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A Stream Algebra for Performance Optimization of Large Scale Computer Vision Pipelines.

Mohamed A Helala, Faisal Z Qureshi, Ken Q Pu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 12, 2020
    PubMed
    Summary

    Researchers developed formal methods to optimize large-scale computer vision systems processing visual data streams. This framework enables adaptive tuning of algorithms for improved performance in real-time image and video analysis.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Image Processing

    Background:

    • Rapid growth in image and video data generation necessitates scalable computer vision systems.
    • Existing systems face challenges in adaptively tuning algorithms due to varying data characteristics and speeds.
    • Lack of formal frameworks hinders the optimization of large-scale visual processing pipelines.

    Purpose of the Study:

    • To present formal methods and algorithms for building and optimizing large-scale computer vision systems.
    • To introduce a formal algebra for mathematically describing computer vision pipelines processing data streams.
    • To enable adaptive tuning and optimization of computer vision algorithms in real-time.

    Main Methods:

    • Developed a formal algebra framework for describing computer vision pipelines.
    • Integrated feedback control mechanisms within the stream algebra.
    • Utilized a general optimizer with feedback control for online parameter optimization.

    Main Results:

    • The formal algebra provides a mathematical description of computer vision pipelines for image and video streams.
    • The framework naturally incorporates feedback control for adaptive system behavior.
    • A common online parameter optimization method is demonstrated for computer vision pipelines.

    Conclusions:

    • The proposed formal methods and algebra overcome challenges in building and optimizing large-scale computer vision systems.
    • The framework facilitates adaptive algorithm tuning and enhances the efficiency of visual data stream processing.
    • This work provides a foundation for developing more robust and scalable computer vision applications.