EE 610 Image Processing ( 2018-2019 )

Instructor: Amit Sethi

Course Name: Image Processing ( EE610 )

Course Type: Theory ( Elective )

Credits: 6

Prerequisites: None

Course Content: 

Introduction to imaging File, Intensity Transform and Spatial Filtering, Frequency Filtering, Image Restoration, Image Interpolation, Colour Image Processing, Wavelet analysis of images, Image compression, Morphological operations, image segmentation, image representation, Object recognition

Books/Other Resources:

Digital Image Processing – By Gonzalez & Woods

The book was followed to the teeth.

About Lectures:

Regular attendance was taken in the lectures, but it wasn't graded. At the start of every topic, the prof would lay the basic foundation (the math, associated notations and concepts) on the black board and then later switch to slides to show the results on images and summarize the theory. The lectures were generally easy to follow and not very mathematically involved. The prof was extremely approachable in terms of doubts in and out of the lectures.

Course Structure:

2 graded coding based assignments: implementation of a GUI of a basic image editor and Image de-blurring(ML and non-ML based approaches). There was a final project as well where there was complete independence of choice of topic (making it fun).

Midsem(27), endsem(38), 2 assignments(10 each),  project(20): Total - 105. Grading was done out of 100. This was later tweaked a little bit and the assignments and project were given more weightage. 

Personal Comments:

Overall, a lot of effort was required to complete the assignments, for someone like me, it made me more efficient with coding, as a side benefit.

Pro tips:

For the assignments, start early. Since you are coding you are bound to have bugs which might take hours to eliminate. Attending lectures, minimizes the effort that you have to put at your room. Slides were enough for exams, though the book was comprehensive. The prof also covered previous year endsem and midsem papers in class. Enjoy the course!

Advanced follow-up courses ( Courses that can be taken after this ):
ML + IP based courses

Respondent: Ananay Garg