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  • Learning OpenCV 4 Computer Vision with Python 3
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Learning OpenCV 4 Computer Vision with Python 3

Learning OpenCV 4 Computer Vision with Python 3

By : Joseph Howse, Joe Minichino
4.1 (14)
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Learning OpenCV 4 Computer Vision with Python 3

Learning OpenCV 4 Computer Vision with Python 3

4.1 (14)
By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science, encompassing diverse applications and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You’ll be able to put theory into practice by building apps with OpenCV 4 and Python 3. You’ll start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you’ll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds. From taking you through image processing, video analysis, and depth estimation and segmentation, to helping you gain practice by building a GUI app, this book ensures you’ll have opportunities for hands-on activities. Next, you’ll tackle two popular challenges: face detection and face recognition. You’ll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed. Later, you’ll develop your skills in 3D tracking and augmented reality. Finally, you’ll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age. By the end of this book, you’ll have the skills you need to execute real-world computer vision projects.
Table of Contents (13 chapters)
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Depth Estimation and Segmentation

This chapter begins by showing you how to use data from a depth camera to identify foreground and background regions, such that we can limit an effect to only the foreground or only the background.

After covering depth cameras, the chapter proceeds with other techniques for depth estimation, namely, stereo imaging and structure from motion (SfM). The latter techniques do not require a depth camera; instead, they rely on capturing images of a subject from multiple perspectives with one or more ordinary cameras.

Finally, the chapter covers segmentation techniques that allow us to extract foreground objects from a single image. By the end of the chapter, you will learn several ways to segment an image into multiple depths or multiple objects. Specifically, we will cover the following topics:

  • Using a depth camera to capture depth maps, point cloud...
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