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OpenCV 3 by Example

Video Description

Enhance your understanding of Computer Vision and image processing by developing real-world projects in OpenCV 3

About This Video

  • Get to grips with the basics of Computer Vision and image processing

  • This is a step-by-step video tutorial to developing several real-world Computer Vision projects using OpenCV 3

  • This video takes a special focus on working with Tesseract OCR, a free, open-source library to recognize text in images

  • In Detail

    Open CV is a cross-platform, free-for-use library that is primarily used for real-time Computer Vision and image processing. It is considered to be one of the best open source libraries that helps developers focus on constructing complete projects on image processing, motion detection, and image segmentation.Whether you are completely new to the concept of Computer Vision or have a basic understanding of it, this video will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects.

    Starting from the installation of OpenCV on your system and understanding the basics of image processing, we swiftly move on to creating optical flow video analysis or text recognition in complex scenes, and will take you through the commonly-used Computer Vision techniques to build your own Open CV projects from scratch.By the end of this video, you will be familiar with the basics of Open CV such as matrix operations, filters, and histograms, as well as more advanced concepts such as segmentation, machine learning, complex video analysis, and text recognition

    Table of Contents

    1. Chapter 1 : Getting Started with OpenCV
      1. The Course Overview 00:05:49
      2. The Human Visual System and Understanding Image Content 00:04:57
      3. What Can You Do with OpenCV? 00:12:10
      4. Installing OpenCV 00:10:17
    2. Chapter 2 : An Introduction to the Basics of OpenCV
      1. Basic CMakeConfiguration and Creating a Library 00:04:04
      2. Managing Dependencies 00:03:17
      3. Making the Script More Complex 00:03:42
      4. Images and Matrices 00:02:33
      5. Reading/Writing Images 00:05:08
      6. Reading Videos and Cameras 00:03:10
      7. Other Basic Object Types 00:02:04
      8. Basic Matrix Operations, Data Persistence, and Storage 00:04:40
    3. Chapter 3 : Learning the Graphical User Interface and Basic Filtering
      1. The OpenCVUser Interface and a Basic GUI 00:05:25
      2. The Graphical User Interface with QT 00:01:49
      3. Adding Slider and Mouse Events to Our Interfaces 00:04:38
      4. Adding Buttons to a User Interface 00:03:57
      5. OpenGL Support 00:04:38
    4. Chapter 4 : Delving into Histograms and Filters
      1. Generating a CMakeScript File 00:01:59
      2. Creating the Graphical User Interface 00:02:25
      3. Drawing a Histogram 00:04:39
      4. Image Color Equalization 00:02:57
      5. Lomography Effect 00:04:18
      6. The CartoonizeEffect 00:04:57
    5. Chapter 5 : Automated Optical Inspection, Object Segmentation, and Detection
      1. Isolating Objects in a Scene 00:02:23
      2. Creating an Application for AOI 00:01:49
      3. Preprocessing the Input Image 00:09:17
      4. Segmenting Our Input Image 00:11:19
    6. Chapter 6 : Learning Object Classification
      1. Introducing Machine Learning Concepts 00:07:05
      2. Computer Vision and the Machine Learning Workflow 00:02:47
      3. Automatic Object Inspection Classification Example 00:02:21
      4. Feature Extraction 00:11:26
    7. Chapter 7 : Detecting Face Parts and Overlaying Masks
      1. Understanding Haar Cascades 00:04:33
      2. What Are Integral Images 00:02:57
      3. Overlaying a Facemask in a Live Video 00:04:26
      4. Get Your Sunglasses On 00:03:23
      5. Tracking Your Nose, Mouth, and Ears 00:01:32
    8. Chapter 8 : Video Surveillance, Background Modeling, and Morphological Operations
      1. Background Subtraction 00:04:13
      2. Frame Differencing 00:02:53
      3. The Mixture of Gaussians Approach 00:03:16
      4. Morphological Image processing 00:03:22
      5. Other Morphological Operators 00:04:19
    9. Chapter 9 : Learning Object Tracking
      1. Tracking Objects of a Specific Color 00:03:19
      2. Building an Interactive Object Tracker 00:05:56
      3. Detecting Points Using the Harris Corner Detector 00:03:29
      4. Shi-Tomasi Corner Detector 00:02:24
      5. Feature-Based Tracking 00:08:22
    10. Chapter 10 : Developing Segmentation Algorithms for Text Recognition
      1. Introducing Optical Character Recognition 00:02:42
      2. The Preprocessing Step 00:10:00
      3. Installing Tesseract OCR on Your Operating System 00:06:22
      4. Using Tesseract OCR Library 00:08:07