This unit covers fundamental techniques in image processing. Topics include image representation and enhancement, thresholding, image algebra, neighbourhood operations on images, Fourier methods, edge detection, feature extraction and representation, shape, texture, segmentation, classification, restoration, image compression, and colour and multiband image processing.
2 hrs lectures/wk, 1 hr laboratory/wk, 1 hr tutorial/wk
and up to an additional 8 hours in some weeks for completing lab and project work, private study and revision.
CSE3314
FIT2004 (or CSE2304) and FIT2014 (or CSE2303)
Sid Ray
Loke Kar Seng
Loke Kar Seng
Examination (3 hours): 70%; In-semester assessment: 30%
| Assessment Task | Value | Due Date |
|---|---|---|
| Assignment 1 | 10% | Week 6, Thursday |
| Assignment 2 | 20% | Week 12, Thursday |
| Examination 1 | 70% | To be advised |
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Please check with your lecturer before purchasing any Required Resources. Prescribed texts are available for you to borrow in the library, and prescribed software is available in student labs.
Software
These are freely available from:
JDK - http://www.oracle.com/technetwork/java/javase/downloads/jdk6-jsp-136632.html
Netbeans -http://netbeans.org/
Prescribed texts are available for you to borrow in the library.
Gonzalez and Woods. (2001). Digital Image Processing. (2nd) Prentice-Hall.
Writing tools
| Week | Activities | Assessment |
|---|---|---|
| 0 | No formal assessment or activities are undertaken in week 0 | |
| 1 | Image Processing Fundamentals; Arithmetic Operations on Images | |
| 2 | Introduction to Image Enhancement Techniques; Linear Stretching | |
| 3 | Spatial Filtering Methods; Sharpening Filters | |
| 4 | Histogram Equalization; Line and Edge Detection | |
| 5 | Image Thresholding; Image Segmentation Methods | |
| 6 | Clustering-Based Image Segmentation; Region Growing; Splitting and Merging | Assignment 1 due Thursday this week |
| 7 | Texture Characterization; Co-occurrence Matrices; Entropy-Based Thresholding | |
| 8 | Image Filtering in Frequency Domain | |
| 9 | Image Data Compression | |
| 10 | Image Representation and Description | |
| 11 | Image Recognition I | |
| 12 | Image Recognition II | Assignment 2 due Thursday this week |
| SWOT VAC | No formal assessment is undertaken SWOT VAC | |
| Examination period | LINK to Assessment Policy: http://policy.monash.edu.au/policy-bank/ academic/education/assessment/ assessment-in-coursework-policy.html |
*Unit Schedule details will be maintained and communicated to you via your MUSO (Blackboard or Moodle) learning system.
Faculty Policy - Unit Assessment Hurdles (http://www.infotech.monash.edu.au/resources/staff/edgov/policies/assessment-examinations/unit-assessment-hurdles.html)
1. Satisfactory implementation according to the requirements of the assignment.
2. Structure, modularity and efficiency of code
3. Ease of use of program user interface
4. Evidence of testing
1. Satisfactory implementation according to the requirements of the assignment.
2. Structure, modularity and efficiency of code
3. Ease of use of program user interface
4. Evidence of testing
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Submission must be made by the due date otherwise penalties will be enforced.
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READING LIST
Recommended Reading
R. C. Gonzalez and R. E. Woods, Digital Image Processing using MATLAB, Prentice Hall, 2004. A. K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, 1990. W. Niblack, An Introduction to Digital Image Processing, PHI, 1986. D. H. Ballard and C. M. Brown, Computer Vision, Prentice-Hall, 1982. M. D. Levine, Vision in Man and Machine, McGraw?-Hill, 1995. R. Jain, R. Kasturi, and B. G. Schunck, Machine Vision, McGraw?-Hill, 1995. C. Watkins, A. Sadun, and S. S. Marenka, Modern Image Processing: Warping, Morphing, and Classical Techniques, Academic Press, 1993. H. R. Myer and A. R. Weeks, The Pocket Handbook of Image Processing Algorithms in C, Prentice-Hall, 1993. S. E. Umbaugh, Computer Vision and Image Processing: a practical approach using CVIPtools, Prentice Hall PTR, 1998.