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Learning Path: OpenCV: Real-Time Computer Vision with OpenCV

Video Description

Practical OpenCV projects

In Detail

Are you looking forward to developing interesting computer vision applications? If yes, then this Learning Path is for you. Computer vision and machine learning concepts are frequently used in practical projects based on computer vision. Whether you are completely new to the concept of computer vision or have a basic understanding of it, this Learning Path will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects.

OpenCV is a cross-platform, open source library that is used for face recognition, object tracking, and image and video processing. Learning the basic concepts of computer vision algorithms, models, and OpenCV’s API will help you develop all sorts of real-world applications.

Starting from the installation of OpenCV 3 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. You’ll explore the commonly-used computer vision techniques to build your own OpenCV projects from scratch. Next, we’ll teach you how to work with the various OpenCV modules for statistical modeling and machine learning. You’ll start by preparing your data for analysis, learn about supervised and unsupervised learning, and see how to use them. Finally, you’ll learn to implement efficient models using the popular machine learning techniques such as classification, regression, decision trees, K-nearest neighbors, boosting, and neural networks with the aid of C++ and OpenCV.

By the end of this Learning Path, you will be familiar with the basics of OpenCV such as matrix operations, filters, and histograms, as well as more advanced concepts such as segmentation, machine learning, complex video analysis, and text recognition.

Prerequisites: Knowledge of C++ and Python is required. Some understanding of statistical concepts would be helpful, but is not mandatory.

Resources: Code downloads and errata:

  • OpenCV 3 by Example

  • Machine Learning with Open CV and Python

  • PATH PRODUCTS

    This path navigates across the following products (in sequential order):

  • OpenCV 3 by Example (3h 57m)

  • Machine Learning with Open CV and Python (1h 35m)

  • Table of Contents

    1. Chapter 1 : OpenCV 3 by Example
      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
      5. Basic CMakeConfiguration and Creating a Library 00:04:04
      6. Managing Dependencies 00:03:17
      7. Making the Script More Complex 00:03:42
      8. Images and Matrices 00:02:33
      9. Reading/Writing Images 00:05:08
      10. Reading Videos and Cameras 00:03:10
      11. Other Basic Object Types 00:02:04
      12. Basic Matrix Operations, Data Persistence, and Storage 00:04:40
      13. The OpenCVUser Interface and a Basic GUI 00:05:25
      14. The Graphical User Interface with QT 00:01:49
      15. Adding Slider and Mouse Events to Our Interfaces 00:04:38
      16. Adding Buttons to a User Interface 00:03:57
      17. OpenGL Support 00:04:38
      18. Generating a CMakeScript File 00:01:59
      19. Creating the Graphical User Interface 00:02:25
      20. Drawing a Histogram 00:04:39
      21. Image Color Equalization 00:02:57
      22. Lomography Effect 00:04:18
      23. The CartoonizeEffect 00:04:57
      24. Isolating Objects in a Scene 00:02:23
      25. Creating an Application for AOI 00:01:49
      26. Preprocessing the Input Image 00:09:17
      27. Segmenting Our Input Image 00:11:19
      28. Introducing Machine Learning Concepts 00:07:05
      29. Computer Vision and the Machine Learning Workflow 00:02:47
      30. Automatic Object Inspection Classification Example 00:02:21
      31. Feature Extraction 00:11:26
      32. Understanding Haar Cascades 00:04:33
      33. What Are Integral Images 00:02:57
      34. Overlaying a Facemask in a Live Video 00:04:26
      35. Get Your Sunglasses On 00:03:23
      36. Tracking Your Nose, Mouth, and Ears 00:01:32
      37. Background Subtraction 00:04:13
      38. Frame Differencing 00:02:53
      39. The Mixture of Gaussians Approach 00:03:16
      40. Morphological Image processing 00:03:22
      41. Other Morphological Operators 00:04:19
      42. Tracking Objects of a Specific Color 00:03:19
      43. Building an Interactive Object Tracker 00:05:56
      44. Detecting Points Using the Harris Corner Detector 00:03:29
      45. Shi-Tomasi Corner Detector 00:02:24
      46. Feature-Based Tracking 00:08:22
      47. Introducing Optical Character Recognition 00:02:42
      48. The Preprocessing Step 00:10:00
      49. Installing Tesseract OCR on Your Operating System 00:06:22
      50. Using Tesseract OCR Library 00:08:07
    2. Chapter 2 : Machine Learning with Open CV and Python
      1. The Course Overview 00:03:01
      2. The Basics of Machine Learning 00:06:09
      3. Creating Training Data and Extracting Information 00:06:18
      4. Extracting Features 00:04:07
      5. K-Nearest Neighbors 00:08:37
      6. Logistic Regression 00:08:03
      7. Normal Bayes Classifier 00:04:27
      8. Decision Trees 00:10:13
      9. Support Vector Machines 00:12:02
      10. Artificial Neural Networks 00:12:30
      11. Unsupervised Learning 00:05:03
      12. Deep Learning 00:15:04