The basics of computer vision
Computer vision (CV) affects many aspects of everyday life, whether people are aware of it or not. A subcategory of artificial intelligence (AI), it constructs or uses existing digital systems to view, identify and process visual data - much the same as the human eye does.
A computer analyses the input and responds with the relevant action. Think of it as digitising human intelligence and instincts.
How computer vision works
Recent headway in AI and deep learning has significantly boosted advances in computer vision. Today, this functionality surpasses human capabilities in tasks such as detecting and labelling objects.
The foundations of computer vision are convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
The neural network type known as convolutional neural networks (CNNs) is used largely for image and video recognition applications. They are made up of many layers of connected nodes, where each layer processes and modifies the data received from the one before it. To recognise images, the network must be able to locate local patterns and relationships within the input data, such as edges, corners and textures.
Recurrent neural networks (RNNs) are a kind of neural network that can analyse time-series data, voice and other sequential data. They are made up of a number of interconnected nodes in a sequence. RNNs can thus process sequences of varying lengths and capture the relationships between the sequence's constituents.
Using CNNs to work through pixelated visual data, computer vision employs deep learning RNN to relate pixels to one another. Secure Redact uses the basis of this same technology to rapidly blur personal data in images or video feeds.
Computer vision applications
Many sectors, including the healthcare, transport, security and industrial markets, use computer vision on a daily basis. The following are some examples of its application:
Eye scanning technology is used by ocular biometric access management to identify people for security reasons. To confirm someone's identification, a picture of their eye is taken and the distinctive patterns and characteristics of the iris or retina are examined. In high-security facilities, military sites, and secure computer systems, this technology is frequently employed to maintain secure environments.
Computer vision is also crucial for self-driving cars to comprehend and interpret the environment around them. By using cameras, LiDAR, and other sensors to collect information about the road, traffic, people, and other objects, it enables the automobile to "see". The computer system of the vehicle then processes and analyses the collected data to make decisions in real-time, such as modifying speed, changing lanes and dodging obstructions.
Equally, to help medical personnel identify and treat diseases and disorders, computer vision is used in medical imagery. The technique enables the automatic interpretation of diagnostic pictures from the medical field, including X-rays, CT scans and MRI scans. Plans for diagnosis and treatment can then be created using this information. Additionally, computer vision can compare subsequent photos to track the development of a disease or treatment over time.
Secure Redact incorporates cutting-edge artificial intelligence, including machine learning and computer vision, to help companies use and manage video safely while protecting the data of individuals. Our breakthrough machine-learning algorithms enable fast and accurate blurring of faces and other personal data in CCTV, as well as with live - and other - video footage types.