Multiscale patch-based contrast measure for small infrared target detection

By Yantao Wei, Xinge You, Hong Li. Pattern Recognition, 2016

722 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Atomic and Molecular Physics, and Optics, Ophthalmology

View Paper: https://www.semanticscholar.org/paper/c68791203e5cc93109fd484096e8151424846bfc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Zhenming Peng
2. Landan Zhang
3. Kobus Barnard
4. Fei Zhou
5. Jocelyn Chanussot
6. Danfeng Hong
7. Xin Wu
8. Siying Cao

In Atomic and Molecular Physics, and Optics

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

In Ophthalmology

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

Citations in the corpus

• Attentional Local Contrast Networks for Infrared Small Target Detection (2020, methodology): “For nonlearning model-driven methods, we choose stable multisubspace learning (SMSL) [19], facet kernel and random walker (FKRW) [44], MPCM [20], infrared patchimage model (IPI) [4], nonnegative IPI model via partial sum minimization of singular values (NIPPS) [10], and reweighted infrared patch-ten”
• Infrared Small Target Detection Utilizing the Multiscale Relative Local Contrast Measure (2018, methodology): “4 using other five state-of-theart algorithms, including DoG [6], MPCM [9], ILCM [4], NLCM [11], and WLDM [12].”
• Asymmetric Contextual Modulation for Infrared Small Target Detection (2020, methodology): “Following the main practices in this field [10, 33], we model infrared small target detection as a semantic segmentation problem.”
• A Local Contrast Method for Infrared Small-Target Detection Utilizing a Tri-Layer Window (2020, methodology): “…an average operation to suppress PNHB; Qin and Li [15] indicated that only some maximal pixels should be considered during averaging; Wei et al. [16] merged two corresponding directions together; Han et al. [17] proposed a relative local contrast method; Nie et al. [18] considered the local…”
• Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm (2019, methodology): “[12] Structure shape: disk, structure size: 3 × 3 LoG [29] 0.72,0.86,1.03,1.24,1.49,1.79,2.14 [31 3,5,7,9 N = , mean filter size: 3 × 3 0.60,0.72,0.86,1.03,1.24,1.49,1.79,2.14,2.57,3.09,3.71 ] 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 )…”
• UIU-Net: U-Net in U-Net for Infrared Small Object Detection (2022, background): “…vision system-based method is perfectly adequate for the object luminance is relatively large and presents a more obvious difference with the surrounding background, e.g., infrared patch image (IPI) [7], local contrast mechanism (LCM) [8], and multi-scale patch-based contrast measure (MPCM) [9].”
• Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background (2023, methodology): “…[8]; 2) human visual 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],…”
• Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection (2017, methodology): “MPCM and WLDM failed to achieve good results because they suffered from a phenomenon we named rare structure effect which is caused by the inaccuracy of the local dissimilarity measure and often happens when the target is extremely dim.”
• 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.”