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Mastering Computer Vision with TensorFlow 2.x

You're reading from   Mastering Computer Vision with TensorFlow 2.x Build advanced computer vision applications using machine learning and deep learning techniques

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Product type Paperback
Published in May 2020
Publisher Packt
ISBN-13 9781838827069
Length 430 pages
Edition 1st Edition
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Author (1):
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Krishnendu Kar Krishnendu Kar
Author Profile Icon Krishnendu Kar
Krishnendu Kar
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Introduction to Computer Vision and Neural Networks
2. Computer Vision and TensorFlow Fundamentals FREE CHAPTER 3. Content Recognition Using Local Binary Patterns 4. Facial Detection Using OpenCV and CNN 5. Deep Learning on Images 6. Section 2: Advanced Concepts of Computer Vision with TensorFlow
7. Neural Network Architecture and Models 8. Visual Search Using Transfer Learning 9. Object Detection Using YOLO 10. Semantic Segmentation and Neural Style Transfer 11. Section 3: Advanced Implementation of Computer Vision with TensorFlow
12. Action Recognition Using Multitask Deep Learning 13. Object Detection Using R-CNN, SSD, and R-FCN 14. Section 4: TensorFlow Implementation at the Edge and on the Cloud
15. Deep Learning on Edge Devices with CPU/GPU Optimization 16. Cloud Computing Platform for Computer Vision 17. Other Books You May Enjoy

Content Recognition Using Local Binary Patterns

Local Binary Patterns (LBP) was first introduced in the International Pattern Recognition Conference in 1994 by Timo Ojala, Matti Pietik äinen, and David Harwood in the paper Performance evaluation of texture measures with classification based on Kullback discrimination of distributions (https://ieeexplore.ieee.org/document/576366).

In this chapter, you will learn how to create an LBP image type binary feature descriptor and the LBP histogram for the classification of textured and non-textured images. You will learn about the different methods you can use to calculate the differences between histograms in order to find a match between various images and how to tune LBP parameters to optimize its performance.

This chapter will cover the following topics:

  • Processing images using LBP
  • Applying LBP to texture recognition
  • Matching...
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