The time artificial intelligence introduced, computer scientists/researchers have been dreaming of developing machines that can see & understand the world as humans do. The efforts made led to the advent of computer vision, which is a vast subfield of AI & computer science that deals with processing the content of visual data/information.
In recent times, computer vision technology has taken great leaps thanks to improvements in deep learning & artificial neural networks. Deep learning is a subdivision of AI that is exceptionally good at procession unstructured data such as videos and images.
The advances have provided the way for boosting the use of computer vision in existing domains and again introducing new ones. In most cases, computer vision algorithms have become an essential component of the applications we are using every day.
Computer vision has the latent to alter a number of operations & sectors. As it grows in importance, its potential & applications will be crucial to helping it maximize your organization or firm. In this article, let have a look at the details of what computer vision is? And the applications of computer vision in all significant sectors or clusters in 2020.
So, what is computer vision?
“The construction of explicit, meaningful descriptions of physical objects from images.”
The above computer vision definition is for people who want a formal textbook definition. To make it easy for you, I am going to explain to you in simple terms.
Computer vision is an interdisciplinary turf that allows the computer to understand, process, and analyze images. It uses the algorithm that can process both static pictures and videos.
It works on enabling computers to see, identify, and process images as human vision do and then provide a suitable output. Sounds simple, isn’t it?
Well, technology isn’t even though the concept is simple. It is not as simple as you read because we need to enable the computer to recognize images of various physical substances. As a technological discipline, computer vision seeks to apply its theories & models for the development of computer vision systems.
One of the driving factors behind the enhancement of computer vision is the amount of data we generate daily (3 million images shared daily) that used to train and make computer vision better.
The expected market size of computer vision and hardware is expected to reach the US $48.6 billion by 2022.
In less than a decade, today’s computer applications reached 99% accuracy from 50%, making them more precise than humans at swiftly retorting to visual inputs. Sub-domains of computer vision include anomaly detection, image restoration, object recognition, video tracking, and indexing.
How does computer vision work?
Most of the people confuse between computer vision and image processing. However, computer vision is more of a high-level process, and it deals with the analysis of an image/video. The process involved in computer vision application is like input an image it will give you the interpretation of the image as output. It covers every component of the picture. These modules are then evaluated. A simple app sees computer vision finding the edges in an image. Computer vision allows patter recognition/shapes to be recognized. More advanced applications enable for people or animals to be recognized. This application of computer vision is central to automated vehicles & drones as well as augmented reality and facial recognition software.
Why study computer vision?
The simple answer I can provide for the above question is that there is a fast-growing collection of useful applications from this field of study. Some of them are:
- Face recognition
- Gaming and controls
- Smart cars
- Image retrieval
- Biometrics
- Surveillance
The current state of computer vision
Before we get thrilled about advances in computer vision, its good to know and understand the limits of current AI technologies. While many significant improvements are coming into the market, we are still fat away from having computational vision algorithms that can make sense of videos and photos in the same manner as humans do.
At present, deep neural networks are the key to computer vision systems, and they are very cool at matching patterns at the pixel level. And the neural networks are very good at classifying images and localizing objects in images.
But when it comes to an understanding of the context of visual data and describing and describing the relationship between various objects, they fail miserably. The current application of computer vision shows how much can be accomplished with pattern matching alone.
Challenges of computer vision?
The current subject is one of the challenging fields of computer science. Enabling a machine to be able to see and procedure what it sees like a human is an adamant thing. Not least because we are still learning precisely how human works. If you want your computer vision software application to be successful, recognition must become robust.
It includes solving issues like identification, classification, verification, and detection. Along with it, an effective CV system will be able to identify the pinpoints in a photograph or image. Object segmentation, categorizing the pixels in a picture is also key to successful object recognition. Don’t get relaxed once recognition is achieved because it should analyze the image correctly. If the same thing applied to a video, it requires accurate motion analysis, which permits the system to guess the velocity of objects in the video.
All the above issues must be solved to continue growing in value and importance.
Computer vision applications in various industries
Automotive
Computer vision is taking the automotive industry by storm. The reason for my above statement is companies like Tesla and Waymo are making maximum usage of the computer vision, and they have very positive results. According to the WHO (World Health Organization) report, more than 1.25 million people die each year by road accidents, among which 20% are caused by fatigue.
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And WHO adds this trend is predicted to become the seventh leading cause of death by 2030, if no proper action is taken on it. It is clear from the above points that these are mainly occurring because of human error and inattention. To resolve this combat issue computer vision is helping automobile manufacturers. Waymo (a subsidiary of Google) is a company that is making driving safer and optimize transportation.
Waymo is working hard to improve transportation for people, building on self-driving car & sensor technology developed in Google labs.
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Waymo is equipping cars with cameras and CV systems that can detect 360-degree view around the vehicle, and it tracks the movements of pedestrians, vehicles, cyclists, and other objects when they are around 300 yards. By doing so, it can detect potential hazards one can take early action. The company tests the technology on 7 million miles of public roads to train vehicles for safe navigation through daily traffic.
