Course

Syllabus

Review of Image Processing – Image Formation, Capture and Representation, Linear Filtering, Correlation, Convolution. Visual Features and Representations: Edge Detection, Object Boundary and Shape Representations, Interest or Corner Point Detectors, Histogram of Oriented Gradients, Scale Invariant Feature Transform, Speeded up Robust Features, Saliency. Visual Matching: Bag-of-words, VLAD; RANSAC, Hough transform; Pyramid Matching; Optical Flow Basics of Artificial Neural Network for Pattern Classification, Convolutional Neural Networks Applications Medical Image Segmentation, Motion Estimation and Object Tracking, Face and Facial Expression Recognition, Image Fusion, Gesture Recognition, Remote sensing etc.

Text Books / References

1. Ralph Gonzalez, Richard Woods, Steven Eddins, “Digital Image Processing Using MATLAB”, McGraw Hill Education, 2^nd edition, 2017. 
2. Richard Szeliski ,“Computer Vision: Algorithms and Applications”, Springer, 2010. 
3. Laurene Fausett, “Fundamentals of Neural Networks Architectures, Algorithms and Applications”, Pearson, 1^st edition, 2004. 
4. Forsyth & Ponce, “Computer Vision-A Modern Approach,” Pearson Education, 2^nd edition 2015. 
5. K. Bhuyan, “Computer Vision and Image Processing: Fundamentals and Applications”, CRC Press, 1^st edition, 2019.

Pre-requisite: Nil

Course Objectives:

• To introduce the fundamentals of image formation, representation, and feature extraction techniques.
• To develop an understanding of visual matching methods and neural networks for pattern classification.
• To give an insight on applications of image processing in medical imaging, object tracking, recognition, and remote sensing.

Course Outcomes:

CO-PO Mapping: