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- W2564352786 abstract "The automatic detection and recognition of small targets in sea-clutter is a key technique in infrared search and tracking applications. In this paper, concentrating on the detection of targets with low SNR in complex sea-sky background, we propose a novel method based on Speeded up Robust Feature (SURF) combined with automatic clustering segmentation. Firstly, the SURF detector is introduced to locate the candidate target with robust features, and then, target trajectory is projected onto one frame. Relying on the aggregation property of the trace segments in space, the fast density-based clustering method is carried out to segment the trajectories. To handle the over& under segmentation problems by the original method for our application, we improve it as a self-adapting one by introducing two factors: percent and gamma. So that, we can achieve complete target trace segments adaptively and identify the cluster centers automatically. During recognition stage, the target is extracted from the clutter one by continuous and consistence in spatial-temporal. Extensive experiments show that the proposed method works more stable for various targets in sea-sky background without segmentation of sea-sky line. Compared with baseline methods and some new presented ones it also demonstrates significant detection performance and false alarm suppression result." @default.
- W2564352786 created "2017-01-06" @default.
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- W2564352786 date "2016-08-01" @default.
- W2564352786 modified "2023-09-22" @default.
- W2564352786 title "Robust Detection Method of Small Targets in Sea-Clutter via Improved Fast Clustering Segmentation" @default.
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- W2564352786 doi "https://doi.org/10.1109/ihmsc.2016.236" @default.
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