Image Processing
Spring 2015
Intended Learning Outcomes (ILOs)
On completion of this module students should be able to:
Knowledge and understanding
1. Describe the principles of digital image representation and encoding by the underlying mathematical principles and algorithms for image processing.
2. Recognize the fundamental techniques for image processing and manipulation.
Subject-specific Cognitive skills
3. Critically assess the features and limitations of image processing techniques so as to inform selection of the most appropriate processing steps for a range of different applications.
Subject-specific Practical skills
4. Apply scientific methods to evaluate image processing techniques and their applicability to different problem domains. [C1]
5. Develop a working knowledge of image processing algorithms and libraries. [C3]
6. Implement learnt algorithms and develop simple image processing functions in languages such as Java and/or by using library or tools like MATLAB.
Transferable skills
7. Develop suitable decision making strategies and to be able to apply knowledge of image processing techniques to different problem domains.
Knowledge and understanding
1. Describe the principles of digital image representation and encoding by the underlying mathematical principles and algorithms for image processing.
2. Recognize the fundamental techniques for image processing and manipulation.
Subject-specific Cognitive skills
3. Critically assess the features and limitations of image processing techniques so as to inform selection of the most appropriate processing steps for a range of different applications.
Subject-specific Practical skills
4. Apply scientific methods to evaluate image processing techniques and their applicability to different problem domains. [C1]
5. Develop a working knowledge of image processing algorithms and libraries. [C3]
6. Implement learnt algorithms and develop simple image processing functions in languages such as Java and/or by using library or tools like MATLAB.
Transferable skills
7. Develop suitable decision making strategies and to be able to apply knowledge of image processing techniques to different problem domains.
Course Materials
28 Sept. 2015: Lecture#01: Basic concepts for digital images (e.g. image acquisition and representation, pixel, grey level, histograms, frames, digital geometry, image coding and compression, video coding and compression)
05 Oct. 2015: Lecture#02: Image enhancement – point operators, histogram modification, filtering
12 Oct. 2015: Lecture#03: Image features analysis, classification and synthesis – edges, corners, lines, curves, regions, textures
19 Oct. 2015: Lecture#04: Image noise and its retrieval
26 Oct. 2015: Lecture#05: Feature detection techniques and tracking methods
02 Nov. 2015: Lecture#06: The use of digital morphology
09 Nov. 2015: Lecture#07: Methods in Grey-level and color image segmentation
16 Nov. 2015: Lecture#08: Mid-term Examination
23 Nov. 2015: Lecture#09: Recognition – statistical classification, decision trees, geometric model matching
30 Nov. 2015: Lecture#10: Overview of contemporary application areas (e.g. biometrics, e.g. face, fingerprint, iris and gait recognition)
07 Dec. 2015: Lecture#11: Image processing using available commercial software
14 Dec. 2015: Lecture#12: Revision
05 Oct. 2015: Lecture#02: Image enhancement – point operators, histogram modification, filtering
12 Oct. 2015: Lecture#03: Image features analysis, classification and synthesis – edges, corners, lines, curves, regions, textures
19 Oct. 2015: Lecture#04: Image noise and its retrieval
26 Oct. 2015: Lecture#05: Feature detection techniques and tracking methods
02 Nov. 2015: Lecture#06: The use of digital morphology
09 Nov. 2015: Lecture#07: Methods in Grey-level and color image segmentation
16 Nov. 2015: Lecture#08: Mid-term Examination
23 Nov. 2015: Lecture#09: Recognition – statistical classification, decision trees, geometric model matching
30 Nov. 2015: Lecture#10: Overview of contemporary application areas (e.g. biometrics, e.g. face, fingerprint, iris and gait recognition)
07 Dec. 2015: Lecture#11: Image processing using available commercial software
14 Dec. 2015: Lecture#12: Revision
Reference Books
- Gonzalez, R. and Woods, R., “Digital Image Processing”, 3rd Int. Edition, Prentice Hall, ISBN: 013505267-X (2008).
- Digital Image Processing Using MATLAB, 2nd ed. , Rafael C. Gonzalez, Richard E. Woods, & Steven L. Eddins, ISBN: 0982085400, Gatesmark Publishing; 2nd edition (2009)
- An Introduction to Digital Image Processing with MATLAB, Alasdair McAndrew, ISBN: 0534400116, Brooks/Cole (7 May 2004)
- Matlab for Beginners: A Gentle Approach, Peter I. Kattan, ISBN: 1438203098, published by: CreateSpace (April 11, 2008)
- MATLAB Primer, Eighth Edition, Timothy A Davis, ISBN: 1439828628, published by: CRC Press; 8th edition (August 18, 2010)
Assessment
- %25 Two in-lab tests
- %25 Mid-term exam
- %50 Final written exam
Instructor
Essam Rashed, Ph.D.