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读论文系列:The TNO Multiband Image Data Collection

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文章目录

读论文系列:The TNO Multiband Image Data Collection1️⃣ 资料2️⃣ 文章翻译摘要Specifications Table 规格表Value of the Data 数据的价值Data 数据Experimental design, materials, and methods 3️⃣ 我的笔记目录结构TRICLOOBSTNOKayak第一部分第二部分 我的总结

读论文系列:The TNO Multiband Image Data Collection

1️⃣ 资料

说明:非常老牌的红外可见光融合数据集,值得一看官方仓库:https://figshare.com/articles/dataset/TNO_Image_Fusion_Dataset/1008029大佬笔记:https://blog.csdn.net/qq_43249953/article/details/139769505

2️⃣ 文章翻译

摘要

Despite of the ongoing interest in the fusion of multi-band images for surveillance applications and a steady stream of publications in this area, there is only a very small number of static registered multi-band test images (and a total lack of dynamic image sequences) publicly available for the development and evaluation of image fusion algorithms. To fill this gap, the TNO Multiband Image Collection provides intensified visual (390–700 nm), nearinfrared (700–1000 nm), and longwave infrared (8–12 μm) nighttime imagery of different military and surveillance scenarios, showing different objects and targets (e.g., people, vehicles) in a range of different (e.g., rural, urban) backgrounds. The dataset will be useful for the development of static and dynamic image fusion algorithms, color fusion algorithms, multispectral target detection and recognition algorithms, and dim target detection algorithms.

尽管人们对用于监控应用的多波段图像融合持续感兴趣,并且该领域的出版物源源不断,但只有极少数静态注册的多波段测试图像(完全缺乏动态图像序列)可供公开用于图像融合算法的开发和评估。为了填补这一空白,TNO多波段图像集提供了不同军事和监视场景的增强视觉(390-700nm)、近红外(700-1000nm)和长波红外(8-12μm)夜间图像,显示了不同背景(如农村、城市)中的不同物体和目标(如人、车辆)。该数据集将有助于开发静态和动态图像融合算法、颜色融合算法、多光谱目标检测和识别算法以及弱目标检测算法。

Specifications Table 规格表

标题内容
Subject areaDigital image processing - Image fusion
Type of dataVisual, near-infrared (NIR) and longwave infrared (LWIR) digital images representing different nighttime military and surveillance scenarios.
How data was acquiredThe images were acquired with different multiband camera systems. 这些图像是用不同的多波段相机系统采集的。
Data formatBMP, TIF, MP4
Experimental factorsThe images have been geometrically warped and registered so that corresponding image pairs have pixelwise correspondence. 图像已经过几何扭曲和配准,使得相应的图像对具有像素对应关系。
Experimental featuresThe imagery was collected in (semi-)darkness during several outdoor field trials in both rural and urban areas. 这些图像是在农村和城市地区的几次户外田间试验中在(半)黑暗中收集的。
Data source locationThe imagery was collected at different sites in the Netherlands. 这些图像是在荷兰的不同地点收集的。
Data accessibilityhttps://doi.org/10.6084/m9.figshare.c.3860689.v1
[1]A. Toet, J.K. IJspeert, A.M. Waxman, M. Aguilar, Fusion of visible and thermal imagery improves situational awareness, Displays 18 (2) (1997) 85–95. http://dx.doi.org/10.1016/S0141-9382(97)00014-0.
[2]A. Toet, Detection of dim point targets in cluttered maritime backgrounds through multisensor image fusion, in: W. R. Watkins, D. Clement, W.R. Reynolds (Eds.), Targets and Backgrounds: Characterization and Representation VIII, The International Society for Optical Engineering, Bellingham, WA, http://dx.doi.org/10.1117/12.478798.
[3]A. Toet, M.A. Hogervorst, A.R. Pinkus, The TRICLOBS dynamic multi-band image data set for the development and evaluation of image fusion methods, PLoS One 11 (12) (2016) e0165016. http://dx.doi.org/10.1371/journal.pone.0165016.

