Building Computer Vision Projects with OpenCV 4 and C++ (häftad)
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Häftad (Paperback / softback)
Antal sidor
Packt Publishing Limited
Joshi, Prateek / Mendona, Vincius G.
Black & white illustrations
230 x 195 x 30 mm
910 g
Antal komponenter
403:B&W 7.5 x 9.25 in or 235 x 191 mm Perfect Bound on White w/Matte Lam
Building Computer Vision Projects with OpenCV 4 and C++ (häftad)

Building Computer Vision Projects with OpenCV 4 and C++

Implement complex computer vision algorithms and explore deep learning and face detection

Häftad Engelska, 2019-03-26
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Delve into practical computer vision and image processing projects and get up to speed with advanced object detection techniques and machine learning algorithms Key Features Discover best practices for engineering and maintaining OpenCV projects Explore important deep learning tools for image classification Understand basic image matrix formats and filters Book DescriptionOpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books: Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millan Escriva Learn OpenCV 4 By Building Projects - Second Edition by David Millan Escriva, Vinicius G. Mendonca, and Prateek Joshi What you will learn Stay up-to-date with algorithmic design approaches for complex computer vision tasks Work with OpenCV's most up-to-date API through various projects Understand 3D scene reconstruction and Structure from Motion (SfM) Study camera calibration and overlay augmented reality (AR) using the ArUco module Create CMake scripts to compile your C++ application Explore segmentation and feature extraction techniques Remove backgrounds from static scenes to identify moving objects for surveillance Work with new OpenCV functions to detect and recognize text with Tesseract Who this book is forIf you are a software developer with a basic understanding of computer vision and image processing and want to develop interesting computer vision applications with OpenCV, this Learning Path is for you. Prior knowledge of C++ and familiarity with mathematical concepts will help you better understand the concepts in this Learning Path.
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David Millan Escriva was eight years old when he wrote his first program on an 8086 PC using the BASIC language. He completed his studies in IT from the Universitat Politecnica de Valencia with honors in human-computer interaction supported by computer vision with OpenCV (v0.96). He has a master's degree in artificial intelligence, computer graphics, and pattern recognition, focusing on pattern recognition and computer vision. He also has more than nine years' experience in computer vision, computer graphics, and pattern recognition. He is the author of the Damiles Blog, where he publishes articles and tutorials on OpenCV, computer vision in general, and optical character recognition algorithms. Vinicius G. Mendonca is a computer graphics university professor at Pontifical Catholic University of Parana (PUCPR). He started programming with C++ back in 1998, and ventured into the field of computer gaming and computer graphics back in 2006. He is currently a mentor at the Apple Developer Academy in Brazil, working with, and teaching, metal, machine learning and computer vision for mobile devices. He has served as a reviewer on other Pack books, including OpenNI Cookbook, and Mastering OpenCV and Computer Vision with OpenCV 3 and Qt5. In his research, he has used Kinect, OpenNI, and OpenCV to recognize Brazilian sign language gestures. His areas of interest include mobile, OpenGL, image processing, computer vision, and project management. Prateek Joshi is an artificial intelligence researcher, an author of eight published books, and a TEDx speaker. He has been featured in Forbes 30 Under 30, CNBC, TechCrunch, Silicon Valley Business Journal, and many more publications. He is the founder of Pluto AI, a venture-funded Silicon Valley start-up building an intelligence platform for water facilities. He graduated from the University of Southern California with a Master's degree specializing in Artificial Intelligence. He has previously worked at NVIDIA and Microsoft Research. Roy Shilkrot is an assistant professor of computer science at Stony Brook University, where he leads the Human Interaction group. Dr. Shilkrot's research is in computer vision, human-computer interfaces, and the cross-over between these two domains, funded by US federal, New York State, and industry grants. Dr. Shilkrot graduated from the Massachusetts Institute of Technology (MIT) with a PhD, and has authored more than 25 peer-reviewed papers published at premier computer science conferences, such as CHI and SIGGRAPH, as well as in leading academic journals such as ACM Transaction on Graphics (TOG) and ACM Transactions on Computer-Human Interaction (ToCHI).


Table of Contents Getting Started with OpenCV An Introduction to the Basics of OpenCV Learning Graphical User Interfaces Delving into Histogram and Filters Automated Optical Inspection, Object Segmentation, and Detection Learning Object Classification Detecting Face Parts and Overlaying Masks Video Surveillance, Background Modeling, and Morphological Operations Learning Object Tracking Developing Segmentation Algorithms for Text Recognition Text Recognition with Tesseract Deep Learning with OpenCV Cartoonifier and Skin Color Analysis on the RaspberryPi Explore Structure from Motion with the SfM Module Face Landmark and Pose with the Face Module Number Plate Recognition with Deep Convolutional Networks Face Detection and Recognition with the DNN Module Android Camera Calibration and AR Using the ArUco Module iOS Panoramas with the Stitching Module Finding the Best OpenCV Algorithm for the Job Avoiding Common Pitfalls in OpenCV