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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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Toc

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

Dataset and libraries

In this chapter, we will be using OpenCV for most of the applications. In the last section, for Visual Simultaneous Localization and Mapping (vSLAM) techniques, we will see the use of an open source repository; directions for its use are mentioned in the section. The dataset is the RGBD dataset, consisting of a sequence of images captured using RGB and a depth camera. To download this dataset, visit the following link and download the fr1/xyz tar file: https://vision.in.tum.de/data/datasets/rgbd-dataset/download.

Alternatively, use the following (Linux only) command in a Terminal:

wget https://vision.in.tum.de/rgbd/dataset/freiburg1/rgbd_dataset_freiburg1_xyz.tgz
tar -xvf rgbd_dataset_freiburg1_xyz.tgz
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