Value of the Data 数据的价值

The dataset will be useful for the development of

static and dynamic image fusion algorithms, 静态和动态图像融合算法color fusion algorithms, 颜色融合算法multispectral target detection and recognition algorithms, 多光谱目标检测和识别算法dim target detection algorithms. 弱小目标检测算法

Data 数据

The TNO Multiband Image Collection currently consists of three individual image sets:

The TNO Image Fusion Dataset The Kayak Image Fusion Sequence (parts I and II) The TRICLOBS Dynamic Multiband Image Dataset

The TNO Image Fusion Dataset [1] contains intensified visual (390–700 nm), near-infrared (7001000 nm), and longwave infrared (8–12 μm) nighttime imagery of different military and surveillance scenarios, showing different objects and targets (e.g., people, vehicles) in different (e.g., rural, urban) backgrounds.

TNO图像融合数据集[1]包含不同军事和监视场景的增强视觉(390-700nm)、近红外(7001000nm)和长波红外(8-12μm)夜间图像,显示了不同背景(如农村、城市)中的不同物体和目标(如人、车辆)。

The multimodal Kayak Image Fusion Sequence [2] contains registered visual, near-infrared and longwave infrared image sequences showing three approaching kayaks in a cluttered maritime background. Because of the variation in distance the targets (kayaks) vary from dim point targets to easily distinguishable objects.

多模式皮划艇图像融合序列[2]包含注册的视觉、近红外和长波红外图像序列,显示了三艘正在接近的皮划艇在杂乱的海上背景中。由于距离的变化,目标(皮划艇)从暗淡的点目标到易于区分的物体各不相同。

The TRICLOBS Dynamic Multiband Image Dataset [3] contains registered visual (400–700 nm), near-infrared (NIR, 700–1000 nm) and longwave infrared (LWIR, 8–14 μm) motion sequences of dynamic surveillance scenarios in an urban environment. To enable the development or realistic color remapping procedures, the dataset also contains color photographs of each of the three scenes. This dataset was collected during several field trials at three different locations and contains 16 motion sequences representing different military and civilian surveillance scenarios.

TRICLOBS动态多波段图像数据集[3]包含城市环境中动态监控场景的注册视觉(400-700nm)、近红外(NIR,700-1000nm)和长波红外(LWIR,8-14μm)运动序列。为了实现开发或逼真的颜色重映射过程,数据集还包含三个场景中每个场景的彩色照片。该数据集是在三个不同地点的几次现场试验中收集的,包含代表不同军事和民用监视场景的16个运动序列。

All three datasets include publications describing the registration conditions and the used camera systems in full detail. The data collection will be incrementally extended with new imagery when this becomes available. The images in this data collection can freely be used for research purposes, and may be used in publications without prior notice, provided this paper is properly referenced.

所有三个数据集都包括详细描述注册条件和使用的相机系统的出版物。当新的图像可用时,数据收集将逐步扩展。本数据集中的图像可自由用于研究目的,并可在不事先通知的情况下用于出版物,前提是正确引用本文。

Experimental design, materials, and methods

The original sensor signals were warped and subsampled to achieve pixelwise image registration.
原始传感器信号被扭曲和二次采样,以实现像素图像配准。

3️⃣ 我的笔记

目录结构

我感觉有必要分别列举一下目录结构。因为其一是TNO 中包含了三个数据集,内容混乱,其二是本身图片并不多,一一列举是实际的。
在这里插入图片描述
可见,并不是 TNO、Kayak、TRICLOBS 三个文件夹,而是一堆杂乱的结构。

