Book Image

Practical Computer Vision

By : Abhinav Dadhich
Book Image

Practical Computer Vision

By: Abhinav Dadhich

Overview of this book

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.
Table of Contents (12 chapters)

3D Computer Vision

In the last few chapters, we have discussed the extraction of an object and semantic information from images. We saw how good feature extraction leads to object detection, segmentation, and tracking. This information explicitly requires the geometry of the scene; in several applications, knowing the exact geometry of a scene plays a vital role.

In this chapter, we will see a discussion leading to the three-dimensional aspects of an image. Here, we will begin by using a simple camera model to understand how pixel values and real-world points are linked correspondingly. Later, we will study methods for computing depth from images and also methods of computing the motion of a camera from a sequence of images.

We will cover the following topics in the chapter:

  • RGDB dataset
  • Applications to extract features from images
  • Image formation
  • Aligning of images
  • Visual odometry...