevic</a>,
 
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  <a href="http://www.andrew.cmu.edu/user/jelenak/Book/index.html"> <i>Wavelets and Subband
 
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Coding</i></a>, Prentice Hall, 1995.  </li>
 
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      <li> <a href="http://www.mathworks.com/wavelet.html">      Matlab
 
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Wavelet  Toolbox</a>, This toolbox is available on the student
 
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  computer
 
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accounts.  <br>
 
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  </li>
 
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  </ul>
 
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  </li>
 
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  <li>     
 
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  <h4>Recommended</h4>
 
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  <ul>
 
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  <li> <a href="http://www-math.mit.edu/%7Egs/">Gilbert Strang</a> and <a
 
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href="http://www.engr.wisc.edu/ece/faculty/nguyen_truong.html">Truong Q.
 
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Nguyen</a>,    <a
 
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href="http://saigon.ece.wisc.edu/%7Ewaveweb/Tutorials/book.html"><i>Wavelets
 
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and Filter Banks</i></a>,  Wellesley-Cambridge Press, 1995 </li>
 
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      <li><a href="http://www.systems.caltech.edu/EE/Faculty/PPV.html">P.
 
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P. Vaidyanathan</a>, <a
 
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href="http://www.prenhall.com/013/605717/60571-7.html">    <i>Multirate
 
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Systems and Filter Banks</i>    ,</a> Prentice Hall, 1993<br>
 
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      </li>
 
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  </ul>
 
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  <h3>Some useful pointers</h3>
 
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  <ul>
 
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    <li> General links                   
 
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      <ul>
 
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  <a href="http://www.mathsoft.com/wavelets.html">Wavelet page at Mathsoft</a>
 
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<li> <a href="http://www.amara.com/current/wavelet.html"> Amara's Wavelet
 
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Page</a> </li>
 
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          <li> <a href="http://www.math.wustl.edu/wavelet/"> Washington Univ.
 
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Wavlet NetCare </a>      </li>
 
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      </ul>
 
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    </li>
 
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      <li> Tutorials                   
 
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      <ul>
 
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  <li> <a href="http://www.amara.com/IEEEwave/IEEEwavelet.html">An Introduction
 
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to Wavelets </a> </li>
 
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          <li> <a
 
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href="http://engineering.rowan.edu/~polikar/WAVELETS/WTtutorial.html">
 
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The Wavelet tutorial by Robi Polikar</a>      </li>
 
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      </ul>
 
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    </li>
 
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      <li> Software                   
 
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      <ul>
 
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  <li> <a href="http://www-dsp.rice.edu/software/RWT/">Rice Wavelet Toolbox
 
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for Matlab</a> </li>
 
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          <li> <a href="http://www-stat.stanford.edu/%7Ewavelab/"> Wavelab
 
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at Stanford </a> </li>
 
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          <li> <a href="http://www.mathworks.com/products/wavelet/"> The
 
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Mathworks  Matlab Wavelet Toolbox</a> </li>
 
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          <li> <a
 
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href="http://www.math.yale.edu/pub/wavelets/software/xwpl/html/xwpl.html">X-Windows
 
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Wavelet Packet Lab </a> </li>
 
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          <li> <a href="http://www.wavelsoftware.com/">WaveL Software</a>
 
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</li>
 
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          <li> <a
 
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href="http://www.cs.ubc.ca/nest/imager/contributions/bobl/wvlt/top.html">
 
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Imager Wavelet Library at UBC </a> </li>
 
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          <li> <a
 
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href="http://www.cs.dartmouth.edu/~sp/liftpack/lift.html"> Liftpack</a>
 
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      </li>
 
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      </ul>
 
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    </li>
 
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      <li> People                   
 
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      <ul>
 
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  <li> <a href="http://iaks-www.ira.uka.de/home/klappi/people.html"> People
 
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          </a>      </li>
 
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      </ul>
 
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  </li>
 
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  </ul>
 
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  <h3> Material covered (Note: based on the material covered in Fall'04,
 
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subject to change)</h3>
 
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  <ul>
 
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    <li>
 
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<b> Weeks 1 and 2 </b>
 
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Introduction and Motivation. Signal representation using bases.
 
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Hilbert spaces. Orthogonal, bi-orthogonal basis and overcomplete
 
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expansions.
 
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Example: representing finite energy continuous signals using Haar basis.
 
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Example of construction of Haar basis
 
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      <li> <b> Week 3</b>
 
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  Bases for discrete signals. Finite and infinite dimensional spaces.
 
