The use of morphology and the low fourier frequencies in hierarchical fuzzy FLIR target segmentation and classification

Kim Kanzaki, Edward K. Wong, Matthew Kabrisky

    Research output: Contribution to journalConference article

    Abstract

    A fuzzy hierarchical FLIR ATR is proposed which more closely models the fuzziness in the FLIR data and the human decision process than the traditional ATR methods. The target and its internal hot spots are segmented out from the background by use of an iterative volume based morphological contrast peak extraction routine. The segmented regions are then represented by a set of silhouettes for each segmented blob rather than just the one "best" silhouette. For the target or foundation segment, the primary recognition feature, silhouette shape, is captured by the low frequencies of the 2-D DFT of each member of the set. The hot spots are represented both by the shape features (DFT) and by positional features. The first level of this hierarchical classification system uses an Euclidean distance figure of merit for the foundation's silhouette to assign a fuzzy classification to the target. This initial guess is then adjusted based on the internal features.

    Original languageEnglish (US)
    Pages (from-to)95-106
    Number of pages12
    JournalProceedings of SPIE - The International Society for Optical Engineering
    Volume1957
    DOIs
    StatePublished - Oct 20 1993
    EventArchitecture, Hardware, and Forward-Looking Infrared Issues in Automatic Target Recognition 1993 - Orlando, United States
    Duration: Apr 11 1993Apr 16 1993

    ASJC Scopus subject areas

    • Electronic, Optical and Magnetic Materials
    • Condensed Matter Physics
    • Computer Science Applications
    • Applied Mathematics
    • Electrical and Electronic Engineering

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