ashiishme/Deep-Learning-for-Computer-Vision

Deep-Learning-for-Computer-Vision

This is based on Course taught at nptel by professor Vineeth N Balasubramanian from IIT Hydreabad.

Pre-Requistite :

• Familar with Probaility, Statistics
  • MIT Course on probability and Statistics.
• Linear Algebra
  • Gilbert Strang Lectures.
• A Course on Deep Learning.
  • AndrewNG Lectures.

Week 0

• Preliminary Asignment ( Python Basics, List Comprehentions and Numpy ) - Assignment 0

Week 1

• Contrast Stretching
• Gray Scaling
• Image as Grid of Pixels
• Min-Max Stretching
• OpenCV Introduction

Week 1 LECTURE SLIDES

Week 2

• Linear Filtering
• Convolution using filter
• Sobel Filter(Vertical and Horizontal )
• Laplacian Filter

Week 2 LECTURE SLIDES

Week 3 and Week 4

• Canny Edge Detector(OpenCV)
• Hough Transform(OpenCV)
• Estimating Affine Transformation
• Point match using RANSAC(Scratch Implementation)
• Harris Corner Detector (OpenCV)
• CNN in Pytorch

Week 3 LECTURE SLIDES

Week 4 LECTURE SLIDES

Books :

• Computer Vision: Algorithms and Applications