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      </li>
 
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      <li> <b> Week 4</b> Overcomplete expansions. Searching for the
 
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  best representation. Matching pursuits and variations. Compressed
 
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  sensing.
 
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  </li>
 
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      <li> <b> Weeks 5 and 6</b>
 
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  Multirate signal processing. Filterbanks
 
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  and discrete wavelet transforms. Time domain, frequency domain
 
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  and polyphase domain representations.
 
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      <li> <b> Week 7-8</b> 2-Channel orthogonal filterbanks. Iterated
 
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  filterbanks. Bi-orthogonal filterbanks. Lifting
 
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  factorizations. Multichannel filterbanks. Modulated filterbanks.
 
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  </li>
 
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      <li> <b> Weeks 9 and 10</b> Multidimensional wavelets. Edgelets,
 
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  bandlets, ridgelets and other extensions. Lifting for video
 
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          representation.
 
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      </li>
 
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      <li> <b> Week 11</b> Continuous time wavelets. Series
 
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  expansions of continuous signals. Haar, Sinc, Meyer, Daubechies
 
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  and Spline wavelets. Mallat algorithm. </li>
 
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      <li> <b> Weeks 12 and 13</b> Applications. Compression. Classification. Graphics.  </li>
 
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  </ul>
 
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  <h3>Projects</h3>
 
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  <ul>
 
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  <li> Project requirements:                 
 
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      <ul>
 
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        <li> Projects should be done individually.      </li>
 
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          <li> Each project must involve using the wavelet transform as a
 
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tool. A   signal is analyzed/classified, etc by computing its wavelet
 
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transform and then the required task   (e.g. denoising/classification) is
 
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performed in the transform domain.      </li>
 
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          <li> The Matlab toolbox or C libraries can be used for the project.
 
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C libraries are available at <a
 
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href="http://www.cs.dartmouth.edu/%7Egdavis/wavelet/wavelet.html"> Dartmouth
 
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          </a> and   <a
 
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href="http://math.rutgers.edu/%7Eojanen/wavekit/"> Rutgers. </a>.     
 
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        </li>
 
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          <li> Whichever method is used, the source code will have to be
 
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made    available along with the project report (only for the routines that
 
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  you write, which could call those available in matlab or C.)    </li>
 
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      </ul>
 
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  </li>
 
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      <li> Reporting requirements: a final report and a class presentation.
 
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    </li>
 
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      <li> <a href="./Projects596.html"> Project descriptions and references
 
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      </a> </li>
 
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      <li>  Test data for the projects </li>
 
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      <li> <a href="./ee596_wavelet_toolbox.html"> Software packages</a>
 
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    </li>
 
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  </ul>
 
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        <p> Examples of coding using JPEG and the latest version of JPEG 2000
 
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(provided by Christos Chrysafis, HP Labs)      </p>
 
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      <ul>
 
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        <li><a href="./Images/original.gif"> Original Image </a>      </li>
 
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          <li><a href="./Images/jpeg_40_1.gif"> JPEG Coded at 0.2 bpp (40:1
 
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compression) </a>      </li>
 
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          <li><a href="./Images/jpeg2000_40_1.gif"> JPEG2000 Coded at 0.2
 
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bpp (40:1 compression)</a>      </li>
 
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          <li><a href="./Images/jpeg_70_1.gif"> JPEG Coded at 0.11 bpp (70:1
 
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compression)</a>      </li>
 
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          <li><a href="./Images/jpeg2000_70_1.gif"> JPEG2000 Coded at 0.11
 
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bpp (70:1 compression) </a>      </li>
 
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      </ul>
 
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      <p> Demos on the web      </p>
 
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      <ul>
 
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<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>
 
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        <li><a href="http://www.math.sc.edu/%7Esjohnson/wvlib/demo/"> Wavelet
 
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Library Demo at South Carolina </a>      </li>
 
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          <li><a href="http://cm.bell-labs.com/cm/ms/who/wim/cascade/"> Bell
 
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Labs: Wim Sweldens' Wavelet Cascade Applet </a>      </li>
 
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          <li><a
 
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href="http://bigwww.epfl.ch/demo/fractsplines/demoprep.html"> Biomedical
 
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Group at EPFL - Fractional Splines Demo </a>      </li>
 
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          <li><a href="http://www.ics.forth.gr/%7Eliapis/demo/"> Texture
 
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Classification  Demo </a> </li>
 
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          <li><a href="http://www-db.stanford.edu/IMAGE/">  SIMPLIcity Content
 
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Based Image Retrieval - Search </a> </li>
 
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          <li><a
 
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href="http://www.ai.polymtl.ca/webLab/SMART/Facet1DocD/Facet1DocD.html">
 
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Wavelet-Based View Synthesis </a>      </li>
 
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          <li><a
 
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href="http://telin.rug.ac.be/%7Efrooms/links/wavelets.html"> More links...
 
