The most important structure in a Computer Vision is without any doubt the images. The image in Computer Vision is a representation of the physical world captured with a digital device. This picture is only a sequence of numbers stored in a matrix format, as shown in the following image. Each number is a measurement of the light intensity for the considered wavelength (for example, red, green, or blue in color images) or for a wavelength range (for panchromatic devices). Each point in an image is called a pixel (for a picture element), and each pixel can store one or more values depending on whether it is a gray, black, or white image (called a binary image as well) that stores only one value, such as 0 or 1, a gray-scale-level image that can store only one value, or a color image that can store three values. These values are usually integer numbers between 0 and 255, but you can use the other range. For example, 0 to 1 in a floating point numbers such as HDRI (High...
OpenCV By Example
By :
OpenCV By Example
By:
Overview of this book
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.
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Table of Contents (18 chapters)
OpenCV By Example
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Getting Started with OpenCV
An Introduction to the Basics of OpenCV
Learning the Graphical User Interface and Basic Filtering
Delving into Histograms 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
Index
Customer Reviews