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Practical Computer Vision
Practical Computer Vision

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

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Practical Computer Vision

Libraries, Development Platform, and Datasets

In this chapter, we will be setting up a development environment to help run codes for the book as well as for generic development and also introduce various datasets for computer vision. Since there are several standard libraries which are used both for studying computer vision and in the industry for deployment, it becomes trivial to also use them in learning path. As we study the various sub-topics of computer vision in further chapters, we will be able to directly implement the codes introduced then rather than getting stuck in installations and other library dependencies.

This chapter is divided into two major sections:

  • Firstly we will be setting up python based environment such Anaconda
  • We will then setup OpenCV and various forms of its installations
  • For deep learning, we will also setup Keras and TensorFlow
...

Libraries and installation

Before we begin, it is required that we install each library. There are two major methods of installing a library:

  • We download the source code and build binaries by compiling the code
  • We can directly download binaries and put them in relevant directories

While downloading pre-built binaries is a faster method, however, due to the difference of platforms or non-availability of binaries may force to build a library from source. If readers are using different OS then the mentioned in the following sections, they might come across such a situation. Once installed a library, it can be used with programs or other libraries.

Since it is crucial to have libraries that are not affected by other installations, we will be using Python-based environments in most of the book. This helps in keeping track of libraries installed and also separates different environment...

Datasets

In computer vision, datasets play a key role in developing efficient applications. Also, now, with the availability of large open source datasets, it has become much easier to create best performing models for computer vision tasks. In this section, we will see several datasets for computer vision.

ImageNet

ImageNet is one of the largest annotated datasets for computer vision. The data is arranged according to a hierarchical order. There are 1,000 classes with 1.4 million images overall. Though the images are for non-commercial use, ImageNet is still one of the most popular datasets when it comes to learning computer vision. Especially in deep learning, the dataset is used to create image classification models due...

Summary

In this chapter, we learned how to install the different library files of Python, Keras, and TensorFlow. In order to use several code snippets in further chapters, these libraries will be sufficient. We also had a look at different datasets like ImageNet, MNIST, CIFAR-10, MSCOCO and TUM RGBD datasets. These datasets are the backbone for computer vision applications since the ability of several software that we develop directly depends on the availability of these datasets.

In next chapter, we will begin with more in-depth image analysis by introducing different types of filters and also learn transformations on image such as translation, rotation or affine.

References

  • Krizhevsky, Alex, and Geoffrey Hinton. Learning multiple layers of features from tiny images. (2009).
  • Lin, Tsung-Yi, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pp. 740-755. Springer, Cham, 2014.
  • Sturm, Jürgen, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. A benchmark for the evaluation of RGB-D SLAM systems. In Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, pp. 573-580. IEEE, 2012.
  • Everingham Mark, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision 88, no. 2 (2010): 303-338.
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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.

Who is this book for?

This book is for machine learning practitioners and deep learning enthusiasts who want to understand and implement various tasks associated with Computer Vision and image processing in the most practical manner possible. Some programming experience would be beneficial while knowing Python would be an added bonus.

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

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Publication date : Feb 05, 2018
Length: 234 pages
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Publication date : Feb 05, 2018
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Table of Contents

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

Customer reviews

Rating distribution
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.5
(2 Ratings)
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PG Feb 09, 2019
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
This book has a lot of mistakes, grammatical and in code. The concepts are described at a very high level, and the explanations feels incomplete. Its certainly not for beginners in computer vision. I'm at a mid-level of CV and I didn't find too much benefit from it either.
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Amazon Customer Sep 20, 2018
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I got the book as per PACK publishing . If you are looking for computer vision introduction to advanced version this is not the book. The book is not filled with contents , no proper line by line code explanation . The author has copy pasted the code from the internet and doesn't go forward in explaining what computer vision acutally is .AUTHOR HAS NO IDEA WHAT COMPUTER VISION IS ? DONT BUY THIS BOOK. IT IS VERY DISAPPOINTING.
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