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)

A Fast Introduction to Computer Vision

Computer vision applications have become quite ubiquitous in our lives. The applications are varied, ranging from apps that play Virtual Reality (VR) or Augmented Reality (AR) games to applications for scanning documents using smartphone cameras. On our smartphones, we have QR code scanning and face detection, and now we even have facial recognition techniques. Online, we can now search using images and find similar looking images. Photo sharing applications can identify people and make an album based on the friends or family found in the photos. Due to improvements in image stabilization techniques, even with shaky hands, we can create stable videos.

With the recent advancements in deep learning techniques, applications like image classification, object detection, tracking, and so on have become more accurate and this has led to the development of more complex autonomous systems, such as drones, self-driving cars, humanoids, and so on. Using deep learning, images can be transformed into more complex details; for example, images can be converted into Van Gogh style paintings.

Such progress in several domains makes a non-expert wonder, how computer vision is capable of inferring this information from images. The motivation lies in human perception and the way we can perform complex analyzes of the environment around us. We can estimate the closeness of, structure and shape of objects, and estimate the textures of a surface too. Even under different lights, we can identify objects and even recognize something if we have seen it before.

Considering these advancements and motivations, one of the basic questions that arises is what is computer vision? In this chapter, we will begin by answering this question and then provide a broader overview of the various sub-domains and applications within computer vision. Later in the chapter, we will start with basic image operations.