What is Computer Vision?

Computer vision is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do.

Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions.Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that can interface with other thought processes and elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.

As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems.

Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, and image restoration.

Overview

The goal of Computer Vision is to emulate human vision using digital images through three main processing components, executed one after the other:

1. Image acquisition
2. Image processing
3. Image analysis and understanding

As our human visual understanding of world is reflected in our ability to make decisions through what we see, providing such a visual understanding to computers would allow them the same power 

Image acquisition
Image acquisition is the process of translating the analog world around us into binary data composed of zeros and ones, interpreted as digital images.

Different tools have been created to build such data sets:

1. Webcams & embedded cameras
2. Digital compact cameras & DSLR

3. Consumer 3D cameras & laser range finders

Image processing

The second component of Computer Vision is the low-level processing of images. Algorithms are applied to the binary data acquired in the first step to infer low-level information on parts of the image. This type of information is characterized by image edges, point features or segments, for example. They are all the basic geometric elements that build objects in images.

This second step usually involves advanced applied mathematics algorithms and techniques.

Low-level image processing algorithms include

1. Edge detection
2. Segmentation
3. Classification

4. Feature detection and matching

Image processing

The second component of Computer Vision is the low-level processing of images. Algorithms are applied to the binary data acquired in the first step to infer low-level information on parts of the image. This type of information is characterized by image edges, point features or segments, for example. They are all the basic geometric elements that build objects in images.

This second step usually involves advanced applied mathematics algorithms and techniques.

Low-level image processing algorithms include:
1. Edge detection
2. Segmentation
3. Classification

4. Feature detection and matching

Applications of computer vision

Techniques developed for Computer Vision have many applications in the fields of robotics, human-computer interaction and visualization, to name a few:
1. Motion recognition
2. Augmented reality
3. Autonomous cars
4. Domestic/service robots

5. Image restoration such as denoising

Challenges in computer vision

When developing Computer Vision algorithms, one has to face different issues and challenges, related to the very nature of the data or event the application to be created and its context:
1. Noisy or incomplete data
2. Real-time processing
3. Limited resources: power, memory

Current research is focused on addressing these challenges to make the algorithms more robust and efficient in difficult conditions.



Credits
Richard Szeliski, “Computer Vision: Algorithms and Applications”, Springer Science & Business Media, 2010

Karen Reed, “8 Ways Technology is Improving your Health”, Positive Health Wellness, 2017