CMP 165: Image Processing  (Graduate Course, First Semester)

OBJECTIVES: Present image and video processing concepts, and overview current approaches for image and video processing problems (grayscale and color),. This course has theoretical and practical components, and provides opportunities for students to apply research results to practical problems in science and technology (e.g. Medical Image and Video Processing, and Multimedia Data Processing).

COURSE OUTLINE:

Fundamentals: Visual Perception, Image/Video Formation and Acquisition, Examples and Problems

Image Transforms: Unitary Transforms, Algorithms, Examples and Problems

Image Enhancement: Image Filtering in the Spatial and Spectral Domains (noise suppression, detail enhancement, adaptive filtering), Color Image Filtering and Enhancement, Examples and Problems

Image Restoration: Image Degradation Models, Current Image Restoration Approaches, Examples and Problems

Image and Video Compression: Fundamental Concepts of  Image and Video Compression, Current Techniques and Standards, Overview of IP Based Video Communications, Examples and Problems

Image Analysis and Interpretation: Image Segmentation (Approaches and Methods),  Image Representation and Description (Shape, Color, Texture, Spatial Relationship, other Features), Concepts of Pattern Recognition and Machine Vision, Examples and Problems  

BIBLIOGRAPHY:

1.      "Fundamentals of Digital Image Processing", A.K.Jain , Prentice-Hall, 1989.

2.      "Digital Image Processing", R. C. Gonzalez e R. E. Woods, Addison-Wesley 2003.

3.       "Digital Image Processing Algorithms", I. Pitas, Prentice Hall 1993.

4.      "Image Processing, Analysis and Machine Vision", M. Sonka, V. Hlavac e R. Boyle, PWS Publishing, 1999. 2nd ed.

5.  "Video Processing and Communications", Y. Wang; J. Ostermann;Y. Zhang. Prentice Hall, 2002.

6.      "Introduction to Statistical Pattern Recognition", K. Fukunaga, Academic Press 1990.

7.   "Computer Vision", L. Shapiro e G. C. Stockman. Prentice-Hall, 2001.

8.   "Computer Vision: Algorithms and Applications", Richard Szeliski, Springer, 2010.

9. Selected papers.

Hours/Week: 4

Credits: 4