OpenCV Essentials

Acquire, process, and analyze visual content to build full-fledged imaging applications using OpenCV

OpenCV Essentials

Oscar Deniz Suarez et al.

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Acquire, process, and analyze visual content to build full-fledged imaging applications using OpenCV
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Book Details

ISBN 139781783984244
Paperback214 pages

Book Description

OpenCV, arguably the most widely used computer vision library, includes hundreds of ready-to-use imaging and vision functions used in both academia and industry. It mainly focuses on real-time image processing. As cameras get cheaper and imaging features grow in demand, the range of applications using OpenCV increases significantly, both for desktop and mobile platforms.

The book provides an example-based tour of OpenCV's main modules and algorithms, including the latest available in version 3.0. Starting with the setup and description of the library, this book teaches you how to add graphical user interface capabilities to OpenCV programs. Further, you will learn about the essential techniques such as image processing, image segmentation, object detection, and motion, which will help you process and analyze images better. You will also learn how to extract 2D features from images and how to take advantage of machine learning. By the end of this book, you will completely understand how to put those computer vision techniques into practice.

 

Table of Contents

Chapter 1: Getting Started
Setting up OpenCV
API concepts and basic datatypes
Our first program – reading and writing images and videos
Reading and playing a video file
Live input from a camera
Summary
Chapter 2: Something We Look At – Graphical User Interfaces
Using OpenCV's highgui module
Text and drawing
Selecting regions
Using Qt-based functions
Summary
Chapter 3: First Things First – Image Processing
Pixel-level access and common operations
Image histogram
Histogram equalization
Brightness and contrast modeling
Histogram matching and LUT
Conversion from RGB to other color spaces
Filtering with the retina model
Arithmetic and geometrical transforms
Summary
What else?
Chapter 4: What's in the Image? Segmentation
Thresholding
Contours and connected components
Flood fill
Watershed segmentation
GrabCut
Summary
What else?
Chapter 5: Focusing on the Interesting 2D Features
Interest points
Feature detectors
Feature descriptor extractors
Descriptor matchers
Summary
What else?
Chapter 6: Where's Wally? Object Detection
Object detection
Detecting objects with OpenCV
Cascades are beautiful
Latent SVM
Scene text detection
Summary
What else?
Chapter 7: What Is He Doing? Motion
Motion history
Reading video sequences
The Lucas-Kanade optical flow
The Gunnar-Farneback optical flow
The Mean-Shift tracker
The CamShift tracker
The Motion templates
The Background subtraction technique
Image alignment
Summary
What else?
Chapter 8: Advanced Topics
Machine learning
The KNN classifier
The Random Forest classifier
SVM for classification
What about GPUs?
Setting up OpenCV with CUDA
Our first GPU-based program
Going real time
Summary
What else?

What You Will Learn

  • Explore advanced image processing techniques such as the retina algorithm, morphing, and color transfer
  • Create programs using advanced segmentation tools such as the new connectedComponents and connectedComponentsWithStats functions
  • Use flood filling along with the watershed transform to obtain better segmentations
  • Explore the new powerful KAZE features
  • Use advanced video-based background/foreground segmentation for class BackgroundSubtractor and ECC-based warping
  • Leverage the available object detection frameworks and the new scene text detection functionality
  • Get a grasp of advanced topics such as machine learning and GPU optimization

Authors

Table of Contents

Chapter 1: Getting Started
Setting up OpenCV
API concepts and basic datatypes
Our first program – reading and writing images and videos
Reading and playing a video file
Live input from a camera
Summary
Chapter 2: Something We Look At – Graphical User Interfaces
Using OpenCV's highgui module
Text and drawing
Selecting regions
Using Qt-based functions
Summary
Chapter 3: First Things First – Image Processing
Pixel-level access and common operations
Image histogram
Histogram equalization
Brightness and contrast modeling
Histogram matching and LUT
Conversion from RGB to other color spaces
Filtering with the retina model
Arithmetic and geometrical transforms
Summary
What else?
Chapter 4: What's in the Image? Segmentation
Thresholding
Contours and connected components
Flood fill
Watershed segmentation
GrabCut
Summary
What else?
Chapter 5: Focusing on the Interesting 2D Features
Interest points
Feature detectors
Feature descriptor extractors
Descriptor matchers
Summary
What else?
Chapter 6: Where's Wally? Object Detection
Object detection
Detecting objects with OpenCV
Cascades are beautiful
Latent SVM
Scene text detection
Summary
What else?
Chapter 7: What Is He Doing? Motion
Motion history
Reading video sequences
The Lucas-Kanade optical flow
The Gunnar-Farneback optical flow
The Mean-Shift tracker
The CamShift tracker
The Motion templates
The Background subtraction technique
Image alignment
Summary
What else?
Chapter 8: Advanced Topics
Machine learning
The KNN classifier
The Random Forest classifier
SVM for classification
What about GPUs?
Setting up OpenCV with CUDA
Our first GPU-based program
Going real time
Summary
What else?

Book Details

ISBN 139781783984244
Paperback214 pages
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