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EE7403 - Image Analysis & Pattern Recognition

Learning Objective:

Understanding the fundamental yet critical methods of automatic image analysis and pattern recognition by computers/machines. Acquiring foundations for further topics such as computer vision, machine learning, data mining and artificial intelligence.

Content:

Image Fundamentals. Image Enhancement and Restoration. Image Analysis. Decision Theory and Statistical Estimation. Classification and Clustering. Dimensionality Reduction.

Learning Outcome:

Students of this course will be trained to have the ability of utilizing mathematics to solving real-world problems in the area of image analysis and pattern recognition. Students will learn solid fundamentals in image processing and analysis, statistical estimation, machine learning, pattern recognition and classification.

Textbooks:

• R. C. Gonzalez and R. E. Woods, "Digital Image Processing, 3rd Edition," Pearson Prentice Hall, 2008.
• R. O. Duda, P. E. Hart, and D. G. Stork, "Pattern Classification, 2nd Edition," Wiley Inter-science, 2001.

References:

• R. C. Gonzalez, R. E. Woods, and S. L. Eddins, "Digital Image Processing Using Matlab," Pearson Prentice Hall, 2004.
• C. M. Bishop, "Pattern Recognition and Machine Learning, 2nd Edition," Springer, 2011.

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