2021
DOI: 10.1109/tgrs.2020.3037938
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Edge and Corner Awareness-Based Spatial–Temporal Tensor Model for Infrared Small-Target Detection

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Cited by 68 publications
(28 citation statements)
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“…where σ in and σ out denote the standard deviations of the full backgrounds in intensities in the input original images and the propocessed images, respectively. Since the BSF index is used to evaluate the effectiveness of background suppression, the target regions are excluded when calculating [14,39]. The higher value of the BSF index, the better background suppression of the algorithm.…”
Section: Resultsmentioning
confidence: 99%
“…where σ in and σ out denote the standard deviations of the full backgrounds in intensities in the input original images and the propocessed images, respectively. Since the BSF index is used to evaluate the effectiveness of background suppression, the target regions are excluded when calculating [14,39]. The higher value of the BSF index, the better background suppression of the algorithm.…”
Section: Resultsmentioning
confidence: 99%
“…Multiple subspace learning is adopted to modify [39] in [40]. Taking edge and corner into consideration, Zhang et al [41] proposed a novel spatial-temporal tensor model to detect infrared small target. Motivated by human visual perception, Li et al [42] proposed a novel spatio-temporal saliency approach for dim moving target detection.…”
Section: ) Image Patch Association Based Methodsmentioning
confidence: 99%
“…Some methods extend the optimization problem on 2-D image patches in the spatial domain to 3-D spatial-temporal tensors (STT) [ 32 , 33 , 34 ]. For example, a novel edge and corner awareness-based spatial-temporal tensor (ECS-STT) model [ 35 ] was presented to suppress the strong edge and corner. These algorithms are more effective, but adopting the cues in the temporal dimension increases the convergence time of them.…”
Section: Related Workmentioning
confidence: 99%