&lt;title&gt;Beyond third generation: a sensor-fusion targeting FLIR pod for the F/A-18&lt;/title&gt;

Krebs, William K.; Scribner, Dean A.; Miller, Geoffrey M.; Ogawa, James S; Schuler, Jonathon M.

doi:10.1117/12.303673

Cited by 33 publications

(27 citation statements)

References 6 publications

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“…This may lead to a performance that is even worse compared to single band imagery alone (Sinai et al, 1999a). Experiments have indeed convincingly demonstrated that a false color rendering of fused night-time imagery which resembles natural color imagery significantly improves observer performance and reaction times in tasks that involve scene segmentation and classification (Essock et al, 1999;Sinai et al, 1999b;Toet et al, 1997a;Toet & IJspeert, 2001;Vargo, 1999;White, 1998), whereas color mappings that produce counterintuitive (unnaturally looking) results are detrimental to human performance (Krebs et al, 1998;Toet & IJspeert, 2001;Vargo, 1999). One of the reasons often cited for inconsistent color mapping is a lack of physical color constancy (Vargo, 1999).…”

Section: Color Representation Of Fused Imagerymentioning

confidence: 99%

“…Simply mapping the signals from different nighttime sensors (sensitive in different spectral wavebands) to the individual channels of a standard color display or to the individual components of perceptually decorrelated color spaces, sometimes preceded by principal component transforms or followed by a linear transformation of the color pixels to enhance color contrast, usually results in imagery with an unnatural color appearance (e.g. Howard et al, 2000;Krebs et al, 1998;Li et al, 2004;Schuler et al, 2000;Scribner et al, 2003). More intuitive color schemes may be obtained through opponent processing through feedforward center-surround shunting neural networks similar to those found in vertebrate color vision (Aguilar et al, 1998;Aguilar et al, 1999;Fay et al, 2000a;Fay et al, 2000b;Huang et al, 2007;Warren et al, 1999;Waxman et al, 1995;Waxman et al, 1997;Waxman et al, 1998).…”

Section: Color Representation Of Fused Imagerymentioning

confidence: 99%

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Cognitive Image Fusion and Assessment

Toet¹

2011

Image Fusion

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Section: Color Representation Of Fused Imagerymentioning

confidence: 99%

Section: Color Representation Of Fused Imagerymentioning

confidence: 99%

Cognitive Image Fusion and Assessment

Toet¹

2011

Image Fusion

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“…This may lead to a performance that is even worse compared to single band imagery alone [28,29]. Experiments have indeed demonstrated that a false color rendering of fused nighttime imagery which resembles realistic color imagery significantly improves observer performance and reaction times in tasks that involve scene segmentation and classification [30][31][32][33], and the simulation of color depth cues by varying saturation can restore depth perception [34], whereas color mappings that produce counter-intuitive (unrealistically looking) results are detrimental to human performance [30,35,36]. One of the reasons often cited for inconsistent color mapping is a lack of physical color constancy [35].…”

Section: Introductionmentioning

confidence: 99%

Improved Color Mapping Methods for Multiband Nighttime Image Fusion

Hogervorst¹,

Toet²

2017

J. Imaging

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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.

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“…False color rendering of night-time imagery that agrees with natural daytime colors (intuitively correct color mapping) significantly improves observer performance and reaction times in tasks that involve scene segmentation and classification [1][2][3][4][5][6] . However, color mappings that do not result in natural object colors (counterintuitive color mapping) seriously degrade situational awareness 4,5,7 . One of the main reasons is the counter intuitive appearance of scenes rendered in some artificial color schemes and the lack of color constancy 5 .…”

Section: Introductionmentioning

confidence: 99%

Transferring color to single-band intensified night vision images

Toet

2004

Enhanced and Synthetic Vision 2004

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We present a method to give single band intensified nightvision imagery a natural day-time color appearance. For input, the method requires a true color RGB source image and a grayscale nightvision target image. The source and target image are both transformed into a perceptually decorrelated color space. In this color space a best matching source pixel is determined for each pixel of the target image. The matching criterion uses the first order statistics of the luminance distribution in a small window around the source and target pixels. Once a best matching source pixel is found, its chromaticity values are assigned (transferred) to the target pixel while the original luminance value of the target pixel is retained. The only requirement of the method is that the compositions of the source and target scenes resemble each other.

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<title>Beyond third generation: a sensor-fusion targeting FLIR pod for the F/A-18</title>

Cited by 33 publications

References 6 publications

Cognitive Image Fusion and Assessment

Cognitive Image Fusion and Assessment

Improved Color Mapping Methods for Multiband Nighttime Image Fusion

Transferring color to single-band intensified night vision images

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