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Practical Computer Vision

You're reading from   Practical Computer Vision Extract insightful information from images using TensorFlow, Keras, and OpenCV

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788297684
Length 234 pages
Edition 1st Edition
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Author (1):
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Abhinav Dadhich Abhinav Dadhich
Author Profile Icon Abhinav Dadhich
Abhinav Dadhich
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Table of Contents (12) Chapters Close

Preface 1. A Fast Introduction to Computer Vision FREE CHAPTER 2. Libraries, Development Platform, and Datasets 3. Image Filtering and Transformations in OpenCV 4. What is a Feature? 5. Convolutional Neural Networks 6. Feature-Based Object Detection 7. Segmentation and Tracking 8. 3D Computer Vision 9. Mathematics for Computer Vision 10. Machine Learning for Computer Vision 11. Other Books You May Enjoy

3D Computer Vision

In the last few chapters, we have discussed the extraction of an object and semantic information from images. We saw how good feature extraction leads to object detection, segmentation, and tracking. This information explicitly requires the geometry of the scene; in several applications, knowing the exact geometry of a scene plays a vital role.

In this chapter, we will see a discussion leading to the three-dimensional aspects of an image. Here, we will begin by using a simple camera model to understand how pixel values and real-world points are linked correspondingly. Later, we will study methods for computing depth from images and also methods of computing the motion of a camera from a sequence of images.

We will cover the following topics in the chapter:

  • RGDB dataset
  • Applications to extract features from images
  • Image formation
  • Aligning of images
  • Visual odometry...
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