TRICLOOBS

TRICLOBS子数据集对应着 Triclobs_images文件夹。

(base) kimshan@MacBook-Pro Triclobs_images % lsBallsMarne_06ReekBosniaMarne_07VeluweFarmMarne_09VlasakkersHouseMarne_11barbed_wire_1Kaptein_01Marne_15barbed_wire_2Kaptein_1123Marne_24houses_with_3_menKaptein_1654Movie_01jeep_in_smokeKaptein_19Movie_12pancake_houseMarne_01Movie_14soldier_behind_smokeMarne_02Movie_18soldiers_with_jeepMarne_03Movie_24square_with_housesMarne_04REFRENCES

总结一下,

场景可见光近红外远红外其他
Balls
屋外的黑色铁球
VIS.bmpNIR.bmpphoto.bmp
barbed_wire_1
人跑过铁丝网
R_Vis.tifG_NIR.tifB_LWIR.tifRGB.tif
barbed_wire_2
人跑过铁丝网
a_VIS-MarnehNew
_24RGB_1110.tif
b_NIR-MarnehNew
_24RGB_1110.tif
c_LWIR-MarnehNew
_24RGB_1110.tif
Bosnia
草地上的房子
VIS_R.bmpNIR_G.bmpLWIR_B.bmpdaylight_image.bmp
Farm
草地上的房子
Farm_Vis.bmpFarm_II.bmpFarm_IR.bmpphoto.bmp
House
灯热影响红外
VIS.bmpNIR.bmpphoto.bmp
houses_with_3_men
房子和三个人
VIS.bmpNIR.bmpLWIR.bmp
jeep_in_smoke
雾中的吉普
VIS_R.bmpNIR_G.bmpLWIR_B.bmp
Kaptein_01
黑夜中的门
Vis01.bmpNIR01.bmpIR01.bmpphoto.bmp
Kaptein_19
黑夜中的帐篷
Vis19.bmpNIR19.bmpIR19.bmpphoto.bmp
Kaptein_1123
黑夜中的门和人
Kaptein_
1123_Vis.bmp
Kaptein_
1123_II.bmp
Kaptein_
1123_IR.bmp
photo.bmp
Kaptein_1654
黑夜中的帐篷和人
Kaptein_
1654_Vis.bmp
Kaptein_
1654_II.bmp
Kaptein_
1654_IR.bmp
Kaptein_1654_
ref_with_man.bmp,
Kaptein_
1654_REF.bmp
Marne_01
白天的房子
Marne_01_Vis.bmpMarne_01_II.bmpMarne_01_IR.bmpMarne_01_REF.bmp
Marne_02
白天的房子
Marne_02_Vis.bmpMarne_02_II.bmpMarne_02_IR.bmpMarne_02_REF.bmp
Marne_03
白天的房子
Marne_03_Vis.bmpMarne_03_II.bmpMarne_03_IR.bmpMarne_03_REF.bmp
Marne_04
著名的吉普
Marne_04_Vis.bmpMarne_04_II.bmpMarne_04_IR.bmpMarne_04_REF.bmp
Marne_06
白天的房子
Marne_06_Vis.bmpMarne_06_II.bmpMarne_06_IR.bmpMarne_06_REF.bmp
Marne_07
白天的房子
Marne_07_Vis.bmpMarne_07_II.bmpMarne_07_IR.bmpMarne_07_REF.bmp
Marne_08
白天的房子
Marne_08_Vis.bmpMarne_08_II.bmpMarne_08_IR.bmpMarne_08_REF.bmp
Marne_09
白天的房子
Marne_09_Vis.bmpMarne_09_II.bmpMarne_09_IR.bmpMarne_09_REF.bmp
Marne_11
白天的房子
Marne_11_Vis.bmpMarne_11_II.bmpMarne_11_IR.bmp
Marne_15
白天的房子
Marne_15_Vis.bmpMarne_15_II.bmpMarne_15_IR.bmp
Marne_24
铁丝和奔跑的人
Marne_15_Vis.bmpMarne_15_II.bmpMarne_15_IR.bmp
Movie_01
白天的街道
Movie_01_Vis.bmpMovie_01_II.bmpMovie_01_IR.bmpMovie_01_REF.bmp
Movie_12
房子
Movie_12_Vis.bmpMovie_12_II.bmpMovie_12_IR.bmpMovie_12_REF.bmp
Movie_14
房子和人
Movie_14_Vis.bmpMovie_14_II.bmpMovie_14_IR.bmpMovie_14_REF.bmp
Movie_18
房子和人和车
Movie_18_Vis.bmpMovie_18_II.bmpMovie_18_IR.bmpMovie_18_REF.bmp
Movie_24
房子
Movie_24_Vis.bmpMovie_24_II.bmpMovie_24_IR.bmp
pancake_house
白天的街道
VIS.tifNIR.tifphoto.tif
Reek
白天的房子
Reek_Vis.bmpReek_II.bmpReek_IR.bmpReek_REF.bmp
soldier_behind_smoke
雾中的士兵
VIS-MarnehNew_
15RGB_603.tif
NIR-MarnehNew_
15RGB_603.tif
LWIR-MarnehNew_
15RGB_603.tif
RGB-MarnehNew_
15RGB_603.tif
soldiers_with_jeep
士兵和吉普
Jeep_Vis.bmpJeep_II.bmpJeep_IR.bmp
square_with_houses
房子和方框
VIS.bmpNIR.bmpLWIR.bmp
Veluwe
望远镜中的房子
VIS.bmpNIR.bmpphoto.bmp
Vlasakkers
房子
VIS.tifNIR.tifphoto.tif

