About the Author

Aleksandr Rybnikov has over 5 years of experience in C++ programming, including 3 years in the Computer Vision (CV) domain. He worked at Itseez, a company that supported and developed OpenCV, and then at Intel. He enriched OpenCV's dnn module by adding support of another two Deep Learning (DL) frameworks and many features, along with improving the existing functionality. As an engineer, he participated in CV and DL projects such as iris recognition, object detection, semantic segmentation, 6-DOF pose estimation, and digital hologram reconstruction. He has a master's degree in physics.

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Key Features

Build computer vision applications with OpenCV functionality via Python APIGet to grips with image processing, multiple view geometry, and machine learningLearn to use deep learning models for image classification, object detection, and face recognition

Book Description

OpenCV 3 is a native cross-platform library for computer vision, machine learning, and image processing. OpenCV's convenient high-level APIs hide very powerful internals designed for computational efficiency that can take advantage of multicore and GPU processing. This book will help you tackle increasingly challenging computer vision problems by providing a number of recipes that you can use to improve your applications.

In this book, you will learn how to process an image by manipulating pixels and analyze an image using histograms. Then, we'll show you how to apply image filters to enhance image content and exploit the image geometry in order to relay different views of a pictured scene. We'll explore techniques to achieve camera calibration and perform a multiple-view analysis.

Later, you'll work on reconstructing a 3D scene from images, converting low-level pixel information to high-level concepts for applications such as object detection and recognition. You'll also discover how to process video from files or cameras and how to detect and track moving objects. Finally, you'll get acquainted with recent approaches in deep learning and neural networks.

By the end of the book, you'll be able to apply your skills in OpenCV to create computer vision applications in various domains.

What you will learn

Get familiar with low-level image processing methodsSee the common linear algebra tools needed in computer visionWork with different camera models and epipolar geometryFind out how to detect interesting points in images and compare themBinarize images and mask out regions of interestDetect objects and track them in videos

Who This Book Is For

This book is for developers who have a basic knowledge of Python. If you are aware of the basics of OpenCV and are ready to build computer vision systems that are smarter, faster, more complex, and more practical than the competition, then this book is for you.

Table of Contents

IO and GUIMatrices colors and filtersContours and segmentationObject detection and machine learningDeep learningLinear algebraDetectors and descriptorsImage and video processingMultiple view geometry