Computer vision is a field of study that focuses on how computers can be made to interpret and understand the visual world. This field has a wide range of applications, from recognizing objects in images and videos, to detecting and tracking objects in real-time, to generating virtual environments and more.
The goal of computer vision is to enable machines to understand and process visual information in the same way that humans do. To achieve this, computer vision algorithms use various techniques such as image processing, pattern recognition, machine learning and deep learning to extract meaningful information from images and videos.
In this chapter, we will introduce the fundamentals of computer vision and explore some of the key concepts and techniques used in the field. We will also examine some real-world applications of computer vision and discuss how these algorithms are used in practice.
Where to Learn More#
I’ve covered computer vision in-depth in the following course:
Deep Learning: Advanced Computer Vision (GANs, SSD, +More!)
Computer vision is also discussed in the following courses:
Deep Learning: Convolutional Neural Networks in Python
Deep Learning: GANs and Variational Autoencoders
Tensorflow 2.0: Deep Learning and Artificial Intelligence