According to a survey conducted by Mordor Intelligence, the computer vision market is expected to grow from $14.65 billion in 2023 to $33.13 billion by 2028.
These numbers indicate significant growth and increased technology use across various sectors. The research also states that due to advances in image and object sensors, the application of computer vision has expanded in many sectors, such as healthcare and security, for example.
So, if you are interested and want to learn more about what computer vision is, how it works, its main applications, and tasks, keep reading the article.
What is computer vision?
Computer Vision is a field of computer science that leverages Artificial Intelligence (AI), Machine Learning (ML), and Optical Character Recognition (OCR) to develop computational systems capable of seeing, interpreting, and classifying visual information in a manner similar to humans.
This is achieved through the development of algorithms capable of extracting patterns and useful information by simply analyzing images and videos. This enables the generation of relevant data, automation of tasks, and acceleration of decision-making in various areas.
How does computer vision work?
For us humans, the process of seeing, analyzing, and understanding the world around us is a routine task. However, this physiological system involves several steps: when light reflected by objects passes through the cornea, it travels along the nerves of the retina and only then reaches the brain so that images can be processed and understood.
With continuous learning, we enhance our ability to differentiate objects, measure distances, and understand patterns, gaining context about the world around us.
With computer vision, the process is similar. Through the use of algorithms and operating systems with mathematical and physical data, the machine becomes capable of analyzing, interpreting, and extracting information from visual elements.
To operate correctly, computer vision systems rely on techniques such as Machine Learning and Deep Learning to improve their capabilities over time. In other words, machines need to be trained and fed with a large volume of data to optimize their processing capacity.
Only then can the system be trained to succeed in its goal, distinguishing characters, symbols, animals, people, and objects, for example. This process is cumulative – the more data and information provided to the system, the greater its ability to perform tasks appropriately.
Applications of computer vision
After understanding what computer vision is and how it works, let’s explore some examples of applications of this technology in different processes and sectors. Check it out:
IT Automation
Computer vision technology is also used in Robotic Process Automation (RPA) in various areas, such as IT.
With computer vision, robots created to automate IT processes can “see” systems and navigate between screens, being able to fill in fields, update files, and more.
As a result, if changes are made to the system the robot works on, it can still scan the screens until it finds the information it is looking for.
For example, BotCity, a company specialized in business process automation, offers a code assistant and a document converter in Python RPA based on computer vision, helping developers create robots in Python RPA.
💡Learn more: How to optimize performance with Python RPA
Agriculture
Computer vision enhances agriculture in various aspects: planting control, pest control, weed identification, herd tracking, soil moisture verification, harvest robotization, and advanced weather analysis, for example.
All these functionalities contribute to increased productivity, improved time between sowing and harvesting, and, especially, cost reduction.
Autonomous vehicles
For autonomous vehicles, computer vision allows them to understand and react to the environment very similarly to human capability. This is because the technology gives autopilot the ability to map, analyze, and react quickly to all objects around.
Tasks such as object detection, recognition of signage, depth perception, recognition of pedestrians and cyclists, autonomous decision-making, and mapping are applied.
Intelligent document processing
Within computer vision, there is also Optical Character Recognition (OCR), a widely used area for intelligent document processing.
For example, OCR-based robots are widely used for reading and classifying documents in various business sectors. They can also convert documents into code and trigger automations based on their reading.
Applications are diverse: from a purchase order, payment approval to bank reconciliation, and inventory checking, much can be done with OCR combined with process automation.
Facial recognition
Facial recognition is available in personal use applications and is widely used by various individuals on their smartphones and companies working with identity verification.
The public security industry uses facial recognition systems in various cities around the world to detect and prevent criminal activities. The facial recognition process begins with computer vision algorithms delineating faces in images. Then, facial features are extracted and transformed into numerical data for analysis.
Finally, the person is identified based on the recognition of unique patterns present in facial features, allowing computer vision to compare these patterns with stored data.
Entertainment
Entertainment has increasingly presented itself with innovative and interactive experiences. Users can participate and interact, making experiences immersive and dynamic.
Computer vision is an essential component in the creation of Augmented Reality and Virtual Reality, as it allows devices to interact with the environment. Digital games have become more interactive with systems like Microsoft’s Kinect, for example.
Health
The health sector is also benefiting from computer vision to optimize processes, especially in imaging exams such as X-rays and ultrasounds.
Algorithms analyze images and can identify possible anomalies that require attention from the doctor. This way, diagnoses become faster, more accurate, and the chance of human error decreases.
Main tasks of computer vision
Now, let’s check the main tasks of computer vision:
Image Classification
Through algorithms and various learning models, computer vision can classify images by identifying patterns and, based on that, categorize them by class. For example, after identifying objects, the technology can separate them into furniture, clothing, and electronics.
Object Tracking
Present in surveillance systems and autonomous vehicles, object tracking is the ability to follow and locate static or moving objects in sequences of images and videos.
And based on this, analyze and perform a specific function. For example, a car can trigger a warning sound when it identifies that another vehicle is approaching quickly.
Segmentation
The segmentation algorithm in computer vision can separate images into significant parts according to visual characteristics, such as brightness and color. This task is fundamental for understanding the structure of a scene and identifying objects.
Ready to use computer vision?
We hope this content has clarified the main questions about computer vision, its operation, applications, and tasks. This is a promising field that evolves constantly and aids in the development of companies.
If you are interested in computer vision for your area, it’s worth checking out BotCity’s computer vision solutions.
If your business is ready to take the next step and boost the efficiency and orchestration of your automations, it’s worth exploring BotCity’s RPA orchestrator. Click here to speak with a specialist!
Or, if you’re taking the first steps in robotic process automation (RPA), take advantage of the opportunity to register for free at BotCity right now!
BotCity offers a free Python code assistant for RPA, as well as more than 30 RPA frameworks for common use cases of automation.
