**Image Processing**

*Spring 2015*

**Intended Learning Outcomes (ILOs)**

**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

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**

**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

**Reference Books**

**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**

**Assessment**

- %25 Two in-lab tests
- %25 Mid-term exam
- %50 Final written exam

**Instructor**

**Instructor**

Essam Rashed, Ph.D.