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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

Machine Learning for Computer Vision

In this chapter, we will get an overview of the relevant machine learning theory and tools that are useful when developing applications such as image classification, object detection, and so on. With widespread communication tools and the wide availability of camera sensors, we are now bombarded with large amounts of image data. Utilizing this data to create computer vision applications requires an understanding of some basic machine learning concepts.

Let's begin by first explaining what machine learning is, and then we will see the different types of algorithms in it.

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