Learn OpenCV for Computer Vision
Learn OpenCV for Computer Vision, available at $19.99, has an average rating of 5, with 63 lectures, 10 quizzes, based on 1 reviews, and has 11 subscribers.
You will learn about 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 This course is ideal for individuals who are Anyone interested in the field of computer vision or Anyone interested in image processing It is particularly useful for Anyone interested in the field of computer vision or Anyone interested in image processing.
Enroll now: Learn OpenCV for Computer Vision
Summary
Title: Learn OpenCV for Computer Vision
Price: $19.99
Average Rating: 5
Number of Lectures: 63
Number of Quizzes: 10
Number of Published Lectures: 63
Number of Published Quizzes: 10
Number of Curriculum Items: 73
Number of Published Curriculum Objects: 73
Original Price: ₹3,299
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- Anyone interested in the field of computer vision
- Anyone interested in image processing
Target Audiences
- Anyone interested in the field of computer vision
- Anyone interested in image processing
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.
Course Curriculum
Chapter 1: About the Program
Lecture 1: Course Introduction
Lecture 2: Course Outline
Chapter 2: Introduction to Computer Vision
Lecture 1: What is Computer Vision?
Lecture 2: Applications of Computer Vision
Lecture 3: Difference between Computer Vision & DIP
Lecture 4: Tools for Computer Vision
Chapter 3: Software Installation
Lecture 1: Installing Anaconda Distribution
Lecture 2: Handling Jupyter Notebooks 1
Lecture 3: Handling Jupyter Notebooks 2
Lecture 4: Handling Jupyter Notebooks 3
Lecture 5: Handling Jupyter Notebooks 4
Lecture 6: Handling Jupyter Notebooks 5
Lecture 7: Installation of OpenCV
Chapter 4: Fundamentals of OpenCV
Lecture 1: Fundamentals of Image Processing
Lecture 2: Reading Images
Lecture 3: Video Loading
Lecture 4: Changing Color Spaces
Lecture 5: Changing Color Spaces (Jupyter)
Lecture 6: Pixel Manipulation
Lecture 7: Pixel Manipulation – Initial Setup (Jupyter)
Lecture 8: Pixel Manipulation – Operation 1 (Jupyter)
Lecture 9: Pixel Manipulation – Operation 2 (Jupyter)
Lecture 10: Region of Interest
Lecture 11: Region of Interest (Jupyter)
Chapter 5: Image Processing – Image Manipulation
Lecture 1: What is Image Resizing?
Lecture 2: Image Resizing (Jupyter)
Lecture 3: What is Image Blurring?
Lecture 4: Image Blurring (Jupyter)
Lecture 5: What is Image Pyramid?
Lecture 6: Image Pyramid (Jupyter)
Chapter 6: Image Processing – Arithmetic Operations
Lecture 1: What is Arithmetic Operation?
Lecture 2: What is Image Blending?
Lecture 3: Image Blending (Jupyter)
Lecture 4: What is Image Subtraction?
Lecture 5: Image Subtraction (Jupyter)
Lecture 6: What is Bitwise Operation?
Lecture 7: Bitwise Operation (Jupyter)
Chapter 7: Edge Detection
Lecture 1: Edge Detection
Lecture 2: Edge Detection (Jupyter)
Chapter 8: Morphological Operations
Lecture 1: Morphological Transformations
Lecture 2: Morphological Transformations – Initial Setup (Jupyter)
Lecture 3: Understanding Erosion and Dilation
Lecture 4: Morphological Transformations – Erosion & Dilation (Jupyter)
Lecture 5: Understanding Morphological Techniques
Lecture 6: Morphological Transformations – Opening & Closing (Jupyter)
Chapter 9: Image Thresholding & Filtering
Lecture 1: Simple Thresholding
Lecture 2: Simple Thresholding (Jupyter)
Lecture 3: What is Noise in an Image?
Lecture 4: Sobel Filter-Using Gradients
Lecture 5: Sobel filter-Using Gradients (Jupyter)
Lecture 6: Laplacian Filter-Using Gradients
Lecture 7: Laplacian Filter-Using Gradients (Jupyter)
Chapter 10: Image Segmentation
Lecture 1: What is Image Segmentation?
Lecture 2: Understanding Cluster based Segmentation
Lecture 3: Image Segmentation (Jupyter)
Chapter 11: Feature Extraction
Lecture 1: What is Feature Matching?
Lecture 2: Understanding HOG
Lecture 3: Feature Matching – Using HOG (Jupyter)
Chapter 12: Motion Detection
Lecture 1: What is Motion Detection?
Lecture 2: Understanding Dense Optical Flow
Lecture 3: Dense Optical Flow (Jupyter)
Chapter 13: Project
Lecture 1: Cartoonify
Chapter 14: About the Program
Lecture 1: Course Conclusion
Instructors
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Prag Robotics
Robotics & A.I.
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Frequently Asked Questions
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