Computer vision
Scrump team automates processes and simplifies solutions to business problems based on computer vision technology.
Computer vision
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand.
Computer vision helps to reduce the time spent on routine tasks. Computer vision is used in industries ranging from energy and utilities to manufacturing and automotive – and the market is continuing to grow.
There is a lot of research being done in the computer vision field, but it’s not just research. Real-world applications demonstrate how important computer vision is to endeavors in business, entertainment, transportation, healthcare and everyday life. A key driver for the growth of these applications is the flood of visual information flowing from smartphones, security systems, traffic cameras and other visually instrumented devices. For example, Google Translate lets users point a smartphone camera at a sign in another language and almost immediately obtain a translation of the sign in their preferred language.
Application
- Identification
- Text recognition
- Reconstructing 3D shapes from 2D images
- Assessing movement
- Restoring images
- Highlighting structures of a certain kind in images
- Image segmentation
- Optical flow analysis
Fields of application
Manufacturing. For instance, predictive maintenance systems use computer vision in their inspection systems. These tools minimize machinery breakdowns and product deformities by constantly scanning the environment. If a likely breakdown or low-quality product is detected, the system notifies human personnel, allowing them to trigger further actions. Apart from this, computer vision is used by workers in packaging and quality monitoring activities.
Autonomous vehicles. Tesla’s autonomous cars use multi-camera setups to analyze their surroundings. This enables the vehicles to provide users with advanced features, such as autopilot. The vehicle also uses 360-degree cameras to detect and classify objects through computer vision.
Agriculture. Companies specializing in agriculture technology are developing advanced computer vision and artificial intelligence models for sowing and harvesting purposes. These solutions are also useful for weeding, detecting plant health, and advanced weather analysis.Computer vision has numerous existing and upcoming applications in agriculture, including drone-based crop monitoring, automatic spraying of pesticides, yield tracking, and smart crop sorting & classification.
Medical imaging. There has been a noteworthy increase in the application of computer vision techniques for the processing of medical imagery. This is especially prevalent in pathology, radiology, and ophthalmology. Visual pattern recognition, through computer vision, enables advanced products, such as Microsoft InnerEye, to deliver swift and accurate diagnoses in an increasing number of medical specialties.
Education.For instance, teachers use computer vision solutions to evaluate the learning process non-obstructively. These solutions allow teachers to identify disengaged students and tweak the teaching process to ensure that they are not left behind.
Features
- Image classification sees an image and can classify it (a dog, an apple, a person’s face). More precisely, it is able to accurately predict that a given image belongs to a certain class. For example, a social media company might want to use it to automatically identify and segregate objectionable images uploaded by users.
- Object detection can use image classification to identify a certain class of image and then detect and tabulate their appearance in an image or video. Examples include detecting damages on an assembly line or identifying machinery that requires maintenance.
- Object tracking follows or tracks an object once it is detected. This task is often executed with images captured in sequence or real-time video feeds. Autonomous vehicles, for example, need to not only classify and detect objects such as pedestrians, other cars and road infrastructure, they need to track them in motion to avoid collisions and obey traffic laws.
- Content-based image retrieval uses computer vision to browse, search and retrieve images from large data stores, based on the content of the images rather than metadata tags associated with them. This task can incorporate automatic image annotation that replaces manual image tagging. These tasks can be used for digital asset management systems and can increase the accuracy of search and retrieval.
For business
Computer vision is used in business to analyse video and surveillance images, classify scanned documents and extract data from them, monitor social media, recognise clothing and accessories in photos (for e-commerce), and automatically monitor employees from screenshots of computer screens.
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