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

Practical Computer Vision: Extract insightful information from images using TensorFlow, Keras, and OpenCV
Practical Computer Vision: Extract insightful information from images using TensorFlow, Keras, and OpenCV by Abhinav Dadhich
English | 2018 | ISBN: 1788297684 | 234 Pages | True PDF, EPUB | 173 MB

A practical guide designed to get you from basics to current state of art in computer vision systems.
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