Infrared Patch-Image Model for Small Target Detection in a Single Image

By Chenqiang Gao, Deyu Meng, Yi Yang, Yongtao Wang, Xiaofang Zhou, Alexander G. Hauptmann. IEEE Transactions on Image Processing, 2013

1,179 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/dff4676c6d5ec404f291e250b51bae15fe3a5d61

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In Aerospace Engineering

1. Zhenming Peng
2. Yimian Dai
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4. Landan Zhang
5. Kobus Barnard
6. Fei Zhou
7. Siying Cao
8. Tianfang Zhang

In Computer Vision and Pattern Recognition

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2. Jungang Yang
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In Electrical and Electronic Engineering

1. Zhenming Peng
2. Yimian Dai
3. Yiquan Wu
4. Kobus Barnard
5. Fei Zhou
6. Landan Zhang
7. Wei An
8. Jungang Yang

Citations in the corpus

• Attentional Local Contrast Networks for Infrared Small Target Detection (2020, methodology): “, the most sparse [4] or salient [8], about the background, e.”
• Infrared Small Target Detection via Non-Convex Rank Approximation Minimization Joint l2, 1 Norm (2018, methodology): “The existing single-frame-based detection algorithms mainly fall into three main groups: methods of background-based assumption, methods of target-based assumption, and methods of target and background separating.”
• 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”
• Infrared Small Target Detection Utilizing the Multiscale Relative Local Contrast Measure (2018, background): “INFRARED (IR) small target detection plays an important role in precise guidance and early warning [1], [2].”
• Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection (2017, methodology): “Baselines and Parameter Settings: The proposed algorithm is compared with ten state-of-the-art solutions, including three filtering-based methods (Max-Median [10], Top-Hat [49], TDLMS [9]), three HVS-based methods (PFT [50], MPCM [19], WLDM [23]), and four recently developed low-rank methods (IPI [2”
• Dense Nested Attention Network for Infrared Small Target Detection (2021, methodology): “Typical methods include filtering-based methods [6], [9], local contrast measure based methods [10], [11], [12], [13], [14], [15], and low rank based methods [7], [8] [16], [17], [18], [19].”
• Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background (2023, background): “…system-based approaches assume that the saliency of a target is only re-lated to the local contrast of its surroundings [9], [10], [11], [12]; 3) optimization-based approaches transform the target detection task into a sparse low-rank tensor decomposition problem [13], [14], [15], [16], [17], [18].”
• Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm (2019, methodology): “…] MPCM [31] N = 3,5,7,9, mean filter size : 3 × 3 RLCM [19] ( K 1 , K 2 ) = ( 2,4 ) , ( 5,9 ) , and ( 9,16 ) IPI [20] Patch size : 50 × 50, sliding step : 10, λ = 1/ min ( m , n ) , ε = 10 Top-hat [12] Structure shape: disk, structure size: 3 × 3 LoG [29] σ = […”
• Infrared Dim and Small Target Detection via Multiple Subspace Learning and Spatial-Temporal Patch-Tensor Model (2021, methodology): “Finally, we can use an adaptive threshold to separate the target, which is defined as follows: [22]: where μ and σ denote the average and the standard deviation of the target image, and k is a constant determined experien-tially. ν min = 0 .”
• Small Infrared Target Detection Based on Weighted Local Difference Measure (2016, methodology): “Moreover, the target size may vary due to different types of targets, imaging distances, environments, and so on [1].”