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OUTCOMES:
Upon successful completion of this course, students will be able to:
Discuss digital image fundamentals.
Apply image enhancement and restoration techniques
Use image compression and segmentation Techniques.
Represent features of images
Detect morphed image
TEXT BOOK:
1. Rafael C. Gonzales, Richard E. Woods, ―Digital Image Processing‖, Third Edition, Pearson
Education, 2010.
REFERENCES:
1. Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, ―Digital Image Processing Using
MATLAB‖, Third Edition Tata Mc Graw Hill Pvt. Ltd., 2011.
2. Anil Jain K. ―Fundamentals of Digital Image Processing‖, PHI Learning Pvt. Ltd., 2011.
3. Willliam K Pratt, ―Digital Image Processing‖, John Willey, 2002.
4. Malay K. Pakhira, ―Digital Image Processing and Pattern Recognition‖, First Edition, PHI
Learning
Pvt. Ltd., 2011.
5. http://eeweb.poly.edu/~onur/lectures/lectures.html.
6. http://www.caen.uiowa.edu/~dip/LECTURE/lecture.html
7. http://andrew.gibiansky.com/blog/image-processing/image-morphing/
OEC1704 Pattern Recognition and Artificial Intelligence L T P C
3 0 0 3
OBJECTIVES:
To understand the fundamentals of Pattern recognition.
To learn unsupervised classification
To learn to choose an appropriate feature, pattern classification algorithm for a pattern
recognition problem.
To enrich the knowledge with fuzzy systems and its applications
To enrich the knowledge with recent advances and applications using fuzzy systems.
UNIT I OVERVIEW OF PATTERN RECOGNITION 9
Discriminant functions- Supervised learning - Parametric estimation-Maximum Likelihood estimation -
Bayesian parameter estimation – Problems with Bayes Approach. Non Parametric techniques,
Perceptron Algorithm-LMSE Algorithm- Pattern classification by distance functions - minimum
distance Pattern classifier.
UNIT II UNSUPERVISED CLASSIFICATION 9
Clustering for unsupervised learning and classification, clustering concepts hierarchical clustering,
Curriculum and Syllabus | Open Electives | R 2017 | REC Page 66
