Discover interesting recipes to help you understand the concepts of object detection, image processing, and facial detectionKey FeaturesExplore the latest features and APIs in OpenCV 4 and build computer vision algorithmsDevelop effective, robust, and fail-safe vision for your applicationsBuild computer vision algorithms with machine learning capabilitiesBook DescriptionOpenCV 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, such as facial recognition and detection. 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 to computer vision algorithms to meet the technical requirements of your complex CV projectsWhat you will learnInstall and create a program using the OpenCV librarySegment images into homogenous regions and extract meaningful objectsApply image filters to enhance image contentExploit image geometry to relay different views of a pictured sceneCalibrate the camera from different image observationsDetect people and objects in images using machine learning techniquesReconstruct a 3D scene from imagesExplore face detection using deep learningWho this book is forIf 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.

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Detalles bibliogr�ficos

T�tulo: OpenCV 4 Computer Vision Application Programming Cookbook
Editor: Packt Publishing Limited, GB
Fecha de publicaci�n: 2019
Idioma: Ingl�s
ISBN 10: 1789340721
ISBN 13: 9781789340723
Encuadernaci�n: Paperback
Edici�n: Fourth.
Estado: New

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Sinopsis

This book will present a variety of CV algorithms using the standard library. It will implement any shortfall that might come in CV by practicing the recipes that implement various tasks such as image processing and object recognition among others. It will help you in implementing CV algorithms to meet the technical requirement of your projects.

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David Mill�n Escriv� was eight 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 through the Universitat Polit�cnica de Valenci with honors in human-computer interaction supported by computer vision with OpenCV (v0.96). He had a final project based on this subject and published it on HCI Spanish congress. He has worked with Blender, an open source, 3D software project, and worked on his first commercial movie, Plumiferos - Aventuras voladoras, as a computer graphics software developer. David now has more than 10 years of experience in IT, with experience in computer vision, computer graphics, and pattern recognition, working with different projects and start-ups, applying his knowledge of computer vision, optical character recognition, and augmented reality. He is the author of the DamilesBlog blog, where he publishes research articles and tutorials about OpenCV, computer vision in general, and optical character recognition algorithms.

Robert Laganiere is a professor at the University of Ottawa, Canada. He is also a faculty member of the VIVA research lab and is the coauthor of several scientific publications and patents in content-based video analysis, visual surveillance, driver-assistance, object detection, and tracking. He cofounded Visual Cortek, a video analytics start-up, which was later acquired by iWatchLife. He is also a consultant in computer vision and has assumed the role of chief scientist in a number of start-ups companies, including Cognivue Corp, iWatchLife, and Tempo Analytics. Robert has a Bachelor of Electrical Engineering degree from Ecole Polytechnique in Montreal (1987), and M.Sc. and Ph.D. degrees from INRS-Telecommunications, Montreal (1996).

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