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Belloni et al., 2019 - Google Patents

Comparison of descriptors for SAR ATR

Belloni et al., 2019

Document ID
14744527254665715057
Author
Belloni C
Aouf N
Le Caillec J
Merlet T
Publication year
Publication venue
2019 IEEE Radar Conference (RadarConf)

External Links

Snippet

Recent studies have reported very high automatic target recognition (ATR) rates of synthetic aperture radar (SAR) images based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database. However, one of the limitations of the MSTAR database is …
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Classifications

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    • G06K9/20—Image acquisition
    • G06K9/32—Aligning or centering of the image pick-up or image-field
    • G06K9/3233—Determination of region of interest
    • G06K9/3241—Recognising objects as potential recognition candidates based on visual cues, e.g. shape
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6201—Matching; Proximity measures
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    • G06K9/4671—Extracting features based on salient regional features, e.g. Scale Invariant Feature Transform [SIFT] keypoints
    • G—PHYSICS
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    • G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
    • G06K9/629—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion of extracted features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
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    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
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