Introduction to Computer Vision (Ultimate Guide 2023)
Today, computer vision is one of the most important subfields of Artificial Intelligence and machine learning, given its wide variety of applications and tremendous potential. It aims to replicate the powerful capabilities of human vision.
“The eyes, chico. They never lie” — Tony Montana
But what exactly is computer vision? What are the typical tasks of computer vision? How is it currently applied in different industries? What is the important role of Theos AI in relation to computer vision?
What is Computer Vision?
Computer vision is the field aiming at giving machines the power to analyze and understand images and videos. While the types of algorithms that make this possible have existed in various forms since the 1960s, recent advances in machine learning and computational power, along with the accumulation of high amounts of image data, have driven significant improvements in how well our machines can see the world.
Typical Tasks in Computer Vision
Computer vision is composed of a broad set of diverse tasks, combined to achieve highly sophisticated applications. The most common tasks in computer vision are image classification, object detection, semantic and instance segmentation, pose estimation and face recognition. Some of the newest tasks include image inpainting, face generation, and text to image generation. The latter is the task performed by DALLE-2, the incredible AI artist created by OpenAI this year.
What is Image Classification?
Image Classification is a fundamental computer vision task that attempts to comprehend an entire image as a whole. The goal is to classify the image by assigning it to a specific label.
Example of Image Classification
What is Object Detection?
Object detection is a computer vision technique for locating instances of objects in images or videos. Object detection algorithms typically leverage machine learning or deep learning to produce meaningful results. When humans look at images or videos, we can recognize and locate objects of interest in a matter of milliseconds. The goal of object detection is to replicate this cognitive task in machines by using artificial neural networks.
What is Semantic and Instance Segmentation?
Semantic segmentation, or image segmentation, is the task of clustering parts of an image together which belong to the same object class.
Example of Semantic Segmentation
Instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.
Example of Instance Segmentation
What is Pose Estimation?
Pose Estimation is a general problem in computer vision that attempts to detect the position and orientation of objects within images and videos.
Example of Pose Estimation
What is Face Recognition?
Face recognition is the task of making a positive identification of a face in an image or video frame against a pre-existing database of faces.
Example of Face Recognition
What is Image Inpainting?
Image Inpainting is a task of reconstructing missing regions in an image. It is an important problem in computer vision and an essential functionality in many imaging and graphics applications, e.g. object removal, image restoration, manipulation, re-targeting, compositing, and image-based rendering.
Example of wrinkle removal through Image Inpainting
What is Face Generation?
Face generation is the task of generating (or interpolating) new faces from an existing dataset.
Example of Face Generation
What is Text-to-Image Generation?
Text to image generation is the task of creating an output image from a given text input.
Example of text to image generation, performed by DALLE-2
Industry Applications
Humans are not only capable of understanding scenes, but also of interpreting calligraphy, impressionistic or abstract paintings and, with a little training, the 2D ultrasound of a baby. In that sense, the field of computer vision is particularly complex, possessing an immense range of practical applications. The beauty of innovation that relies on artificial intelligence, and computer vision in particular, is that companies of all types and sizes, from the e-commerce industry to the more classical ones like manufacturing, can take advantage of its powerful capabilities.
Let's take a look at some of the industries that have been the most impacted by computer vision.
Manufacturing
The two main problems that can occur on a manufacturing line are machine breakdowns or the production of defective products. This results in delays and significant losses in profits.
Machine vision algorithms prove to be a great means of automatic maintenance. By analyzing visual information (e.g., video from cameras attached to robots), algorithms can identify potential problems before they become severe. The fact that a system can anticipate that an automotive packaging or assembly robot will fail is a great contribution.
The same idea applies to defect reduction, where the system can detect defects in components and products along the entire production line. This allows manufacturers to take action in real-time and decide what needs to be done in order to solve the problem. Perhaps the defect is not so serious and the process can continue, but the product is marked in some way or rerouted through a specific production route. Sometimes, however, it may be necessary to stop the production line. Another interest is that the system can be trained, for each use case, to classify defects by types and degrees of severity.
Identification of defects using Automated Visual Inspection Technology
Healthcare
In healthcare, the number of existing machine vision applications are outstanding.
Undoubtedly, medical image analysis is the best known example, as it helps to significantly improve the medical diagnostic process. Images from MRIs, CT scans and X-rays are analyzed to find abnormalities such as tumors or to look for signs of neurological diseases.
Computer vision algorithm that detects problems within dental x-rays
Autonomous vehicles
Have you ever wondered how autonomous cars can "see" the world? The field of computer vision plays a central role in the domain of autonomous vehicles, as it allows them to perceive and understand the environment around them in order to operate correctly.
One of the most exciting challenges in computer vision is the detection of objects in images and videos. This involves locating a variable number of objects and the ability to classify them, to distinguish whether an object is a traffic light, a car or a person.
Object detection for autonomous vehicles
This type of technology, combined with data analysis from other sources, such as sensors and/or radar, is what allows an autonomous vehicle to perceive the world.
Insurance
The use of computer vision in insurance has had a major impact, particularly in claims processing.
A computer vision application can guide customers through the process of visually documenting a claim. It can analyze images in real-time and send them to the appropriate agents. At the same time, it can estimate and adjust repair costs, determine if they are covered by insurance, and even check for possible fraud. All of this minimizes the claims cycle time, resulting in a better customer experience.
From a preventive standpoint, computer vision is of great help in avoiding accidents; there are applications for collision prevention, integrated into industrial machinery, automobiles and drones. This is a new era of risk management that will most likely change the field of insurance forever.
Car damage detection for insurance claims
Agriculture
Agriculture is an important industry where computer vision is having a tremendous impact, especially in the area of precision agriculture.
In grain production, a global economic activity, a number of valuable applications have been developed. Grain production faces certain recurring problems, which have historically been monitored by humans. However, computer vision algorithms can now detect, or in some cases can reasonably predict, pest and insect diseases or infestations. Early diagnosis allows growers to take appropriate action quickly, reducing losses and ensuring yield quality.
Another ongoing challenge is weed control, considering that weeds have become resistant to herbicides over time and represent significant losses for farmers. There are robots with integrated machine vision technology that monitor entire farms and spray herbicides with precision. This saves huge volumes of pesticides, which is an incredible benefit for the planet and in terms of production costs.
Soil quality is also an important factor in agriculture. There are applications that can recognize, from images taken with cell phones, possible defects and nutritional deficiencies in soils. After analyzing the images sent, these applications suggest soil restoration techniques and possible solutions to the problems detected.
Computer vision can be further used in grading. Algorithms exist to sort fruits, vegetables and even flowers by identifying their main properties (e.g., size, quality, weight, color, texture). These algorithms are also able to detect defects and estimate which items will last longer and which should be sent to local markets. This leads to maximizing the shelf life of the items and reduces their time to market.
Why is Theos the solution?
All of these examples can be made possible thanks to Theos. Our AI development platform will eventually support all the subfields of AI that mimic most brain functions, such as computer vision, natural language processing, and speech recognition and synthesis. For now, we have object detection development, the task of giving an image to an AI and asking it to predict the classes of objects within the image and their positions.
You can take a look at our platform and learn more about it. You will be to create your own artificial intelligence without the need to be an expert engineer in the subject.
You can try it now for free.
