Learn Computer Vision Using OpenCV: With Deep Learning CNNs and RNNs

By Sunila Gollapudi

Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer vision such as image and object detection, tracking and motion analysis and their applications with examples. 
The author starts with an introduction to computer vision followed by setting up OpenCV from scratch using Python. The next section discusses specialized image processing and segmentation and how images are stored and processed by a computer. This involves pattern recognition and image tagging using the OpenCV library. Next, you’ll work with object detection, video storage and interpretation, and human detection using OpenCV. Tracking and motion is also discussed in detail. The book also discusses creating complex deep learning models with CNN and RNN. The author finally concludes with recent applications and trends in computer vision.
After reading this book, you will be able to understand and implement computer vision and its applications with OpenCV using Python. You will also be able to create deep learning models with CNN and RNN and understand how these cutting-edge deep learning architectures work.
What You Will Learn

• Understand what computer vision is, and its overall application in intelligent automation systems
  
• Discover the deep learning techniques required to build computer vision applications
  
• Build complex computer vision applications using the latest techniques in OpenCV, Python, and NumPy
  
• Create practical applications and implementations such as face detection and recognition, handwriting recognition, object detection, and tracking and motion analysis
  

Who This Book Is For Those who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications. 

• Language: English
• Publisher: Apress
• Release date: Apr 26, 2019
• ISBN: 9781484242612

Book preview

© Sunila Gollapudi 2019

Sunila GollapudiLearn Computer Vision Using OpenCVhttps://doi.org/10.1007/978-1-4842-4261-2_1

1. Artificial Intelligence and Computer Vision

Sunila Gollapudi¹ 

(1)

Hyderabad, Telangana, India

The field of artificial intelligence, and its application in day-to day life, has seen remarkable evolution in the past three to five years. Artificial intelligence (AI) is an enabler that potentially facilitates machines doing everything that humans can do. This includes perceiving, reasoning, rationalizing, and problem-solving while working within a context or interacting with the environment with more efficiency and accuracy. Here, the word context means the domain or the business where the problem is dealt with, for example online shopping, social media, insurance, manufacturing, and others. Interacting with the environment could mean that computers or machines work along with the humans or take input from external stimuli and adjust their behaviors accordingly. Computer vision, which enables computers and machines to see and understand the world around them, specifically has become a game-changer for how and where machines can be used and AI can be adopted.

This chapter covers the larger AI dream that is all about touching both the personal and professional lives of humans and how computer vision among other areas is a key enabler. Also, you’ll learn about a few real-world applications, challenges, and technology tools such as OpenCV that help in complex implementations.

The following topics are covered in detail in this chapter:

Artificial intelligence and its landscape, which includes a basic definition and the usage context of robotics, intelligent automation, natural language processing, expert systems, speech recognition, computer vision, and machine learning

Computer vision, including its challenges and applications in today’s world

Computer vision architecture and tools, including what images are and how to understand and manipulate key attributes of images

A sneak-peak into the core building blocks of computer vision and aspects such as image manipulation and segmentation, object detection, motion analysis and tracking, and others

A brief introduction to optical character recognition, intelligent character recognition, and optimal mark