Learn Opencv For Computer Vision

Learn CV tool perform hands-on in all essential topics

What you'll learn

To study the process of Image formation and Image manipulation

To study about image processing

To impart knowledge on image enhancement techniques

To understand the significance of vision in robotics

To understand the process of integrating intelligence in Vision

Requirements

Basic knowledge of Programming

Knowledge in Python Programming Language

Basic Understanding of Computer Vision

Description

If you ever wondered what the logic and the program is behind on how a computer is interpreting the images that are being captured, then this is the correct course for you. In this course we will be using Open CV Library. This library comprises of programming functions mainly aimed at real-time computer vision.At this point, you would be wondering what is the purpose of learning Computer Vision? This is an area segment in Artificial Intelligence where computer algorithms are used to decipher what the computer understands from captured images. This field is currently used by various leading companies like Google, Facebook, Apple etc. You are having Computer Vision related aspects even in mobile phone applications like Snapchat, Instagram, Google Lens, etc.In this course, we will cover the basics of Computer Vision and create a project. At this point, you would be wondering what is the purpose of learning Computer Vision? This is an area segment in Artificial Intelligence where computer algorithms are used to decipher what the computer understands from captured images. This field is currently used by various leading companies like Google, Facebook, Apple etc. You are having Computer Vision related aspects even in mobile phone applications like Snapchat, Instagram, Google Lens, etc.In this course, we will cover the basics of Computer Vision and create a project.

Overview

Section 1: About the Program

Lecture 1 Course Introduction

Lecture 2 Course Outline

Section 2: Introduction to Computer Vision

Lecture 3 What is Computer Vision?

Lecture 4 Applications of Computer Vision

Lecture 5 Difference between Computer Vision & DIP

Lecture 6 Tools for Computer Vision

Section 3: Software Installation

Lecture 7 Installing Anaconda Distribution

Lecture 8 Handling Jupyter Notebooks 1

Lecture 9 Handling Jupyter Notebooks 2

Lecture 10 Handling Jupyter Notebooks 3

Lecture 11 Handling Jupyter Notebooks 4

Lecture 12 Handling Jupyter Notebooks 5

Lecture 13 Installation of OpenCV

Section 4: Fundamentals of OpenCV

Lecture 14 Fundamentals of Image Processing

Lecture 15 Reading Images

Lecture 16 Video Loading

Lecture 17 Changing Color Spaces

Lecture 18 Changing Color Spaces (Jupyter)

Lecture 19 Pixel Manipulation

Lecture 20 Pixel Manipulation - Initial Setup (Jupyter)

Lecture 21 Pixel Manipulation - Operation 1 (Jupyter)

Lecture 22 Pixel Manipulation - Operation 2 (Jupyter)

Lecture 23 Region of Interest

Lecture 24 Region of Interest (Jupyter)

Section 5: Image Processing - Image Manipulation

Lecture 25 What is Image Resizing?

Lecture 26 Image Resizing (Jupyter)

Lecture 27 What is Image Blurring?

Lecture 28 Image Blurring (Jupyter)

Lecture 29 What is Image Pyramid?

Lecture 30 Image Pyramid (Jupyter)

Section 6: Image Processing - Arithmetic Operations

Lecture 31 What is Arithmetic Operation?

Lecture 32 What is Image Blending?

Lecture 33 Image Blending (Jupyter)

Lecture 34 What is Image Subtraction?

Lecture 35 Image Subtraction (Jupyter)

Lecture 36 What is Bitwise Operation?

Lecture 37 Bitwise Operation (Jupyter)

Section 7: Edge Detection

Lecture 38 Edge Detection

Lecture 39 Edge Detection (Jupyter)

Section 8: Morphological Operations

Lecture 40 Morphological Transformations

Lecture 41 Morphological Transformations - Initial Setup (Jupyter)

Lecture 42 Understanding Erosion and Dilation

Lecture 43 Morphological Transformations - Erosion & Dilation (Jupyter)

Lecture 44 Understanding Morphological Techniques

Lecture 45 Morphological Transformations - Opening & Closing (Jupyter)

Section 9: Image Thresholding & Filtering

Lecture 46 Simple Thresholding

Lecture 47 Simple Thresholding (Jupyter)

Lecture 48 What is Noise in an Image?

Lecture 49 Sobel Filter-Using Gradients

Lecture 50 Sobel filter-Using Gradients (Jupyter)

Lecture 51 Laplacian Filter-Using Gradients

Lecture 52 Laplacian Filter-Using Gradients (Jupyter)

Section 10: Image Segmentation

Lecture 53 What is Image Segmentation?

Lecture 54 Understanding Cluster based Segmentation

Lecture 55 Image Segmentation (Jupyter)

Section 11: Feature Extraction

Lecture 56 What is Feature Matching?

Lecture 57 Understanding HOG

Lecture 58 Feature Matching - Using HOG (Jupyter)

Section 12: Motion Detection

Lecture 59 What is Motion Detection?

Lecture 60 Understanding Dense Optical Flow

Lecture 61 Dense Optical Flow (Jupyter)

Section 13: Project

Lecture 62 Cartoonify

Section 14: About the Program

Lecture 63 Course Conclusion

Anyone interested in the field of computer vision,Anyone interested in image processing