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

Learning Path: OpenCV: Real-Time Computer Vision with OpenCV, available at $34.99, has an average rating of 4.45, with 62 lectures, based on 74 reviews, and has 707 subscribers.

You will learn about Learn about the computer vision workflows and understand the basic image matrix format and filters Understand the segmentation and feature extraction techniques Learn how to remove backgrounds from a static scene to identify moving objects for video surveillance Use the new OpenCV functions for text detection and recognition with Tesseract Master logistic regression and regularization techniques Solve image segmentation problem using k-means clustering Load models trained with popular deep learning libraries such as Caffe This course is ideal for individuals who are This Learning Path is for developers who have a basic understanding of computer vision and image processing and want to develop interesting computer vision applications with OpenCV. It is particularly useful for This Learning Path is for developers who have a basic understanding of computer vision and image processing and want to develop interesting computer vision applications with OpenCV.

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

Summary

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

Price: $34.99

Average Rating: 4.45

Number of Lectures: 62

Number of Published Lectures: 62

Number of Curriculum Items: 62

Number of Published Curriculum Objects: 62

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Learn about the computer vision workflows and understand the basic image matrix format and filters

Understand the segmentation and feature extraction techniques

Learn how to remove backgrounds from a static scene to identify moving objects for video surveillance

Use the new OpenCV functions for text detection and recognition with Tesseract

Master logistic regression and regularization techniques

Solve image segmentation problem using k-means clustering

Load models trained with popular deep learning libraries such as Caffe

Who Should Attend

This Learning Path is for developers who have a basic understanding of computer vision and image processing and want to develop interesting computer vision applications with OpenCV.

Target Audiences

This Learning Path is for developers who have a basic understanding of computer vision and image processing and want to develop interesting computer vision applications with OpenCV.

Are you looking forward to developing interesting computer vision applications? If yes, then this Learning Path is for you.

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

Computer vision and machine learning concepts are frequently used together 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. By learning the basic concepts of computer vision algorithms, models, and OpenCV’s API, you will be able to develop different types of real-world applications.

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 and 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.

Meet Your Experts:

We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:

David Millán Escriváwas eight years old when he wrote his first program on an 8086 PC with Basic language, which enabled the 2D plotting of basic equations. In 2005, he finished his studies in IT through the Universitat Politécnica de Valencia with honors in human-computer interaction supported by computer vision with OpenCV (v0.96).

Prateek Joshi is an artificial intelligence researcher, published author of five books, and TEDx speaker. He is the founder of Pluto AI, a venture-funded Silicon Valley startup building an analytics platform for smart water management powered by deep learning.

Joe Minichino is a computer vision engineer for Hoolux Medical by day and a developer of the NoSQL database LokiJS by night. At Hoolux, he leads the development of an Android computer vision-based advertising platform for the medical industry.

Course Curriculum

Chapter 1: OpenCV 3 by Example

Lecture 1: The Course Overview

Lecture 2: The Human Visual System and Understanding Image Content

Lecture 3: What Can You Do with OpenCV?

Lecture 4: Installing OpenCV

Lecture 5: Basic CMakeConfiguration and Creating a Library

Lecture 6: Managing Dependencies

Lecture 7: Making the Script More Complex

Lecture 8: Images and Matrices

Lecture 9: Reading/Writing Images

Lecture 10: Reading Videos and Cameras

Lecture 11: Other Basic Object Types

Lecture 12: Basic Matrix Operations, Data Persistence, and Storage

Lecture 13: The OpenCVUser Interface and a Basic GUI

Lecture 14: The Graphical User Interface with QT

Lecture 15: Adding Slider and Mouse Events to Our Interfaces

Lecture 16: Adding Buttons to a User Interface

Lecture 17: OpenGL Support

Lecture 18: Generating a CMakeScript File

Lecture 19: Creating the Graphical User Interface

Lecture 20: Drawing a Histogram

Lecture 21: Image Color Equalization

Lecture 22: Lomography Effect

Lecture 23: The CartoonizeEffect

Lecture 24: Isolating Objects in a Scene

Lecture 25: Creating an Application for AOI

Lecture 26: Preprocessing the Input Image

Lecture 27: Segmenting Our Input Image

Lecture 28: Introducing Machine Learning Concepts

Lecture 29: Computer Vision and the Machine Learning Workflow

Lecture 30: Automatic Object Inspection Classification Example

Lecture 31: Feature Extraction

Lecture 32: Understanding Haar Cascades

Lecture 33: What Are Integral Images

Lecture 34: Overlaying a Facemask in a Live Video

Lecture 35: Get Your Sunglasses On

Lecture 36: Tracking Your Nose, Mouth, and Ears

Lecture 37: Background Subtraction

Lecture 38: Frame Differencing

Lecture 39: The Mixture of Gaussians Approach

Lecture 40: Morphological Image processing

Lecture 41: Other Morphological Operators

Lecture 42: Tracking Objects of a Specific Color

Lecture 43: Building an Interactive Object Tracker

Lecture 44: Detecting Points Using the Harris Corner Detector

Lecture 45: Shi-Tomasi Corner Detector

Lecture 46: Feature-Based Tracking

Lecture 47: Introducing Optical Character Recognition

Lecture 48: The Preprocessing Step

Lecture 49: Installing Tesseract OCR on Your Operating System

Lecture 50: Using Tesseract OCR Library

Chapter 2: Machine Learning with Open CV and Python

Lecture 1: The Course Overview

Lecture 2: The Basics of Machine Learning

Lecture 3: Creating Training Data and Extracting Information

Lecture 4: Extracting Features

Lecture 5: K-Nearest Neighbors

Lecture 6: Logistic Regression

Lecture 7: Normal Bayes Classifier

Lecture 8: Decision Trees

Lecture 9: Support Vector Machines

Lecture 10: Artificial Neural Networks

Lecture 11: Unsupervised Learning

Lecture 12: Deep Learning

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 6 votes

2 stars: 6 votes

3 stars: 11 votes

4 stars: 24 votes

5 stars: 27 votes

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