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)

Summary

In this chapter, we saw a brief overview of computer vision with basic IO operations on images. Though it is a vast field, there are always exciting applications that can be built using computer vision techniques. This book tries to bridge the gap between theory and a practical approach to several of the popular algorithms. Further, in this book, we will begin with understanding more basic image operations that can perform filtering and transformations. Extending these basic techniques, we will then see what comprises of feature and how to compute them.

Following the introduction to computer vision in this chapter, we will start setting up libraries and an environment in the next chapter. These libraries will be used across the book. The datasets introduced in the next chapter can be the starting point for several algorithms further.