National Taiwan Normal University
Department of Computer Science & Information Engineering

Course Title: CSC0030: Pattern Recognition       Syllabus

Credits: 3 (Spring, 2008)

Classes: 3:10-6:00 Thursday at Room S402

Instructor: Dr. Chaur-Chin Chen, http://www.cs.nthu.edu.tw/~cchen

Tel/E-mail: (03) 573-1078, cchen@cs.nthu.edu.tw at EECS Rm 539

Prerequisites: Linear Algebra, Probability Theory, Data Structure, C/C++, [Matlab, Image Processing]

Contents:
1. Introduction + Foundation of Mathematics (Chapter 1)    
    Introduction to Image Processing   Print images    raw2ps.c    Print images by Matlab   
    Face and Fingerprint Recognition     Preparing a technical paper     ICME paper     ICPR paper
    Texture discrimination
2. Classifiers Based on Bayesian Decision Theory (Chapter 2)     A Simulation Program
      Matlab Program q01     Two Rayleigh Density Functions
3. Linear Classifiers (Chapter 3)     Linear and Nonlinear Classifiers     Simulate MVN distribution
4. Nonlinear Classifiers (Chapter 4)     Matlab code for Quadratic Classifiers     Matlab code for Density Estimation
5. Feature Selection (Chapter 5)
6. Feature Generation (Chapters 6,7)
7. Content-Dependent Classification (Chapter 9)
8. System Evaluation (Chapter 10)     IRIS data set     IMOX data set
    Mahalanobis Distance     Matlab for Apparent Error     Matlab for Apparent Error
    Matlab for NN Rule     Matlab for Quadratic Classifier     Matlab for LDA
9. Case Studies: Texture Discrimination and Shape Discrimination
(A) PRL papers     Four Textures     Texture Discrimination
    Pressed cork     Pebbles     Calf leather     Brodatz D77
(B) Shape Discrimination     Six Country Maps
    Canada     China     France     Italy     Taiwan     USA
10.Clustering Algorithms (Chapters 11-17)    Matlab for 2d Dendrogram    Matlab for 2d Dendrogram    Results of 55 Hepatoma Patients
11. Selected Topics from Recent Researches
(A) Microarray Image Data Analysis
(B) Biometrics for Personal Verification/Identification


Textbooks:
1. S. Theodoridis and K. Koutroumbas, Pattern Recognition, Version 3, Academic Press (2006)
References:
1. D. Hanselman and B.Littlefield, Mastering MatLab 7 (2005)
2. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2001)
3. C.M. Bishop, Pattern Recognition and Machine Learning (2006)
4. R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, John Wiley (2001)
5. A.K. Jain and R.C. Dubes, Algorithms for Clustering Data (1988+)
6. Journals: PR, PRL, IEEE Trans. (PAMI, IP, SMC, Infor. Thy., Comput., ...)
7. Proceedings: ICPR, ICIP, ICASSP, SPIE/VCIP, CVPR, ...
8. Maganizes: IEEE Computers, Communications of ACM, IEEE Spectrum, ...

Grading:
  • (30%) Assignments

  • (20%) Class Attendance
  • (20%) Midterm Exam     Final Exam
  • (30%) Technical Report (with a presentation)
    Updated on June 6, 2008