Shofwatul 'Uyun | Official Website |

TIF3212-Pattern Recognition

Lecturer : Shofwatul ‘Uyun

Email : shofwatul.uyun@uin-suka.ac.id; shofwa_uyun@yahoo.com; shofwa.uyun@gmail.com

Office: the second floor of saintek building
Office Hours: Tuesday 10:30-12:00 am, Thursday 1-2 pm, or by appointment

Website : shofwatuluyun.com
Staff Mailing List:
dosentif-uinsuka@yahoogroups.com
Thursday (09-11:50 am) Location : 409;

Prerequisites : artificial Intelligence

Grading and grading policy

The final grade will be based on :

  • Midterm exam  (25%)
  • Final exam (25%).
  • Project/Group assignments (15%)
  • Paper presentation (15%)
  • Final Project (20%)

References :

  1. Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern classification. 2nd ed. New York, NY: Wiley, 2001. ISBN: 0471056693.
  2. Mallot, Hanspeter A. Computational Vision: Information Processing in Perception and Visual Behavior. Translated by John S. Allen. Cambridge, MA: MIT Press, 2000. ISBN: 0262133814.
  3. Forsyth, David A., and Jean Ponce. Computer Vision: a Modern Approach. Upper Saddle River, NJ: Prentice Hall, 2003. ISBN: 0130851981.
  4. Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction: with 200 full-color illustrations. New York, NY: Springer, c2001. ISBN: 0387952845.

Topics :

  1. Overview of problems of machine vision and pattern classification
  2. Image formation and processing
  3. Feature extraction from images
  4. Biological object recognition
  5. Bayesian Decision Theory
  6. Clustering
  7. Classification

Materials of pattern recognition :

  1. Overview [pdf]
  2. Feature Extraction [pdf]
  3. Classification Method (Supervised) [pdf]
  4. Learning Vector Quantification [winzip]
  5. Support vector Machine-1 [winzip]
  6. Support vector Machine-2 [winzip]
  7. Fuzzy C-Means Clustering [pdf]
  8. Example of Fuzzy C-Means Clustering [pdf]
  9. Substractive Clustering [pdf]
  10. Example  of Substractive Clustering [pdf]

 

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