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OpenCV 3 Computer Vision Application Programming Cookbook

OpenCV 3 Computer Vision Application Programming Cookbook

By : Robert Laganiere
3.5 (2)
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OpenCV 3 Computer Vision Application Programming Cookbook

OpenCV 3 Computer Vision Application Programming Cookbook

3.5 (2)
By: Robert Laganiere

Overview of this book

Making your applications see has never been easier with OpenCV. With it, you can teach your robot how to follow your cat, write a program to correctly identify the members of One Direction, or even help you find the right colors for your redecoration. OpenCV 3 Computer Vision Application Programming Cookbook Third Edition provides a complete introduction to the OpenCV library and explains how to build your first computer vision program. You will be presented with a variety of computer vision algorithms and exposed to important concepts in image and video analysis that will enable you to build your own computer vision applications. This book helps you to get started with the library, and shows you how to install and deploy the OpenCV library to write effective computer vision applications following good programming practices. You will learn how to read and write images and manipulate their pixels. Different techniques for image enhancement and shape analysis will be presented. You will learn how to detect specific image features such as lines, circles or corners. You will be introduced to the concepts of mathematical morphology and image filtering. The most recent methods for image matching and object recognition are described, and you’ll discover how to process video from files or cameras, as well as how to detect and track moving objects. Techniques to achieve camera calibration and perform multiple-view analysis will also be explained. Finally, you’ll also get acquainted with recent approaches in machine learning and object classification.
Table of Contents (15 chapters)
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Eroding and dilating images using morphological filters


Erosion and dilation are the most fundamental morphological operators. Therefore, we will present them in this first recipe. The fundamental component in mathematical morphology is the structuring element. A structuring element can be simply defined as a configuration of pixels (the square shape in the following figure) on which an origin is defined (also called an anchor point). Applying a morphological filter consists of probing each pixel of the image using this structuring element. When the origin of the structuring element is aligned with a given pixel, its intersection with the image defines a set of pixels on which a particular morphological operation is applied (the nine shaded pixels in the following figure). In principle, the structuring element can be of any shape, but most often, a simple shape such as a square, circle, or diamond with the origin at the center is used. Custom structuring elements can be useful to emphasize...

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