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Hands-On Computer Vision with TensorFlow 2

You're reading from   Hands-On Computer Vision with TensorFlow 2 Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781788830645
Length 372 pages
Edition 1st Edition
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Authors (2):
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Eliot Andres Eliot Andres
Author Profile Icon Eliot Andres
Eliot Andres
Benjamin Planche Benjamin Planche
Author Profile Icon Benjamin Planche
Benjamin Planche
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Table of Contents (16) Chapters Close

Preface 1. Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision FREE CHAPTER
2. Computer Vision and Neural Networks 3. TensorFlow Basics and Training a Model 4. Modern Neural Networks 5. Section 2: State-of-the-Art Solutions for Classic Recognition Problems
6. Influential Classification Tools 7. Object Detection Models 8. Enhancing and Segmenting Images 9. Section 3: Advanced Concepts and New Frontiers of Computer Vision
10. Training on Complex and Scarce Datasets 11. Video and Recurrent Neural Networks 12. Optimizing Models and Deploying on Mobile Devices 13. Migrating from TensorFlow 1 to TensorFlow 2 14. Assessments 15. Other Books You May Enjoy

Adding some machine learning on top

It soon appeared clear, however, that extracting robust, discriminative features was only half the job for recognition tasks. For instance, different elements from the same class can look quite different (such as different-looking dogs) and, as a result, share only a small set of common features. Therefore, unlike image-matching tasks, higher-level problems such as semantic classification cannot be solved by simply comparing pixel features from query images with those from labeled pictures (such a procedure can also become sub-optimal in terms of processing time if the comparison has to be done with every image from a large labeled dataset).

This is where machine learning come into play. With an increasing number of researchers trying to tackle image classification in the 90s, more statistical ways to discriminate images based on their features started to appear. Support vector machines (SVMs), which were standardized by Vladimir Vapnik and Corinna...

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