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OpenCV 4 Computer Vision Application Programming Cookbook

OpenCV 4 Computer Vision Application Programming Cookbook

By : Millán Escrivá, Robert Laganiere
5 (1)
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OpenCV 4 Computer Vision Application Programming Cookbook

OpenCV 4 Computer Vision Application Programming Cookbook

5 (1)
By: Millán Escrivá, Robert Laganiere

Overview of this book

OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you'll work with recipes to implement a variety of tasks. 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 guiding you through setting up OpenCV, and explaining 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 this book, you'll have the skills you need to confidently implement a range of computer vision algorithms to meet the technical requirements of your complex CV projects.
Table of Contents (17 chapters)
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Digital image formation

Let's redraw a new version of the diagram shown in Chapter 10, Estimating Projective Relations in Images, describing the pinhole camera model. More specifically, we want to demonstrate the relationship between a point in 3D at its position (X, Y, Z) and its image (x, y) on a camera, specified in pixel coordinates:

Notice the changes that have been made to the original diagram. First, we added a reference frame that we positioned at the center of the projection. Second, we have the y axis pointing downward in order to ensure that the coordinate system is compatible with the usual convention that places the image origin in the upper-left corner of the image. Finally, we also identified a special point on the image plane—considering the line coming from the focal point is orthogonal to the image plane, then the point (u0, v0) is the pixel position...

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