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Learning OpenCV 3 Computer Vision with Python (Update)

Learning OpenCV 3 Computer Vision with Python (Update)

By : Joe Minichino, Joseph Howse
2.1 (7)
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Learning OpenCV 3 Computer Vision with Python (Update)

Learning OpenCV 3 Computer Vision with Python (Update)

2.1 (7)
By: Joe Minichino, Joseph Howse

Overview of this book

OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV's API will enable the development of all sorts of real-world applications, including security and surveillance. Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application.
Table of Contents (11 chapters)
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6
6. Retrieving Images and Searching Using Image Descriptors
10
Index

Chapter 4. Depth Estimation and Segmentation

This chapter shows you how to use data from a depth camera to identify foreground and background regions, so that we can limit an effect to only the foreground or only the background. As prerequisites, we need a depth camera, such as Microsoft Kinect, and we need to build OpenCV with support for our depth camera. For build instructions, see Chapter 1, Setting Up OpenCV.

We'll deal with two main topics in this chapter: depth estimation and segmentation. We will explore depth estimation with two distinct approaches: firstly, by using a depth camera (a prerequisite of the first part of the chapter), such as Microsoft Kinect, and then, by using stereo images, for which a normal camera will suffice. For instructions on how to build OpenCV with support for depth cameras, see Chapter 1, Setting Up OpenCV. The second part of the chapter is about segmentation, the technique that allows us to extract foreground objects from an image.

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