COL783: Digital Image Analysis

I Semester 2024-25

Course Description

Content: Digital image fundamentals; image enhancement in spatial domain: gray level transformation, histogram processing, spatial filters; image transforms: Fourier transform and their properties, fast Fourier transform, other transforms; image enhancement in frequency domain; color image processing; image warping and restoration; image compression; image segmentation: edge detection, Hough transform, region based segmentation; morphological operators; representation and description; features based matching and Bayes classification; introduction to some computer vision techniques: imaging geometry, shape from shading, optical flow. Laboratory exercises will emphasize development and evaluation of image processing methods.

Textbook:

Prerequisites: COL106, ELL205, or equivalent.

Overlaps with: ELL715.

Announcements: All announcements will be made on the Announcements forum of the course Moodle site. It is your responsibility to check it regularly.

Lectures

Slides will be posted here as the semester proceeds.

Monday Thursday
22–26 July 1. Introduction, digital image fundamentals 2. Sampling and quantization, basic image operations
29 July–2 Aug 3. Basic image operations, intensity transformations 4. Histogram processing, spatial filtering
5–9 Aug 5. Spatial filtering, smoothing filters
12–16 Aug 6. Sharpening filters, colour image processing 7. Colour image processing
19–23 Aug 8. The Fourier transform 9. More about the Fourier transform
26–30 Aug 10. Filtering in the frequency domain
11. Image restoration, denoising
2–6 Sept 12. Optimum notch filtering, deconvolution (no slides)
9–13 Sept 13. Deconvolution, reconstruction from projections 14. Tomographic reconstruction
16–20 Sept 15. Image transforms
23–27 Sept 16. Image transforms, multiresolution representations 17. Wavelets
30 Sept–4 Oct 18. Image compression 19. Compressing spatial redundancy
14–18 Oct 20. Compression wrap-up, morphological operations 21. Opening and closing, hit-or-miss transform
21–25 Oct 22. Morphological reconstruction, grayscale morphology 23. Edge detection
28 Oct–1 Nov 24. Region segmentation
4–8 Nov 25. Advanced segmentation techniques 26. Watershed segmentation, feature extraction
11–15 Nov 27. Features from image data 28. Scale-invariant features

Assignments

All dates of future assignments are tentative and subject to change.

  1. Assignment 1: 8 Aug – 29 Aug
  2. Assignment 2: 9 Sep – 30 Sep
  3. Assignment 3: 2 Oct – 25 Oct
  4. Assignment 4: 23 Oct – 11 Nov

Assignments can be done individually or in groups of 2. You must implement all tasks yourself by directly working with the pixel array, unless otherwise specified in the assignment description.

Policies

Evaluation:

The following grading breakdown is tentative and subject to change.

Grading: Following institute policy, a minimum of 80% marks are required for an A grade, and minimum 30% marks for D.

Late policy: Homework assignments are due at 11:59pm on the due date. You are allowed a total of 4 late days across all the assignments. After the total allowed late days have been used up, a 25% penalty will be applied for each extra day a submission is late.

Audit policy: To earn an audit pass, you must score at least 40% in the course total, and at least 20% in each assignment and each exam.

Attendance policy: Attendance lower than 75% may result in a one-grade penalty (e.g. A to A–, or A– to B).

Academic dishonesty: Adapted from SAK’s general guidelines for students:

Remember that you have signed an honour code before getting admitted to IIT Delhi. Check that out on the inside cover page of your prospectus. Here is a non-exhaustive list of dishonest behaviour in assignments, based on past experience.

Please note the following points in addition.