Computer vision at the edge

  • What is computer vision on the edge?

    Edge computing is a crucial aspect of computer vision that allows for data processing to occur at the edge of a network, rather than in the cloud or a data center..

  • What is the meaning of computing at the edge?

    Edge computing is an emerging computing paradigm which refers to a range of networks and devices at or near the user.
    Edge is about processing data closer to where it's being generated, enabling processing at greater speeds and volumes, leading to greater action-led results in real time..

  • An edge in an image is an image contour across which the image's brightness or hue changes abruptly, perhaps in the magnitude or in the rate of change in the magnitude (Nalwa, 75).
    These edges are modeled as mathematical discontinuities.
  • Two essential technologies are used to accomplish this: a type of machine learning called deep learning and a convolutional neural network (CNN).
    Machine learning uses algorithmic models that enable a computer to teach itself about the context of visual data.
By processing visual data at the edge, Edge Computer Vision enables real-time decision-making without relying on a centralized cloud server. This real-time capability is crucial in industries where timely actions can prevent costly issues, reduce downtime, and improve overall efficiency.

Considerations

These considerations implement the pillars of the Azure Well-Architected Framework, which is a set of guiding tenets that can be used to improve the quality of a workload.
For more information, see Microsoft Azure Well-Architected Framework.

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Contributors

This article is maintained by Microsoft.
It was originally written by the following contributors.
Principal Author:.
1) Wilson Lee| Principal Software Engineer

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Next Steps

IoT concepts and Azure IoT Hub

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Scenario Details

Fully automated smart factories use artificial intelligence (AI) and machine learning (ML) to analyze data, run systems, and improve processes over time.
In this example, cameras send images to an Azure IoT Edge device that runs an ML model.
The model calculates inferences, and sends actionable output to the cloud for further processing.
Human inte.


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