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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

Feature-Based Object Detection

In the previous chapter, we understood the importance of and how to model deep layered feature extraction using Convolutional Neural Networks (CNNs). In this chapter, we will learn how to model a CNN to detect where the object in the image is and also classify the object in one of our pre-decided categories.

In this chapter:

  • We will begin with a general discussion on image recognition and what is object detection
  • A working example of the popular techniques for face detection using OpenCV
  • Object detection using two-stage models such as Faster-RCNN
  • Object detection using one-stage model such as SSD
  • The major part of this chapter will be discussing deep learning-based object detectors and explaining them using a code for the demo
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