It means that the car will be able to read temporary road signs & give way to oncoming emergency vehicles.
This application of computer vision sees it working alongside deep neural networks, allowing the car to navigate in busy streets safely. The company is planning to remove noise in sensor data for a self-driving car with the help of Machine Learning. Currently, the company collaborated with Chrysler and Jaguar to continue developing automated vehicles.
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Healthcare
You believe it or not, 90% of all medical data is image-based; there are multiple uses for computer vision in the medical field. From allowing new medical diagnostic methods to examine mammography, X-rays, and other scans to monitor patients to detect problems earlier and assist with surgery, expect that our medical professionals, institutions, and patients will benefit from computer vision.
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Assisting in Diagnostics
Computer vision is being utilized to aid diagnose health conditions. Computer vision is enabling life-saving interventions to be made. When it used along with sensors, smart healthcare becomes a reality. This application helps identify and spot diseases in the earliest stage. The ChironX application uses computer vision to read retinal fundus images. It takes the help of deep learning to sense eye malady at its initial stage. Along with the above, it will predict the risk of eye disease & systemic diseases such as cardiovascular ailments from happening. The application we are using with the help of computer vision not only accurate, but it is also cost-effective and non-invasive. Subsequently, patients can be diagnosed with more effortlessly & consistently.
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Retail industry
Computer vision has made a squelch in the retail sector as well. In recent times, Amazon is leading the way in developing new technology in most of the sectors, and mainly it showed its presence in the retail industry. In 2018 the Amazon company shown a smart store called Amazon Go. Initially, it has automated the store that has no cashiers or checkout station. With the help of computer vision, deep learning, sensor fusion, and other sophisticated tools enable customers to pay for merchandise without the need for a checkout. However, an impressive step makes it easy for customers and owners by automatically billing through customer Amazon accounts. Along with the Amazon, the Chinese internet giant Lenovo has also jumped into this bandwagon. This Cv application makes the need for cashiers mostly redundant.
Based on some of the reports, Amazon Go stores still consist of employees to assist customers, check ID, and stock shelves. The staff at Amazon Go stores also work in the background to train algorithms if they inaccurately identify which item has been detached from the shelf.
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Reduces in-store theft
In the retail sector exclusively in groceries, StopLift claims to have developed a computer vision application that could minimize theft and other various losses at store chains. It developed a product called ScanItAll that detects cashier errors or checkout who avoid scanning, also called Sweethearting.
Sweethearting is the cashier’s act of fake scanning of products at the checkout in cooperation with a customer who could be a family, fellow employee, or friend. ScanItAll can be easily installed into the store’s existing point of sale systems or cameras, making integration easy.
Using various algorithms, StopLift claims that ScanItAll can detect sweethearting behaviors such as stacking items on top of another, covering the barcode, and skipping the scanner & directly packing the products. To get a better idea of technology, please click on the below link
Based on the new gather data, the company claims to have the technology installed in some of the supermarkets in Massachusetts, Rhode Island, and Australia.
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Banking
In previous articles, we have discussed the AI in the banking & finance sector that involved fraud detection & natural language processing.
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Some CV technology has also found its way into the banking industry as well. Computer vision technology already started influencing on baking & finance sector. Mitek systems provide image recognition applications that make use of machine learning to classify, extract information, and authenticate documents like ID cards, checks, passports, and driver licenses. Mitek system applications enable customers to use their mobile device to take a photograph of their ID. It can be sent to the bank where CV software verifies the legitimacy of the document. Using CV systems to automate verification helps to fasten identity checks. It, in turn, fastens up business process & financial transactions. While in most of the cases, images will be rejected due to poor quality, in such situations, the CV can correct images faults such as poor lighting or distortion. One bank called Mercantile Bank of Michigan is the client of Mitek systems. The primary reason behind the implementation is to magnify its retail portfolio & associated core deposits.
Mitek systems took one month to deploy the application. After 4 months of implementation, 20% of online customers at banking using this CV application. This CV application allowed customers to verify their identity in a faster and secure manner. By doing so, human error is removed from the process. As we are successfully scanning, the ID means checks and transactions can be carried out securely and quickly.
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Conclusion
It’s all about the usage of computer vision applications increased in various industries. Some of the industries have adopted the technology faster than others. How much may be computer vision technology increasing it continues to rely on the human effort to monitor, interpret, analyze, control, and decision-making. In addition to helping the automation, computer vision allow stores to operate with minimal human intervention.
As machines and humans continue to collaborate, the human workforce will be freed up to emphasis on higher-value errands because the tools will computerize the process that relies on image recognition.
If you are planning to use computer vision applications and want to clear your myths on it, contact USM Business Systems and talk to our professionals today.
Source: https://www.usmsystems.com/computer-vision-applications/
