(NOTE: All the content was found on the Internet.)
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.
Image Fundamentals. Image Enhancement and Restoration. Image Analysis. Decision Theory and Statistical Estimation. Classification and Clustering. Dimensionality Reduction.
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.
- 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.
- 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.