Enhance your understanding of Computer Vision and image processing by developing real-world projects in OpenCV 3
Key Features
- Get to grips with the basics of Computer Vision and image processing
- This is a step-by-step guide to developing several real-world Computer Vision projects using OpenCV 3
- This book takes a special focus on working with Tesseract OCR, a free, open-source library to recognize text in images
Book Description
Open CV is a cross-platform, free-for-use library that is primarily used for real-time Computer Vision and image processing. It is considered to be one of the best open source libraries that helps developers focus on constructing complete projects on image processing, motion detection, and image segmentation.
Whether you are completely new to the concept of Computer Vision or have a basic understanding of it, this book will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects.
Starting from the installation of OpenCV on your system and understanding the basics of image processing, we swiftly move on to creating optical flow video analysis or text recognition in complex scenes, and will take you through the commonly used Computer Vision techniques to build your own Open CV projects from scratch.
By the end of this book, you will be familiar with the basics of Open CV such as matrix operations, filters, and histograms, as well as more advanced concepts such as segmentation, machine learning, complex video analysis, and text recognition.
What You Will Learn
- Install OpenCV 3 on your operating system
- Create the required CMake scripts to compile the C++ application and manage its dependencies
- Get to grips with the Computer Vision workflows and understand the basic image matrix format and filters
- Understand the segmentation and feature extraction techniques
- Remove backgrounds from a static scene to identify moving objects for video surveillance
- Track different objects in a live video using various techniques
- Use the new OpenCV functions for text detection and recognition with Tesseract
Who This Book Is For
If you are a software developer with a basic understanding of Computer Vision and image processing and want to develop interesting Computer Vision applications with Open CV, this is the book for you. Knowledge of C++ is required.
Alexis Perrier is a data science consultant with a background in signal processing and stochastic algorithms.
A former Parisian, Alexis is now actively involved in the D.C. data science community as an instructor, blogger, and presenter. Alexis is also an avid jazz and classical music fan, a book lover and proud owner of a real chalk blackboard on which he regularly tries to share his fascination with mathematical equations with his 3 children.
He holds a Master in Mathematics from Université Pierre et Marie Curie Paris VI, a Ph.D. in Signal Processing from Telecom ParisTech and currently resides in Washington D.C.
Follow him on twitter @alexip
Giuseppe Ciaburro holds a PhD in environmental technical physics and two master's degrees. His research was focused on machine learning applications in the study of the urban sound environments. He works at Built Environment Control Laboratory - Università degli Studi della Campania Luigi Vanvitelli (Italy). He has over 15 years of work experience in programming (Python, R, and MATLAB), first in the field of combustion and then in acoustics and noise control. He has several publications to his credit.
V Kishore Ayyadevara has over 9 years of experience of using analytics to solve business problems and setting up analytical work streams through his work at American Express, Amazon, and more recently a retail analytics consulting start-up. He is an MBA graduate from IIM Calcutta and also an electronics and communications engineer.
He worked in the fields of credit risk analytics, supply chain analytics, and consulting for multiple FMCG companies to identify ways to improve their profitability.
His interests lie in translating a business problem into a data-related problem by demystifying complexity in data science and identifying ways to further embed analytics in business.