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          </a><a> </a></li>
 
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          <li><a
 
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href="http://www.google.com/search?q=Wavelet+Press+Releases"> A measure
 
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of Wavelet popularity?</a></li>
 
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      </ul>
 
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  <h3>Sample Project Topics (from Fall'01) - Organized by areas </h3>
 
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  <ul>
 
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    <li> Coding                   
 
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      <ul>
 
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  <li>  Implementation of a Pyramidal Image Coder  </li>
 
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          <li> Compression of finite-length discrete-time signals using flexible
 
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adaptive wavelet packets&lt; </li>
 
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          <li> Wavelet Descriptors for Planar Curves  </li>
 
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          <li> Sinusoidal Modeling of Audio Signals Using Frame-Based Perceptually
 
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Weighted Matching Pursuits     </li>
 
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          <li>  Low Complexity Motion Estimation Algorithm for Long-term
 
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Memory  Motion Compensation Using Hierarchical Motion Estimation      </li>
 
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          <li>  Global/Local Motion Compensation for 3D Video Coding Based
 
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on Lifting Techniques      </li>
 
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      </ul>
 
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    </li>
 
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      <li> Classification/Recognition                   
 
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      <ul>
 
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  <li> Shift Invariant Texture Classification by Using Wavelet Frame </li>
 
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          <li> Texture Feature Extraction with Non-Separable Wavelet Transforms
 
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</li>
 
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          <li>  Comparison of Two Wavelet-Based Image Watermarking Techniques
 
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</li>
 
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          <li> Application of Wavelet Transform in Analysis of Fractal Signals
 
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</li>
 
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          <li>  Human-Face Detection and Location in Color Images Using Wavelet
 
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      Decomposition    </li>
 
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          <li>  Music/Speech Classifier using Wavelets       </li>
 
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          <li> Wavelet Decomposition for the Analysis of Heart Rate Variability
 
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</li>
 
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          <li> Wavelet-based fMRI dynamic activation detection </li>
 
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          <li> Wavelet analysis of evoked potentials </li>
 
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          <li>  Detection of Microcalcifications in Mammograms using Wavelet
 
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Transforms     </li>
 
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          <li>  Wavelet-based Tone Classification for Thai      </li>
 
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    </li>
 
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      </ul>
 
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    </li>
 
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      <li> Denoising                     
 
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      <ul>
 
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  <li> Comparison of Denoising via Block Weiner Filtering in Wavelet Domain
 
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with Existing Ad-hoc Linear and Non-linear Denoising Techniques  </li>
 
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          <li> Wavelet-domain filtering of data with Poisson noise  </li>
 
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          <li> Contrast Enhancement and De-noising using Wavelets </li>
 
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          <li>  Wavelet Denoising Applied to Time Delay Estimation  </li>
 
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          <li> Comparison of image denoising using Wavelet Shrinkage vs.
 
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MMSE  using an exponential decay autocorrelation model </li>
 
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          <li> Threshold Denoising Effects on Covariance Matrices  </li>
 
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          <li>  Comparing Performance of Different  Wavelet De-noising algorithms
 
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with Basic Noise Removal  Techniques  </li>
 
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          <li> Information Driven Denosing of MEG data in the Wavelets Domain
 
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  </li>
 
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          <li>  Two Methods for Image Enhancement<a
 
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href="./ChongKim.html">  </a>      </li>
 
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      </ul>
 
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    </li>
 
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      <li> Watermarking/Halftoning                     
 
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      <ul>
 
−
  <li> Introduction of IWT to wavelet-based watermarking and its effect
 
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on performance </li>
 
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          <li> Inverse Halftoning using Wavelets      </li>
 
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      </ul>
 
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    </li>
 
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      <li> Communications                     
 
−
      <ul>
 
−
  <li> Wavelets Based MC-CDMA System </li>
 
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          <li>    MMSE Estimation Multi-user detection for CDMA System based
 
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on Wavelet Transform<a href="EE596_Wu.htm">  </a>      </li>
 
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      </ul>
 
−
 
−
  </li>
 
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  </ul>
 
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−
  <h3>Homeworks</h3>
 
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  <ul>
 
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  </ul>
 
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<li>  &copy;1996-2006 Antonio Ortega.&nbsp;</li>
 
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Revision as of 00:01, 20 August 2008