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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

Datasets

In computer vision, datasets play a key role in developing efficient applications. Also, now, with the availability of large open source datasets, it has become much easier to create best performing models for computer vision tasks. In this section, we will see several datasets for computer vision.

ImageNet

ImageNet is one of the largest annotated datasets for computer vision. The data is arranged according to a hierarchical order. There are 1,000 classes with 1.4 million images overall. Though the images are for non-commercial use, ImageNet is still one of the most popular datasets when it comes to learning computer vision. Especially in deep learning, the dataset is used to create image classification models due...

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