EE610-Digital Image Processing(2019-20)

Course Name: Digital Image Processing 

Course Code:   EE610

Credits: 6

Course Instructor: S.N. Merchant

Course Type:  Institute Elective

Prerequisites:  
A course on Signals and System, and Random Signal Analysis is recommended by the professor. However, he does not strictly enforce these as a formal pre-requisite.

Course Content:  
Intensity Transformations and Spatial filtering: Histogram equalization, Histogram Matching
Discrete Fourier transform
Smoothing and Sharpening in Spatial and Frequency Domain: Ideal, Butterworth and Gaussian Low/High pass filters
Fuzzy sets
Morphological Image processing
Image compression
Image Segmentation: edge based, MH + Canny, Region based + thresholding
Image Restoration: CLS filtering, Weiner filtering, Inverse filtering

Other Topics:
Weber law, Brightness Adaptation, Log transform, Power law transform, Spatial Filter Mask, Convolution operation

Useful Books:
Digital Image Processing by Gonzalez (3rd edition) is faithfully followed by the professor

Online Study Material:

Lectures:
Professor insisted on good attendance. However, 80% attendance policy was not strictly enforced. Instead of biometrics, paper based attendance was followed. Tutorial sessions were conducted before midsems and endsems.
He used blackboards and slides during the lecture. Most of the content was explained using the blackboard. Slides in itself were insufficient. Course content was comprehensive and easy to follow of lectures were regularly attended.

Assignments/Tutorials
Assignments were based on practical application of what was taught in the lectures. Two graded assignments worth 10 points each, were given.

Exams
Two Assignments + One Course Project + Midsem + Endsem

Advanced Follow up Courses:
EE702: Computer Vision is a possible follow up course

Pro-tips:
Regularly attend the lectures. A good understanding of random variables and probability theory is helpful.

Personal Comments:

Respondent: Nitish Ujjwal