The volume of data that must be processed to characterize the performance of target detection algorithms over a complex parameter space requires automated analysis. This paper discusses a methodology for automatically scoring the results from a diversity of detectors producing several different forms of detected regions. The ability to automatically score detector outputs without using full target templates or models has advantages. Using target descriptors-primarily target sizes and locations-reduces the computational cost of matching detected regions against truthed targets in various scenes. It also diminishes the size of and the difficulty of creating an image-truth database. Theoretical considerations are presented. Overcoming issues associated with using limited truth information is explained. Concepts and use of the Auto-Score package are also discussed. The performances of several different laser radar (LADAR) target detectors, applied to imagery containing scenes with targets and both natural and man-made clutter, have been characterized with the aid of Auto-Score. Automatic scoring examples are taken from this domain. However, the scoring process is applicable to detectors operating on other problems and other kinds of data as well. The target-descriptor scoring concept and Auto-Score implementation were originated to support the development of a configurable automatic target recognition (ATR) system for LADAR data, under the auspices of the Office of Naval Research.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Automatically scoring ladar detectors using target descriptors


    Contributors:

    Conference:

    Automatic Target Recognition XI ; 2001 ; Orlando,FL,United States


    Published in:

    Publication date :

    2001-10-22





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Automatically scoring LADAR detectors using target descriptors

    Bergman, A.F. / DeKruger, D.H. | Tema Archive | 2001


    LADAR target detection using morphological shared-weight neural networks

    Khabou, M. A. / Gader, P. D. / Keller, J. M. | British Library Online Contents | 2000


    LADAR system

    CHUNG YOUNG DAE | European Patent Office | 2017

    Free access

    Ladar system

    CHUNG YOUNG DAE | European Patent Office | 2017

    Free access

    LADAR SYSTEM

    CHUNG YOUNG DAE | European Patent Office | 2017

    Free access