The era of Artificial Intelligence (AI) has arrived and is expanding like never before. Computer scientists and researchers have long dreamed of creating machines that can perceive and comprehend the world like humans do. Their efforts have led to the emergence of computer vision, a vast subfield of AI.
In recent years, the integration of computer vision in artificial intelligence has made significant advancements, thanks to improvements in deep learning and AI neural networks. Deep learning, a subset of AI, excels at processing unstructured data such as videos and images.
These advancements have paved the way for the increased utilization of computer vision in existing domains and the introduction of new ones. Today, computer vision algorithms play a critical role in the intelligent applications and systems we use daily.
Computer vision possesses the ability to extract information from digital images, videos, and other visual inputs, providing valuable insights. As its significance grows, the future of computer vision holds great promise and will help organizations enhance their business efficiency.
This article will delve into the concept of computer vision, its applications, benefits, and the future prospects of this technology.
Understanding Computer Vision
“The process of creating explicit, meaningful descriptions of physical objects from digital images.”
Put simply, computer vision is an interdisciplinary field of AI that enables computers to understand, process, and analyze digital images, videos, and other visual inputs. Its sophisticated algorithms can handle both static images and videos.
The goal of computer vision is to empower computers to perceive, identify, and process images in a manner similar to human vision, providing relevant outputs.
One of the driving forces behind the advancement of computer vision is the massive amount of data generated daily (3 million images shared daily), which is used to train and enhance computer vision capabilities. The projected market size of computer vision is expected to reach US $48.6 billion by 2022.
In less than a decade, today’s computer applications have achieved 99% accuracy from 50%, surpassing human precision in responding swiftly to visual inputs. Subfields of computer vision include anomaly detection, image restoration, object recognition, video tracking, and indexing.
Functionality of Computer Vision
Many individuals often confuse computer vision with image processing. However, computer vision is a higher-level process that involves analyzing images/videos.
The application of computer vision entails inputting an image and receiving an interpretation of the image as output. The computer vision software dissects every element of the image, which are then evaluated.
For instance, basic computer vision allows for the recognition of patterns/shapes, while more advanced applications enable the identification of people or animals.
Computer vision applications in 2022 are crucial for automated vehicles, drones, augmented reality, and facial recognition software development.
The Current State of Computer Vision
While there have been remarkable advancements in computer vision, it’s essential to acknowledge the challenges faced by applied computer vision and AI technologies. Despite substantial progress in digital technologies, we are still a long way from deploying computational vision algorithms that can interpret videos and photos with the same level of understanding as humans.
Currently, deep neural networks play a pivotal role in computer vision systems, excelling at pattern matching at the pixel level and classifying images while localizing objects within images.
However, these systems struggle when it comes to understanding the context of visual data, describing relationships between various objects, and interpreting visual data holistically. The current application of computer vision in artificial intelligence underscores the limitations of relying solely on pattern matching.
Key Challenges of Applied Computer Vision
Applied computer vision remains one of the most challenging fields in computer science. Teaching a machine to see and interpret visual data like a human is a complex task, particularly as our understanding of human vision continues to evolve.
For computer vision software to be successful, recognition must be robust, enabling quick and accurate image detection, identification, classification, and verification. These are among the significant benefits of computer vision applications.
Effective computer vision systems developed using AI, facial recognition, and other intelligent technologies can pinpoint details in photographs or images. These types of computer vision applications can segment objects, categorize pixels in input images, and accurately identify objects more efficiently.
However, achieving recognition is just the first step; the software must also analyze images accurately. In the case of videos, accurate motion analysis is required to estimate the velocity of objects within the video.
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Applications of Computer Vision in Various Industries
1. Computer Vision Applications in Automotive
The automotive industry is increasingly embracing automation to enhance operational and production processes, reduce manual labor, and cut costs.
Computer vision technology has made significant strides in the automotive sector. Companies like Tesla and Waymo are at the forefront of leveraging computer vision technology, yielding positive results.
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Key applications of computer vision in the automotive sector include:
- Accurate identification of vehicle parts during assembly
- Enhanced efficiency of self-driving cars
- Ensuring safe driving and optimizing transportation
- Detecting machinery defects
- Stamping applications
- Accurate vehicle parts identification using computer vision-enabled systems
- Determining the shape and size of stamped parts
- Categorizing tires based on patterns and matching them to vehicle wheels
- Performance analysis
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For instance, Waymo equips vehicles with cameras and computer vision systems capable of detecting objects, pedestrians, vehicles, cyclists, and other elements within a 360-degree view up to 300 yards away. This technology enables early detection of potential hazards, allowing for proactive measures to be taken. Waymo has tested this technology on 7 million miles of public roads to train its vehicles for safe navigation in everyday traffic.
Autonomous cars equipped with computer vision technology can recognize temporary road signs, objects, people, and emergency vehicles, enhancing road safety.
By leveraging deep neural networks, computer vision technology enables vehicles to navigate safely in busy urban environments. Waymo is also exploring the use of Machine Learning to reduce sensor data noise in self-driving cars. The company has partnered with Chrysler and Jaguar to further develop automated vehicles.
