There is growing interest in applying machine learning algorithms to target acquisition tasks. Current algorithm studies suggest pixels on target area (POT) of 50-70, 150, 250, and 350 are adequate for detection, classification, recognition, and identification of tank-sized targets respectively. Using simple analyzes, we compare POT to Night Vision Integrated Performance Model (NV-IPM) range probability predictions for a typical LWIR sensor.


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    Titel :

    Comparing pixels on target with NV-IPM range predictions


    Beteiligte:
    Holst, Gerald C. (Herausgeber:in) / Haefner, David P. (Herausgeber:in) / Holst, Gerald C. (Autor:in) / Mahalanobis, Abhijit (Autor:in)

    Kongress:

    Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXIV ; 2023 ; Orlando, Florida, United States


    Erschienen in:

    Proc. SPIE ; 12533


    Erscheinungsdatum :

    2023-06-14





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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