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آموزش بینایی رایانه Real-Time با OpenCV

دسته بندی ها: بینایی رایانه ای (Computer vision) ، آموزش های Packtpub ، آموزش OpenCV
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Learning Path: OpenCV: Real-Time Computer Vision with OpenCV Publisher:Packtpub Author:Ankita Thakur- Curator Duration:05:33:09

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 ... - Selection from Learning Path: OpenCV: Real-Time Computer Vision with OpenCV [Video]
Release Date: May 2017
ISBN: 9781788474832
Video Description
Practical OpenCV projectsIn DetailAre 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 ExampleMachine Learning with Open CV and PythonPATH PRODUCTSThis path navigates across the following products (in sequential order):OpenCV 3 by Example (3h 57m)Machine Learning with Open CV and Python (1h 35m)
Chapter 1 : OpenCV 3 by Example
The Course Overview
00:05:49
The Human Visual System and Understanding Image Content
00:04:57
What Can You Do with OpenCV?
00:12:10
Installing OpenCV
00:10:17
Basic CMakeConfiguration and Creating a Library
00:04:04
Managing Dependencies
00:03:17
Making the Script More Complex
00:03:42
Images and Matrices
00:02:33
Reading/Writing Images
00:05:08
Reading Videos and Cameras
00:03:10
Other Basic Object Types
00:02:04
Basic Matrix Operations, Data Persistence, and Storage
00:04:40
The OpenCVUser Interface and a Basic GUI
00:05:25
The Graphical User Interface with QT
00:01:49
Adding Slider and Mouse Events to Our Interfaces
00:04:38
Adding Buttons to a User Interface
00:03:57
OpenGL Support
00:04:38
Generating a CMakeScript File
00:01:59
Creating the Graphical User Interface
00:02:25
Drawing a Histogram
00:04:39
Image Color Equalization
00:02:57
Lomography Effect
00:04:18
The CartoonizeEffect
00:04:57
Isolating Objects in a Scene
00:02:23
Creating an Application for AOI
00:01:49
Preprocessing the Input Image
00:09:17
Segmenting Our Input Image
00:11:19
Introducing Machine Learning Concepts
00:07:05
Computer Vision and the Machine Learning Workflow
00:02:47
Automatic Object Inspection Classification Example
00:02:21
Feature Extraction
00:11:26
Understanding Haar Cascades
00:04:33
What Are Integral Images
00:02:57
Overlaying a Facemask in a Live Video
00:04:26
Get Your Sunglasses On
00:03:23
Tracking Your Nose, Mouth, and Ears
00:01:32
Background Subtraction
00:04:13
Frame Differencing
00:02:53
The Mixture of Gaussians Approach
00:03:16
Morphological Image processing
00:03:22
Other Morphological Operators
00:04:19
Tracking Objects of a Specific Color
00:03:19
Building an Interactive Object Tracker
00:05:56
Detecting Points Using the Harris Corner Detector
00:03:29
Shi-Tomasi Corner Detector
00:02:24
Feature-Based Tracking
00:08:22
Introducing Optical Character Recognition
00:02:42
The Preprocessing Step
00:10:00
Installing Tesseract OCR on Your Operating System
00:06:22
Using Tesseract OCR Library
00:08:07
Chapter 2 : Machine Learning with Open CV and Python
The Course Overview
00:03:01
The Basics of Machine Learning
00:06:09
Creating Training Data and Extracting Information
00:06:18
Extracting Features
00:04:07
K-Nearest Neighbors
00:08:37
Logistic Regression
00:08:03
Normal Bayes Classifier
00:04:27
Decision Trees
00:10:13
Support Vector Machines
00:12:02
Artificial Neural Networks
00:12:30
Unsupervised Learning
00:05:03
Deep Learning
00:15:04

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