Learn Opencv For Computer Vision
Published 12/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.74 GB | Duration: 4h 3m
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
