Computer Vision and Image Processing - Fundamentals and Applications (English)

   Sunday, March 31, 2024   

Starting a new Lecture Notes Series on Computer Vision and Image Processing - Fundamentals and Applications

Youtube Lecture Playlist Credits 

Channel Name: NPTEL IIT Guwahati

So Let Us Start to This Journey of Learning 

Computer Vision and Image Processing - Fundamentals and Applications By Lecture Notes together!

Lecture 1: Computer Vision and Image Processing – Fundamentals and Applications

Lecture 2: Lec 1 : Introduction to Computer Vision

Lecture 3: Lec 2 : Introduction to Digital Image Processing

Lecture 4: Lec 3 : Image Formation: Radiometry

Lecture 5: Lec 4 : Shape From Shading

Lecture 6: Lec 5 : Image Formation: Geometric Camera Models - I

Lecture 7: Lec 6 : Image Formation: Geometric Camera Model - II

Lecture 8: Lec 7 : Image Formation: Geometric Camera Model - III

Lecture 9: Lec 8 : Image Formation in a Stereo Vision Setup

Lecture 10: Lec 9 : Image Reconstruction from a Series of Projections

Lecture 11: Lec 10 : Image Reconstruction from a Series of Projections

Lecture 12: Lec 11 : Image Transforms - I

Lecture 13: Lec 12 : Image Transforms - II

Lecture 14: Lec 13 : Image Transforms - III

Lecture 15: Lec 14 : Image Transforms - IV

Lecture 16: Lec 15 : Image Enhancement.

Lecture 17: Lec 16 : Image Filtering-I

Lecture 18: Lec 17 : Image Filtering-II

Lecture 19: Lec 18 : Colour Image Processing - I

Lecture 20: Lec 19 : Colour Image Processing - II

Lecture 21: Lec 20 : Image Segmentation

Lecture 22: Lec 21 : Image Features and Edge Detection

Lecture 23: Lec 22 : Edge Detection

Lecture 24: Lec 23 : Hough Transform

Lecture 25: Lec 24 : Image Texture Analysis - I

Lecture 26: Lec 25 : Image Texture Analysis - II

Lecture 27: Lec 26 : Object Boundary and Shape Representations - I

Lecture 28: Lec 27 : Object Boundary and Shape Representations - II

Lecture 29: Lec 28 : Interest Point Detectors

Lecture 30: Lec 29 : Image Features - HOG and SIFT

Lecture 31: Lec 30 : Introduction to Machine Learning - I

Lecture 32: Lec 31 : Introduction to Machine Learning - II

Lecture 33: Lec 32 : Introduction to Machine Learning - III

Lecture 34: Lec 33 : Introduction to Machine Learning - IV

Lecture 35: Lec 34 : Introduction to Machine Learning - V

Lecture 36: Lec 35 : Artificial Neural Network for Pattern Classification - I

Lecture 37: Lec 36 : Artificial Neural Network for Pattern Classification - II

Lecture 38: Lec 37 : Introduction to Deep Learning

Lecture 39: Lec 38 : Gesture Recognition

Lecture 40: Lec 39 : Background Modelling and Motion Estimation

Lecture 41: Lec 40 : Object Tracking

Lecture 42: Lec 41 : Programming Examples