TNO

在Athena_imagesz合格文件夹中的REFRENCES里边,有这样的 pdf:Report_TNO-DV-2007-A329.pdf。所以我认为这个文件夹里边都是 TNO 下的TNO的。

(base) kimshan@MacBook-Pro Athena_images % ls2_men_in_front_of_househelicopterAPC_1lakeAPC_2man_in_doorwayAPC_3soldier_behind_smoke_1APC_4soldier_behind_smoke_2REFRENCESsoldier_behind_smoke_3airplane_in_treessoldier_in_trench_1bunkersoldier_in_trench_2heather

还是整理一下:

场景可见光近红外远红外其他
2_men_in_front
_of_house
房子和两个人
VIS_meting003_r.bmpIR_meting003_g.bmpmeting003_rg.bmp
airplane_in_trees
树中的飞机
vis.bmpir.bmp
APC_1/view_1
装甲车
VIS_fk_06_005.bmpIR_fk_06_005.bmpfk_06_005_rg.bmp
APC_1/view_2
装甲车和人
VIS_fk_ref_01_005.bmpIR_fk_ref_01_005.bmpfk_ref_01_005_rg.bmp
APC_1/view_3
装甲车
VIS_fk_ref_02_005.bmpIR_fk_ref_02_005.bmpfk_ref_02_005_rg.bmp
APC_2/view_1
装甲车
1_fk_ge_03_005.bmp2_fk_ge_03_005.bmpfk_ge_03_005_rg.bmp
APC_2/view_2
装甲车
1_fk_ge_04_005.bmp2_fk_ge_04_005.bmpfk_ge_04_005_rg.bmp
APC_2/view_3
装甲车
1_fk_ge_06_005.bmp2_fk_ge_06_005.bmpfk_ge_06_005_rg.bmp
APC_3view_1
装甲车
VIS_fk_bar_06_005.bmpIR_fk_bar_06_005.bmpfk_bar_06_005_rg.bmp
APC_3view_2
装甲车
VIS_fk_bar_01_005.bmpIR_fk_bar_01_005.bmpfk_bar_01_005_rg.bmp
APC_3view_3
装甲车
VIS_fk_bar_05_005.bmpIR_fk_bar_05_005.bmpfk_bar_05_005_rg.bmp
APC_4
装甲车
VIS_fennek01_005.bmpIR_fennek01_005.bmpfennek01_005_rg.bmp
bunker
树中建筑
bunker_r.bmpIR_bunker_g.bmpVIS_bunker-rg.bmp
heather
树中道路
VIS_hei_vis_r.bmpIR_hei_vis_g.bmphei_vis-rg.bmp
helicopter
直升机
VIS_helib_011.bmpIR_helib_011.bmphelib_011_rg.bmp
lake
VIS_lake_r.bmpIR_lake_g.bmplake_rg.bmp
man_in_doorway
男人在门口
VIS_maninhuis_r.bmpIR_maninhuis_g.bmpmaninhuis_rg.bmp
soldier_behind_smoke_1
烟中狙击手
VIS_meting012-1200_r.bmpIR_meting012-1200_g.bmpmeting012-1200_rg.bmp
soldier_behind_smoke_2
烟中狙击手
VIS_meting012-1500_r.bmpIR_meting012-1500_g.bmpmeting012-1500_rg.bmp
soldier_behind_smoke_3
烟中狙击手
VIS_meting012-1700_r.bmpIR_meting012-1700_g.bmpmeting012-1700_rg.bmp
soldier_in_trench_1
桥里的男人
VIS_meting016_r.bmpIR_meting016_g.bmpmeting016_rg.bmp
soldier_in_trench_2
桥里的男人
VIS_meting055_r.bmpIR_meting055_g.bmpmeting055_rg.bmp

