YoVDO

Computer Vision and Image Processing - Fundamentals and Applications

Offered By: NPTEL via YouTube

Tags

Computer Vision Courses Digital Image Processing Courses Image Processing Courses Image Segmentation Courses Image Filtering Courses Image Enhancement Courses Edge Detection Courses Image Reconstruction Courses

Course Description

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.


Taught by

NPTEL IIT Guwahati

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