Beginner's Guide to Learn Computer Vision with Python

Learn fundamentals of Computer Vision with state of art image and video processing Algorithms.

Course Structure

Introduction:

• Introduction

• Real world Applications

Popular Computer Vision Techniques:

• Image Segmentation

• Demo - Image Segmentation

• Edge Detection

• Demo - Edge Detection

• Feature Extraction

• Demo - Feature Extraction

• Application of CV techniques

Object Detection, Tracking and Classification:

• Object Detection

• Object Tracking

• Image Classification

• Demo: Image Classification

• Challenges in CV

Deep Learning for Computer Vision:

• What is Deep Learning?

• Convolutional Neural Network (CNN)

• Demo - CNN

• Transfer Learning

• Benefits of Deep Learning in CV

Image Recognition:

• Face Detection and Recognition

• Demo - Face Detection

• Optical Character Recognition (OCR)

• Demo - OCR

Advanced Techniques - Panorama Creation:

• Image Registration

• Image Stitching

• Demo - Image Stitching

Motion Analysis:

• Motion Analysis

• Video Processing

• Background Subtraction

• Demo: Background Subtraction

Realtime Video Processing:

• Realtime Video Processing

• Demo - Object Detection

• Application in Robotics

Requirements

• Basics knowledge of computer programming

• Familiar with python programming language and any python IDE (like PyCharm)

• Windows / Linux / Mac OS X Machine with Internet

Content team

• Expert: Arunkumar Krishnan

• Production: Vishnu Sakthivel, Visshwa Balasubramanian

What you will learn?

• Learn fundamentals of Computer Vision

• Understand state of art image and video processing Algorithms in CV

• Understand application of Deep Learning Models in the CV

• Learn to implement CV algorithms with OpenCV python library