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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

Applying LBP to texture recognition

Now that we know the basics of LBP, we will apply it to a texture recognition example. For this example, 11 trained images and 7 test images that are 50 x 50 in size have been developed into the following classes:

  • Trained image
  • Pattern image (7)
  • Plain image (4)
  • Test image
  • Pattern image (4)
  • Plain image (3)

Steps 1 through 5 from the Generating an LBP pattern section are applied, and then each test image's LBP histogram is compared with all of the trained images to find the best match. Although different histogram comparison methods have been used, for this analysis, the Chi-Square test is going to be used as the principal method for determining the match. The final summary output with correct matches is shown with a green line, whereas incorrect matches will be shown with a red line. The solid line is the first match with a minimum distance...

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