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Practical Computer Vision

You're reading from Practical Computer Vision Extract insightful information from images using TensorFlow, Keras, and OpenCV

Product type Paperback

Published in Feb 2018

Publisher Packt

ISBN-13 9781788297684

Length 234 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Computer Vision

Author (1):

Abhinav Dadhich

View More author details

Table of Contents (18) Chapters

Title Page

Copyright and Credits

Dedication

Packt Upsell

Contributors

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

1. Other Books You May Enjoy

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Index

Datasets and libraries

We will be continuing the use of OpenCV and NumPy for image processing. For deep learning, we will use Keras with the TensorFlow backend. For segmentation, we will be using the Pascal VOC dataset. This has annotations for object detection, as well as segmentation. For tracking, we will use the MOT16 dataset, which consists of an annotated sequence of images from video. We will mention how to use the code in the sections where it is used.

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Authors (1)

Abhinav Dadhich

Abhinav Dadhich

Abhinav Dadhich is a Researcher and Application Developer on deep learning at Abeja Inc. Tokyo. His day is often filled with designing deep learning models for computer vision applications like image classification, object detection, segmentation etc. His passion lies in understanding and replicating human vision system. Previously, he has worked on 3D mapping and robot navigation. He has graduated with B.Tech. in EE from IIT Jodhpur, India and has done his M.Eng. in Information Science from NAIST, Japan. He puts up notes and codes for several topics on GitHub profile.

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