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An Automatic Graphic Pattern Generation Algorithm and Its Application to the Multipurpose Camouflage Pattern Design.

Arash Abdi, Reza Safabakhsh

    IEEE Transactions on Cybernetics
    |January 31, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an automatic graphic pattern generation (AGPG) model for creating novel, multipurpose camouflage patterns. The AGPG model designs effective camouflage for diverse backgrounds without human input or existing pattern reliance.

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    Area of Science:

    • Computer Graphics
    • Pattern Recognition
    • Artificial Intelligence

    Background:

    • Traditional graphic pattern creation relies on manual design by graphic designers.
    • Existing automated methods offer limited tools and lack full procedural automation.
    • Current camouflage generation methods often use few backgrounds and depend on existing patterns, limiting novelty and adaptability.

    Purpose of the Study:

    • To propose a fully automatic graphic pattern generation (AGPG) model.
    • To customize the AGPG model for creating versatile camouflage patterns.
    • To develop a method capable of generating novel patterns for diverse and changing backgrounds.

    Main Methods:

    • Development of a fully automatic graphic pattern generation (AGPG) model.
    • Customization of the AGPG model for camouflage pattern design.
    • Implementation of a novel creative drawing engine for generating new graphical structures.
    • Utilizing an evolutionary strategy-based algorithm to tune drawing module parameters.
    • Designing patterns adaptable to an unlimited number of background images.

    Main Results:

    • The AGPG model successfully generates graphic patterns without human interference.
    • Novel multipurpose camouflage patterns were created automatically.
    • Generated camouflage patterns demonstrated high concealment capabilities across various backgrounds.
    • The model's creative drawing engine produced new graphical structures independent of existing patterns.

    Conclusions:

    • The proposed AGPG model offers a fully automated solution for graphic pattern generation.
    • The customized model effectively creates novel, multipurpose camouflage patterns suitable for diverse environments.
    • The evolutionary strategy-based approach enables adaptable and effective camouflage design.