OpenCV 4 computer vision application programming cookbook : build complex computer vision applications with OpenCV and C++ / David Millan Escriva, Robert Laganiere.

Format:

Book

Author/Creator:

Escriva, David Millan, author.

Laganière, R. (Robert), 1964- author.

Language:

English

Subjects (All):

Application software--Development.

Application software.

Computer vision.

Image processing--Digital techniques.

Image processing.

Physical Description:

1 online resource (479 pages)

Edition:

Fourth edition.

Place of Publication:

Birmingham ; Mumbai : Packt Publishing, 2019.

System Details:

Mode of access: World Wide Web.

text file

Biography/History:

Millan Escriva David: David Millan Escriva was 8 years old when he wrote his first program on an 8086 PC in Basic, which enabled the 2D plotting of basic equations. In 2005, he finished his studies in IT with honors, through the Universitat Politecnica de Valencia, in human-computer interaction supported by computer vision with OpenCV (v0. 96). He has worked with Blender, an open source, 3D software project, and on its first commercial movie, Plumiferos, as a computer graphics software developer. David has more than 10 years' experience in IT, with experience in computer vision, computer graphics, pattern recognition, and machine learning, working on different projects, and at different start-ups, and companies. He currently works as a researcher in computer vision. Laganiere Robert: Robert Laganiere is a professor at the School of Electrical Engineering and Computer Science of the University of Ottawa, Canada. He is also a faculty member of the VIVA research lab and is the co-author of several scientific publications and patents in content based video analysis, visual surveillance, driver-assistance, object detection, and tracking. Robert authored the OpenCV2 Computer Vision Application Programming Cookbook in 2011 and co-authored Object Oriented Software Development published by McGraw Hill in 2001. He co-founded Visual Cortek in 2006, an Ottawa-based video analytics start-up that was later acquired by iwatchlife. com in 2009. He is also a consultant in computer vision and has assumed the role of Chief Scientist in a number of start-up companies such as Cognivue Corp, iWatchlife, and Tempo Analytics. Robert has a Bachelor of Electrical Engineering degree from Ecole Polytechnique in Montreal (1987) and MSc and PhD degrees from INRS-Telecommunications, Montreal (1996). You can visit the author's website at laganiere. name.

Summary:

Discover interesting recipes to help you understand the concepts of object detection, image processing, and facial detection Key Features Explore the latest features and APIs in OpenCV 4 and build computer vision algorithms Develop effective, robust, and fail-safe vision for your applications Build computer vision algorithms with machine learning capabilities Book Description OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you'll work through recipes that implement a variety of tasks. With 70 self-contained tutorials, this book examines common pain points and best practices for computer vision (CV) developers. Each recipe addresses a specific problem and offers a proven, best-practice solution with insights into how it works, so that you can copy the code and configuration files and modify them to suit your needs. This book begins by setting up OpenCV, and explains how to manipulate pixels. You'll understand how you can process images with classes and count pixels with histograms. You'll also learn detecting, describing, and matching interest points. As you advance through the chapters, you'll get to grips with estimating projective relations in images, reconstructing 3D scenes, processing video sequences, and tracking visual motion. In the final chapters, you'll cover deep learning concepts such as face and object detection. By the end of the book, you'll be able to confidently implement a range of computer vision algorithms to meet the technical requirements of your complex CV projects. What you will learn Install and create a program using the OpenCV library Segment images into homogenous regions and extract meaningful objects Apply image filters to enhance image content Exploit image geometry to relay different views of a pictured scene Calibrate the camera from different image observations Detect people and objects in images using machine learning techniques Reconstruct a 3D scene from images Explore face detection using deep learning Who this book is for If you’re a CV developer or professional who already uses or would like to use OpenCV for building computer vision software, this book is for you. You’ll also find this book useful if you’re a C++ programmer looking to extend your computer vision skillset by learning OpenCV. Downloading the example code for this ebook: You can download the example code files for this ebook on GitHub at the following link: https://github...

Contents:

Cover

Title Page

Copyright and Credits

About Packt

Contributors

Table of Contents

Preface

Chapter 1: Playing with Images

Installing the OpenCV library

Getting ready

How to do it...

How it works...

There's more...

