The TRICLOBS Dynamic Multi-Band Image Data Set for the Development and Evaluation of Image Fusion Methods

Toet, Alexander; Hogervorst, Maarten A.; Pinkus, Alan R.

doi:10.1371/journal.pone.0165016

Cited by 9 publications

(7 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It may be the case that there are different types of environments in which this relationship differs and that we happen to have recorded (and evaluated) environments in which this relationship was quite similar. Given the limited dataset we used (and which is freely available for research purposes: [74]) this may not be surprising. It suggests that the system should be trained with scenes representing different types of environments in which LWIR can be used to infer the daytime color.…”

Section: Discussionmentioning

confidence: 99%

“…Both evaluation experiments were performed with the same set of 138 color fused multiband images. These images were obtained by fusing 23 three-band (visual, NIR, LWIR) TRICLOBS images (each representing a different scene, see [74]) with each of the six different color mappings investigated in this study (CTN, CTN-2 band, statistical, luminance-from-fit, salient-hot-targets, and rigid-3D-fit).…”

Section: Quantitative Evaluation Of Color Fusion Methodsmentioning

confidence: 99%

“…In this study we use the imagery obtained by the TRI-band color low-light observation (TRICLOBS) prototype imaging system for a comparative evaluation of the different fusion algorithms [73,74]. The TRICLOBS system provides co-axially registered visual, NIR (near infrared), and LWIR (longwave infrared or thermal) imagery (for an example, see Figure 1).…”

mentioning

confidence: 99%

“…This makes it possible to investigate how well a fusion scheme derived from one image (set) and reference (set) transfers to untrained images recorded in the same environment (and with the same sensor settings) to an untrained, new scene recorded in the same environment or in a different environment. The main training set consists of six images recorded in the MOUT (Military Operations in Urban Terrain) village of Marnehuizen in the Netherlands [74]. …”

mentioning

confidence: 99%

See 3 more Smart Citations

Improved Color Mapping Methods for Multiband Nighttime Image Fusion

Hogervorst¹,

Toet²

2017

J. Imaging

Self Cite

View full text Add to dashboard Cite

Previously, we presented two color mapping methods for the application of daytime colors to fused nighttime (e.g., intensified and longwave infrared or thermal (LWIR)) imagery. These mappings not only impart a natural daylight color appearance to multiband nighttime images but also enhance their contrast and the visibility of otherwise obscured details. As a result, it has been shown that these colorizing methods lead to an increased ease of interpretation, better discrimination and identification of materials, faster reaction times and ultimately improved situational awareness. A crucial step in the proposed coloring process is the choice of a suitable color mapping scheme. When both daytime color images and multiband sensor images of the same scene are available, the color mapping can be derived from matching image samples (i.e., by relating color values to sensor output signal intensities in a sample-based approach). When no exact matching reference images are available, the color transformation can be derived from the first-order statistical properties of the reference image and the multiband sensor image. In the current study, we investigated new color fusion schemes that combine the advantages of both methods (i.e., the efficiency and color constancy of the sample-based method with the ability of the statistical method to use the image of a different but somewhat similar scene as a reference image), using the correspondence between multiband sensor values and daytime colors (sample-based method) in a smooth transformation (statistical method). We designed and evaluated three new fusion schemes that focus on (i) a closer match with the daytime luminances; (ii) an improved saliency of hot targets; and (iii) an improved discriminability of materials. We performed both qualitative and quantitative analyses to assess the weak and strong points of all methods.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Quantitative Evaluation Of Color Fusion Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Improved Color Mapping Methods for Multiband Nighttime Image Fusion

Hogervorst¹,

Toet²

2017

J. Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“… Data source location The imagery was collected at different sites in the Netherlands. Data accessibility https://doi.org/10.6084/m9.figshare.c.3860689.v1 Related research articles See [3] See [2] See [1] …”

mentioning

confidence: 99%

The TNO Multiband Image Data Collection

Toet¹

2017

Data in Brief

Self Cite

162

View full text Add to dashboard Cite

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), near-infrared (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.

show abstract