Linear dependence between variables is the simplest of all possible options. It can be found in many applications, from approximation and geometry tasks, to data compression, camera calibration, and machine learning. But despite its simplicity, things get complicated when real-world influences come into play. All data gathered from sensors includes a portion of noise, which can lead systems of linear equations to have unstable solutions. computer vision problems often require solving systems of linear equations. Even in many OpenCV functions, these linear equations are hidden; it's certain that you will face them in your computer vision applications. The recipes in this chapter will acquaint you with approaches from linear algebra that can be useful and actually are used in computer Vision.
OpenCV 3 Computer Vision with Python Cookbook
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OpenCV 3 Computer Vision with Python Cookbook
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Overview of this book
OpenCV 3 is a native cross-platform library for computer vision, machine learning, and image processing. OpenCV's convenient high-level APIs hide very powerful internals designed for computational efficiency that can take advantage of multicore and GPU processing. This book will help you tackle increasingly challenging computer vision problems by providing a number of recipes that you can use to improve your applications.
In this book, you will learn how to process an image by manipulating pixels and analyze an image using histograms. Then, we'll show you how to apply image filters to enhance image content and exploit the image geometry in order to relay different views of a pictured scene. We’ll explore techniques to achieve camera calibration and perform a multiple-view analysis.
Later, you’ll work on reconstructing a 3D scene from images, converting low-level pixel information to high-level concepts for applications such as object detection and recognition. You’ll also discover how to process video from files or cameras and how to detect and track moving objects. Finally, you'll get acquainted with recent approaches in deep learning and neural networks.
By the end of the book, you’ll be able to apply your skills in OpenCV to create computer vision applications in various domains.
Table of Contents (11 chapters)
Preface
Free Chapter
I/O and GUI
Matrices, Colors, and Filters
Contours and Segmentation
Object Detection and Machine Learning
Deep Learning
Linear Algebra
Detectors and Descriptors
Image and Video Processing
Multiple View Geometry
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