Computer Vision and Image Processing - Fundamentals and Applications

Overview

PRE-REQUISITES: Basic co-ordinate geometry, matrix algebra, linear algebra and random process.

INTENDED AUDIENCE: UG, PG and Ph.D. students

INDUSTRIES APPLICABLE TO: The software industries that develop computer visions apps would be benefitted from this course.

COURSE OUTLINE: The intent of this course is to familiarize the students to explain the fundamental concepts/issues of Computer Vision and Image Processing, and major approaches that address them. Even though Computer Vision is being used for many practical applications today, it is still not a "solved" problem. Hence, definitive solutions are available only rarely.

Syllabus

Computer Vision and Image Processing – Fundamentals and Applications [Intro Video].
Lec 1 : Introduction to Computer Vision.
Lec 2 : Introduction to Digital Image Processing.
Lec 3 : Image Formation: Radiometry.
Lec 4 : Shape From Shading.
Lec 5 : Image Formation: Geometric Camera Models - I.
Lec 6 : Image Formation: Geometric Camera Model - II.
Lec 7 : Image Formation: Geometric Camera Model - III.
Lec 8 : Image Formation in a Stereo Vision Setup.
Lec 9 : Image Reconstruction from a Series of Projections.
Lec 10 : Image Reconstruction from a Series of Projections.
Lec 11 : Image Transforms - I.
Lec 12 : Image Transforms - II.
Lec 13 : Image Transforms - III.
Lec 14 : Image Transforms - IV.
Lec 15 : Image Enhancement..
Lec 16 : Image Filtering-I.
Lec 17 : Image Filtering-II.
Lec 18 : Colour Image Processing - I.
Lec 19 : Colour Image Processing - II.
Lec 20 : Image Segmentation.
Lec 21 : Image Features and Edge Detection.
Lec 22 : Edge Detection.
Lec 23 : Hough Transform.
Lec 24 : Image Texture Analysis - I.
Lec 25 : Image Texture Analysis - II.
Lec 26 : Object Boundary and Shape Representations - I.
Lec 27 : Object Boundary and Shape Representations - II.
Lec 28 : Interest Point Detectors.
Lec 29 : Image Features - HOG and SIFT.
Lec 30 : Introduction to Machine Learning - I.
Lec 31 : Introduction to Machine Learning - II.
Lec 32 : Introduction to Machine Learning - III.
Lec 33 : Introduction to Machine Learning - IV.
Lec 34 : Introduction to Machine Learning - V.
Lec 35 : Artificial Neural Network for Pattern Classification - I.
Lec 36 : Artificial Neural Network for Pattern Classification - II.
Lec 37 : Introduction to Deep Learning.
Lec 38 : Gesture Recognition.
Lec 39 : Background Modelling and Motion Estimation.
Lec 40 : Object Tracking.
Lec 41 : Programming Examples.