Photonics Dictionary

embedded vision

Embedded vision refers to the integration of computer vision technologies into various embedded systems, devices, or machines. Computer vision involves teaching machines to interpret and understand visual information from the world, much like human vision. Embedded vision takes this concept and applies it to systems where the processing occurs locally within the device, as opposed to relying on external servers or cloud-based services.

Key components of embedded vision systems include:

Image sensors: These capture visual information from the environment and convert it into electrical signals.

Processors: Embedded systems incorporate dedicated processors or chips capable of running computer vision algorithms. These algorithms analyze and interpret the visual data captured by the image sensors.

Memory: To store image data, intermediate results, and trained models used by the computer vision algorithms.

Interfaces: Embedded vision systems often have interfaces for connecting with other devices or networks. This can include communication protocols, such as USB, Ethernet, or wireless connectivity.

Power management: Efficient power management is crucial for embedded vision systems, especially in applications with power constraints, such as mobile devices or battery-operated equipment.

Embedded vision finds applications in various industries and devices, including:

Automotive: Embedded vision is used in advanced driver assistance systems (ADAS), autonomous vehicles, and in-vehicle infotainment systems.

Industrial automation: Applications include quality control, object recognition, and robot guidance in manufacturing processes.

Consumer electronics: Smartphones, smart cameras, and other devices use embedded vision for features like facial recognition, augmented reality, and gesture control.

Healthcare: Embedded vision is applied in medical imaging, diagnostics, and monitoring systems.

Surveillance and security: Video surveillance systems often use embedded vision for real-time analysis of video feeds.

The integration of computer vision capabilities directly into devices allows for real-time processing of visual data, enabling these devices to perceive and respond to their environment autonomously. This is particularly important in applications where low latency and efficient use of resources are critical.

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