Computer vision enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information.[1] It works much the same as human vision on telling objects apart, how far away they are, whether they are moving and whether there is something wrong in an image. Computer vision trains machines to perform these functions and do it in much less time with cameras, data and algorithms.

Computer vision is a multidisciplinary field that could broadly be called a subfield of artificial intelligence and machine learning, which may involve the use of specialized methods and make use of general learning algorithms. Some problems in vision may be easily addressed with a hand-crafted statistical method, whereas another may require a large and complex ensemble of generalized machine learning algorithms.[2] Machine learning has been used in computer vision and has improved computer vision’s functions of tracking and recognition, and it offers effective methods acquiring and processing image.

Computer Vision Applied in Various Industries

Computer vision systems are used to improve the security of high-value assets by implementing biometric scanning and facial recognition systems. The most popular use-case of facial recognition is smartphones security, as computer vision systems can recognize different patterns in the retinas and irises of humans. Besides, more advanced use-cases of facial recognition are businesses or residential security systems that use individuals’ physiological features for identity verification.

The automotive industry is witnessing a paradigm shift from human-driven or conventional vehicles into AI-powered or self-driving cars. The application of computer vision systems in self-driving cars is expected to boost the growth of the market, owing to the need for decision-making ability.

The manufacturing industry is experiencing the most extensive use of automation and robotics. As manufacturing facilities are transitioning towards fully automated manufacturing, the requirement for more intelligent systems to monitor industrial processes and outcomes is increasing. While IoT (Internet of Things) is revolutionizing the manufacturing sector and making industrial operations more autonomous, computer vision is further assisting in improving them in the form of machine vision.[5]

Common Computer Vision Tasks

Computer vision extracts high-dimensional data from the real world in order to produce numerical or symbolic information and make decisions. The most popular computer vision tasks include:

Image Classification

Given a group of images, the task is to classify them into a set of predefined classes using solely a set of sample images that have already been classified. As opposed to complex topics such as object detection and image segmentation, which have to localize (or give positions for) the features they detect, image classification deals with processing the entire image as a whole and assigning a specific label to it.[4]

Image Segmentation

Image segmentation is the division of an image into subparts or sub-objects to demonstrate that the machine can discern an object from the background and/or another object in the same image. A segment of an image represents a particular class of object that the neural network has identified in an image, represented by a pixel mask that can be used to extract it.[4]

Object Detection

Object detection refers to the application of machine vision to detect objects in a natural environment and localize them via bounding boxes with the help of visual data. It looks for class-specific details in an image or a video and detects them when they appear. These classes can be animals, humans, or anything on which the detection model has been trained. Often object detection is accompanied by Object Recognition, also known as Object Classification. [4]

Facial Recognition

Facial Recognition is a subpart of object detection where the primary object being detected is the human face. Facial recognition systems search for common features and landmarks in faces such as nose, eyes, and mouth and classify with the help of these features and the positioning of these landmarks.[4]

Edge Detection

Edge detection is the task of detecting boundaries in objects. It is performed by detecting sharp changes or discontinuities in the brightness of the image. Often used as a pre-processing step for many tasks, edge detection is primarily done by traditional image processing-based algorithms with specially designed edge detection filters.[4]

Image Restoration

Image restoration refers to the restoration or the reconstruction of faded and old image hard copies that have been captured and stored in an improper manner, leading to loss of quality of the image.

In Image in-painting, damaged parts of an image are filled with the help of generative models that make an estimate of what the image is trying to convey. Often the restoration process is followed by a colorization process that colors the pictures in black and white in the most realistic manner possible.[4]

Feature Matching

Features in computer vision are regions of an image that tell us the most about a particular object in the image. While edges are strong indicators of object detail and therefore important features, much more localized and sharp details also serve as features. Feature matching helps us to relate the features of one image with those of another image of a similar region.

Scene Reconstruction

One of the most complex problems of computer vision, scene reconstruction is the digital 3D reconstruction of an object from a photograph. Most algorithms in scene reconstruction roughly work by forming a point cloud at the surface of the object and reconstructing a mesh from this point cloud. [4]

Video Motion Analysis

Video motion analysis is a task in machine vision that refers to the study of moving objects or animals and the trajectory of their bodies. Motion analysis as a whole is a combination of many subtasks, particularly object detection, tracking, and segmentation, and pose estimation. Human motion analysis is used in areas such as sports, medicine, intelligent video analytics, and physical therapy. Motion analysis is also used in manufacturing and to count and track microorganisms such as bacteria and viruses.[4]

Computer Vision Market Size

The global computer vision market size was valued at USD 11.32 billion in 2020 and is expected to expand at a compound annual growth rate (CAGR) of 7.3% from 2021 to 2028. The recent advancements in computer have widened the scope for computer vision systems in various industries, including education, healthcare, robotics, consumer electronics, retail, manufacturing, and security & surveillance, among others. For instance, image captioning in social media platforms is one of the most popular applications of computer vision.[5]

Some Applications of Computer Vision
Medical Image Segmentation

Image segmentation is utilized for medical scan analysis and allows the highly precise detection of pathological elements. Timeliness and accuracy in diagnosing different forms of cancer are vital. It has a great impact on how early medical professionals start the correct treatment course. The work on computer vision-based medical apps is one of the top priorities of many companies worldwide. [6]

Object Detection and Tracking in Sports

Computer vision is now becoming popular in sports. For tracking athletes and their performance, new ways of action quality assessment can be used across different sports. For instance, in figure skating and gymnastics, the presentation score is a part of the total score. Thus, the accuracy of action evaluation can be enhanced. Computer vision can also assist in the post-game analysis. And for marketing purpose, it can be employed to detect and track the visibility of brand logos in an event broadcast. [6]

Image and Video Processing in Agriculture

Computer vision is also applied in agriculture production. Augmented reality(AR) can enable the gathering and analyzing data by enhancing images and videos. With the help of computer vision, automated monitoring systems can alter many traditional processes.

For example, wine-making, being very sensitive to soil conditions, employ AI-led solutions to monitor critical data and predict possible diseases or damage to vineyards. Automated drones supplied with infrared cameras can take images from above. After that, such computer vision techniques as object detection, semantic, and instance segmentation allow making a comprehensive analysis of retrieved images. Technologies help prevent potential productivity losses as well as suggest more favorable land areas for cultivating vines. [6]

Future Outlook

Computer vision is already a mature technology that provides huge benefits and wide applications. However, the innovation of AI technology has not stopped, and it is believed that the capability of computer vision will continue to be improved in the future. With further research on and refinement of the technology, computer vision technologies will be easier to train and be able to discern more from images than they do now. As computer vision is a subset of AI technology, it is likely to be used in conjunction with other technologies, developing wider range of functions.