Kayak

那我就认为剩下的都属于Kayak子集。那这就又很杂乱了。我还是分两部分:

第一部分是Kayak的特色,一些序列,第二部分是剩下的杂项。
第一部分
Introduction Intensified visual images have the extensions VIS and/or RNear Infrared images have the extensions NIR and/or GThermal images have the extensions IR , RAD and/or BDHV images are false color (R,G,0) images corresponding to (VIS,NIR,0) Datasets DHV_images/Fire_sequenceFEL_images/Duine_sequenceFEL_images/Nato_camp_sequenceFEL_images/Tree_sequence

总结

名称介绍VISDHVThermal/RAD
DHV_images/Fire_sequence/Part1烟火DHV2.bmp ~ HDV29.bmpRAD2.bmp ~ RAD29.bmp
DHV_images/Fire_sequence/Part2烟火DHVheli0.bmp ~ DHVheli16.bmpRADheli0.bmp ~ RADheli16.bmp
DHV_images/Fire_sequence/Part3烟火DHVheli20.bmp ~ DHVheli80.bmpRADheli20.bmp ~ RADheli80.bmp
FEL_images/Duine_sequence路上人7400v.bmp ~ 7422v.bmp7400i.bmp ~ 7422i.bmp
FEL_images/Nato_camp_sequence路上人1800v.bmp ~ 1831v.bmp1800i.bmp ~ 1831i.bmp
FEL_images/Tree_sequence林中人4900v.bmp ~ 4918i.bmp4900i.bmp ~ 4918i.bmp
第二部分
场景与路径可见光近红外远红外其他
tank
著名的坦克
Vis.tifLWIR.tif
DHV_images/bench
河边人
VIS_37dhvR.bmpNIR_37dhvG.bmpIR_37rad.bmp
DHV_images/sandpath
林中人
VIS_18dhvR.bmpNIR_18dhvG.bmpIR_18rad.bmp
DHV_images/wall
VIS_163dhvR.BMPNIR_163dhvG.BMPIR_163rad.bmp

我的总结

TNO其实是一个散装的数据集,里边包含了三个小数据集。TNO/TNO,TNO/TRICLOOBS 以及 TNO/Kayak 的一部分,结构比较简单,就是可见光,近红外,远红外,照片或者 fask color 组成。其中会有缺省的内容。TNO/Kayak的另一部分都是连续视频的截图,包含了好几个序列。这里边的“烟火”序列,他的可见光和近红外分别组成了 R 通道和 G 通道的 fake color 图片,需要再解析一下。TNO 数据集中文件的命名很随意,有的是 VIS,IR 这样的,其他大部分也加入了场景描述以及序列 id,并且文件夹嵌套混乱,通过程序自动读入比较困难,需要手工挑选图片。

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