1. To get familiarized with MATLAB image processing toolbox
2. To study various pixel-wise transformations and histogram equalization
3. To implement de-noising and de-blurring algorithms in spatial domain
4. To implement de-noising and de-blurring algorithms in frequency domain
5. Spatial and frequency domain filtering for color images
6. Image segmentation using global thresholding
7. K-means clustering based segmentation
8. Classifier design with Neural Network
9. Mini-project

To perform basic pre-processing tasks like: histogram equalization and filtering

To develop segmentation and classification algorithms

To design classifiers

To solve a real-life problem (mini-project)

After completion of this course, a student will be able:
1) To design basic image filters
2) To design and analyze efficient segmentation schemes
3) To design and analyze object classifiers
4) To analyze a real-life problem (e.g. face recognition) and find suitable solutions
5) To carry out research in the field of computer vision.

David A. Forsyth and Jean Ponce, Computer Vision: A Modern Approach, Pearson Education , 2012

R C Gonzalez and R E Woods, Digital Image Processing, Pearson Education , 2018