Analysis of new top-hat transformation and the application for infrared dim small target detection
By X. Bai, Fugen Zhou. Pattern Recognition, 2010
702 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/0e3dd4a5c3ff0c16fa1b8c79e507cb6c728ae147
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In Aerospace Engineering
In Electrical and Electronic Engineering
Citations in the corpus
- Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection (2017, unknown): “[13] designed a new Top-Hat transformation using”
- ISNet: Shape Matters for Infrared Small Target Detection (2022, methodology): “Representative methods include HVS based methods, such as tri-layer local contrast measure (TLLCM) [4] and weighted strengthened local contrast measure (WSLCM) [12], filtering based methods [2, 9], as well as low-rank based methods, such as reweighted infrared patch-tensor (RIPT) [5], partial sum of”
- Small Infrared Target Detection Based on Weighted Local Difference Measure (2016, methodology): “Noticed that a small target usually emerges as a small bright region in an infrared image [8].”
- Asymmetric Contextual Modulation for Infrared Small Target Detection (2020, methodology): “For model-driven methods, we choose eleven methods including top-hat filter [1], local contrast method (LCM) [2],”