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2. Computer Vision Applications in Healthcare
Computer vision technology has revolutionized healthcare by enabling new diagnostic methods to analyze mammograms, X-rays, and other scans, monitor patient conditions, detect issues early, and assist in surgeries. Medical professionals and institutions utilize computer vision-powered systems to provide intelligent diagnostic support.
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The demand for computer vision applications in healthcare is particularly high for intelligent diagnostic assistance. These applications aid in diagnosing health conditions and facilitating life-saving interventions. When combined with sensors, computer vision technology enables medical professionals to deliver smarter healthcare services, identify diseases early, and predict risks of eye and systemic diseases.
For example, the ChironX application employs computer vision to analyze retinal fundus images, utilizing deep learning to detect eye diseases in their early stages and predict the risk of eye and systemic ailments. This application is not only accurate but also cost-effective and non-invasive, making diagnoses easier and more consistent for patients.
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3. Computer Vision Applications in Retail
Computer vision technology is making significant strides in the retail industry. Amazon, in particular, has been a pioneer in developing new technologies in various sectors, notably retail.
In 2018, Amazon introduced the concept of Amazon Go, a cashierless store that automates the checkout process without the need for cashiers or checkout stations.
By leveraging computer vision, deep learning, sensor fusion, and other advanced tools, customers can purchase merchandise without going through a traditional checkout process. Payments are automatically billed to customers’ Amazon accounts, streamlining the shopping experience.
Another noteworthy development is the adoption of computer vision technology by the Chinese internet giant Lenovo, which has also ventured into the retail industry. This application eliminates the need for cashiers by automating the billing process.
While Amazon Go stores still employ staff to assist customers, check IDs, and restock shelves, the use of computer vision technology has streamlined operations and improved efficiency. Staff members also work behind the scenes to train algorithms to accurately identify items removed from shelves.
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By deploying AI-powered computer vision systems in-store, retailers can reduce theft and enhance security. In the grocery sector specifically, StopLift has developed a computer vision application called ScanItAll, which aims to reduce theft and losses in store chains by detecting cashier errors and sweethearting, where cashiers fake scan items in collusion with customers.
ScanItAll can be seamlessly integrated into existing point-of-sale systems or cameras, allowing for easy implementation. By leveraging various algorithms, StopLift claims that ScanItAll can detect sweethearting behaviors and prevent losses effectively. The technology has been deployed in supermarkets in Massachusetts, Rhode Island, and Australia, yielding positive results.
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4. Computer Vision Applications in Banking and Finance
Computer vision technology has found its way into the banking and finance sector, complementing existing AI applications such as fraud detection and natural language processing.
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Companies like Mitek Systems offer image recognition applications that utilize machine learning to classify, extract information, and authenticate documents such as ID cards, checks, passports, and driver licenses. These applications enable customers to verify their identity by taking a photograph of their ID with a mobile device, which is then authenticated by the bank using CV software.
Automating verification processes with computer vision technology accelerates identity checks, streamlining business operations and financial transactions. Additionally, CV systems can correct image imperfections such as poor lighting or distortion, ensuring accurate verification even in challenging conditions. One bank that has adopted Mitek Systems’ technology is the Mercantile Bank of Michigan, enhancing its retail portfolio and core deposits.
By deploying CV applications, customers can verify their identity securely and efficiently, eliminating human errors in the process. Secure identity verification enables seamless checks and transactions to be conducted swiftly and securely.
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5. Computer Vision Applications in Education
Computer vision technology is being increasingly utilized in the education sector for various applications, including mask detection, social distancing monitoring, intrusion detection, attendance management, and facial expression monitoring in classrooms. These applications enhance safety and operational efficiency in educational settings.
6. Application of Computer Vision in Security
In recent years, computer vision-based security systems have gained traction in enhancing security levels and preventing unforeseen incidents. Intelligent surveillance systems powered by computer vision continuously monitor security footage, identify suspicious activities, and enhance safety and security in various environments.
7. Computer Vision Applications in Fitness and Sports
Computer vision technology is making waves in the sports and fitness industry, enabling the detection of player and object coordinates, event tracking, performance monitoring, and personalized fitness coaching. Sports authorities leverage computer vision technology to enhance player performance analysis, event detection, and workout supervision.
These are just a few examples of the wide-ranging applications of computer vision technology in different industries, including manufacturing, mining, and precision agriculture.
Conclusion
The use of computer vision applications is on the rise across diverse industries, with some sectors adopting the technology more rapidly than others. While computer vision technology continues to evolve, human oversight remains essential for monitoring, interpreting, analyzing, controlling, and decision-making. By automating processes reliant on image recognition, computer vision tools enable stores to operate with minimal human intervention.
As machines and humans collaborate, human workers can focus on higher-value tasks, while automated tools streamline image recognition processes. If you are considering implementing computer vision applications and seeking clarity on their potential, reach out to USM Business Systems today.
