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Practical Computer Vision
Practical Computer Vision

Practical Computer Vision: Extract insightful information from images using TensorFlow, Keras, and OpenCV

Profile Icon Abhinav Dadhich
By Abhinav Dadhich
$20.98 $29.99
Book Feb 2018 234 pages 1st Edition
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$38.99
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Profile Icon Abhinav Dadhich
By Abhinav Dadhich
$20.98 $29.99
Book Feb 2018 234 pages 1st Edition
eBook
$20.98 $29.99
Print
$38.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$20.98 $29.99
Print
$38.99
Subscription
Free Trial
Renews at $19.99p/m

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Practical Computer Vision

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Key benefits

  • Master the different tasks associated with Computer Vision and develop your own Computer Vision applications with ease
  • Leverage the power of Python, Tensorflow, Keras, and OpenCV to perform image processing, object detection, feature detection and more
  • With real-world datasets and fully functional code, this book is your one-stop guide to understanding Computer Vision

Description

In this book, you will find several recently proposed methods in various domains of computer vision. You will start by setting up the proper Python environment to work on practical applications. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. Using these libraries, you'll start to understand the concepts of image transformation and filtering. You will find a detailed explanation of feature detectors such as FAST and ORB; you'll use them to find similar-looking objects. With an introduction to convolutional neural nets, you will learn how to build a deep neural net using Keras and how to use it to classify the Fashion-MNIST dataset. With regard to object detection, you will learn the implementation of a simple face detector as well as the workings of complex deep-learning-based object detectors such as Faster R-CNN and SSD using TensorFlow. You'll get started with semantic segmentation using FCN models and track objects with Deep SORT. Not only this, you will also use Visual SLAM techniques such as ORB-SLAM on a standard dataset. By the end of this book, you will have a firm understanding of the different computer vision techniques and how to apply them in your applications.

What you will learn

  • •Learn the basics of image manipulation with OpenCV
  • •Implement and visualize image filters such as smoothing, dilation, histogram equalization, and more
  • •Set up various libraries and platforms, such as OpenCV, Keras, and Tensorflow, in order to start using computer vision, along with appropriate datasets for each chapter, such as MSCOCO, MOT, and Fashion-MNIST
  • •Understand image transformation and downsampling with practical implementations.
  • •Explore neural networks for computer vision and convolutional neural networks using Keras
  • •Understand working on deep-learning-based object detection such as Faster-R-CNN, SSD, and more
  • •Explore deep-learning-based object tracking in action
  • •Understand Visual SLAM techniques such as ORB-SLAM

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Feb 5, 2018
Length 234 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781788297684
Category :

What do you get with eBook?

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Product Details

Publication date : Feb 5, 2018
Length 234 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781788297684
Category :

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Table of Contents

12 Chapters
Preface Chevron down icon Chevron up icon
1. A Fast Introduction to Computer Vision Chevron down icon Chevron up icon
2. Libraries, Development Platform, and Datasets Chevron down icon Chevron up icon
3. Image Filtering and Transformations in OpenCV Chevron down icon Chevron up icon
4. What is a Feature? Chevron down icon Chevron up icon
5. Convolutional Neural Networks Chevron down icon Chevron up icon
6. Feature-Based Object Detection Chevron down icon Chevron up icon
7. Segmentation and Tracking Chevron down icon Chevron up icon
8. 3D Computer Vision Chevron down icon Chevron up icon
9. Mathematics for Computer Vision Chevron down icon Chevron up icon
10. Machine Learning for Computer Vision Chevron down icon Chevron up icon
11. Other Books You May Enjoy Chevron down icon Chevron up icon
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