Using Qt for OpenCV developments

The OpenCV developer site

See also

Loading, displaying, and saving images

Clicking on images

Drawing on images

Running the example with Qt

Exploring the cv::Mat data structure

The input and output arrays

Defining regions of interest

Using image masks

Chapter 2: Manipulating the Pixels

Accessing pixel values

The cv::Mat_ template class

Scanning an image with pointers

Other color reduction formulas

Having input and output arguments

Efficient scanning of continuous images

Low-level pointer arithmetics

Scanning an image with iterators

Writing efficient image-scanning loops

Scanning an image with neighbor access

Performing simple image arithmetic

Overloaded image operators

Splitting the image channels

Remapping an image

See also.

Chapter 3: Processing Color Images with Classes

Comparing colors using the strategy design pattern

Computing the distance between two color vectors

Using OpenCV functions

The functor or function object

The OpenCV base class for algorithms

Segmenting an image with the GrabCut algorithm

Converting color representations

Representing colors with hue, saturation, and brightness

Using colors for detection - skin tone detection

Chapter 4: Counting the Pixels with Histograms

Computing the image histogram

Getting started

Computing histograms of color images

Applying lookup tables to modify the image's appearance

Stretching a histogram to improve the image contrast

Applying a lookup table on color images

Equalizing the image histogram

Backprojecting a histogram to detect specific image content

Backprojecting color histograms

Using the mean shift algorithm to find an object

Retrieving similar images using histogram comparison

Counting pixels with integral images

Adaptive thresholding

Visual tracking using histograms

Chapter 5: Transforming Images with Morphological Operations

Eroding and dilating images using morphological filters

Getting ready.

How to do it...

Opening and closing images using morphological filters

Detecting edges and corners using morphological filters

Segmenting images using watersheds

Extracting distinctive regions using MSER

Extracting foreground objects with the GrabCut algorithm

Chapter 6: Filtering the Images

Filtering images using low-pass filters

Downsampling an image

Interpolating pixel values

Filtering images using a median filter

Applying directional filters to detect edges

Gradient operators

Gaussian derivatives

Computing the Laplacian of an image

Enhancing the contrast of an image using the Laplacian

Difference of Gaussians

Chapter 7: Extracting Lines, Contours, and Components

Detecting image contours with the Canny operator

Detecting lines in images with the Hough transform

Detecting circles

Fitting a line to a set of points

Extracting the components' contours

Computing components' shape descriptors

How to do it...

How it works...

Quadrilateral detection

Chapter 8: Detecting Interest Points

Detecting corners in an image

Good features to track

The feature detector's common interface

Detecting features quickly

Adapted feature detection

Detecting scale-invariant features

The SIFT feature-detection algorithm

Detecting FAST features at multiple scales

The ORB feature-detection algorithm

Chapter 9: Describing and Matching Interest Points

Matching local templates

Template matching

Describing local intensity patterns

Cross-checking matches

The ratio test

Distance thresholding

Describing keypoints with binary features

FREAK

Chapter 10: Estimating Projective Relations in Images

Computing the fundamental matrix of an image pair

Matching images using a random sample consensus

Refining the fundamental matrix

Refining the matches

Computing a homography between two images

Detecting planar targets in an image

Chapter 11: Reconstructing 3D Scenes

Digital image formation

Calibrating a camera

There's more...

Calibration with known intrinsic parameters

Using a grid of circles for calibration

Recovering the camera pose

cv::Viz - a 3D visualizer module

Reconstructing a 3D scene from calibrated cameras

Decomposing a homography

Bundle adjustment

Computing depth from a stereo image

Chapter 12: Processing Video Sequences

Reading video sequences

Processing video frames

Processing a sequence of images

Using a frame processor class

Writing video sequences

The codec four-character code

Extracting the foreground objects in a video

The mixture of Gaussian method

Chapter 13: Tracking Visual Motion

Tracing feature points in a video

Estimating the optical flow

Tracking an object in a video

Chapter 14: Learning from Examples

Recognizing faces using the nearest neighbors of local binary patterns

Finding objects and faces with a cascade of Haar features

Face detection with a Haar cascade

Detecting objects and people using SVMs and histograms of oriented gradients

How it works...

There's more...

Notes:

Includes bibliographical references and index.

Description based on print version record.

Description based on print record.

ISBN:

9781789345285

1789345286

OCLC:

1101042394