What is Computer Vision and Image Processing?
Sight is an extraordinary phenomenon. The art of perceiving the observed is strategically complex and sophisticated. Thus, making capturing reality through one’s eyes an elusive process. For instance, when we see a car approaching at high speed, we register the object as the visual signal hits the retinas. First-most, brief observation of the object takes place. Secondly, the virtual cortex of the brain receives data. Lastly, analyzation of scenarios occurs. When the brain perceives the image and matches it to the existing database, the object is classified, and its impact understood. As an output, we get an impulse to move out of the way once the brain has classified the object, analysed the speed and recognised the movement trajectory.
All of this takes place in a fraction of a second.
Having understood how intricate the system of visual perception is, it is fascinating to learn how advancements in technology have managed to recreate the phenomenon in a machine. Technology is now capable of emulating:
- The eye
–(Computer Vision) - The visual cortex which compares and analyses data
–(Deep Learning) - Lastly, responses to visual information
–(Image Processing).
Computer Vision thus is an interdisciplinary discipline that allows the computer to understand, process, and analyze images. It uses the algorithm that can process both static pictures and videos. It works by enabling computers to see, identify, and process images as human vision does and then provide a suitable output.
Nowadays, computer vision technology are achieving great leaps. Deep learning improvements fuel these major leaps. In turn, driving a sheer volume of data generated ever-day. On a daily basis, over three million images are shared.
Thus, enabling computer vision to be better. The expected market size of computer vision and hardware shall reach $48.6 billion by 2022 cementing the technology’s growth and adoption across various industries.
In less than a decade, today’s computer applications have reached 99% accuracy from 50%, making them more precise at swiftly responding to visual inputs. The sub-domains of computer vision include:
- Anomaly detection,
- Image restoration
- Object recognition
- Video tracking
- Indexing
All of the points mentioned above can potentially transform business operations conducted today.
Industry Specific Applications of Computer Vision
Automated Guided Vehicles
An Automated Guided Vehicle (AGV) is a machine or robot that navigates itself using computer vision. Moreover, AGVs are commonly used in an industrial environment to transport raw materials, work-in-process, and finished goods. In fact, they are suitable around facilities such as manufacturing plants, warehouses and distribution centres.
In addition, CPG systems embedded in AGVs increases its capabilities. Thus, operators can direct vehicles to the exact location with utmost precision. Therefore, allowing smooth pick up and drop off of cargo.This GPS capability also provides real-time visibility as to the operations associated with these vehicles and provides insightful and up-to-date data that allows managers to detect inefficiencies and take corrective measures.
Autonomous Vehicles
Self-driving cars are responsible for spreading awareness regarding computer vision. Between 2020 and 2025, fully autonomous vehicles will operate in limited zones. The industry is investing heavily in the development of autonomous vehicles software which uses sensor-fed machine learning algorithms. Companies like Alphabet’s Waymo, Tesla, Uber, Baidu, Aptiv/NuTonomy, Nvidia, Intel/Mobileye, and GM Cruise are all competing to be leading players.
Ambient Commerce
Computer Vision is one of the primary technologies promoting ambient commerce. The importance of leveraging sensors and machine learning in physical stores are limitless. Since, computer vision can detect when an item is removed from the shelf and by whom. The technology then tracks the customer activity throughout the store. Amazon Go has deployed this technology using hundreds of cameras to achieve a fully cashless and contactless shopping experience.
Diagnostic Assistance
90% of medical data is image based. Most importantly, computer vision are deployed to proactively diagnose health conditions. by facilitating the examination of X-rays and other scans to monitor patients and detect problems at an earlier stage.
In-Store Theft Management
In the retail sector exclusively in department stores, StopLift claims to have developed a computer vision application that could potentially minimize theft and other acts of embezzlement at store chains. It proprietary product called ScanItAll detects cashier errors or checkout who avoid scanning, also called Sweethearting.
Sweethearting is the cashier’s act of fake scanning products at the checkout in cooperation with a customer who could be a family, fellow employee, or friend. Installation of ScanItAll in store’s existing point of sale systems or cameras. Thereby, 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 and directly packing the products. This technology is being used by supermarkets in Massachusetts, Rhode Island, and Australia.
Banking
Computer Vision technology has found its way in banking sector as well. Image recognition applications have been developed that make use of machine learning to classify, extract information, and authenticate documents like ID cards, checks, passports, and driver licenses.
Computer Vision is the next step in business innovation. As machines and humans continue to collaborate, tedious and error prone tasks are left to be handled by machines while the workforce can be deployed towards high-value errands.
To find out more about this technology and how it can benefit your business, speak to a professional at TEMPO today.
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