&lt;title&gt;Color night vision: fusion of intensified visible and thermal IR imagery&lt;/title&gt;

Waxman, Allen M.; Fay, David A.; Gove, Alan N.; Seibert, Michael; Racamato, Joseph P.; Carrick, James E.; Savoye, E. D.

doi:10.1117/12.212755

Cited by 56 publications

(42 citation statements)

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“…The contrast of the resulting color imagery can be improved by mapping a grayscale fused representation of the individual image bands to the luminance component of the resulting color images. The color transfer method presented here can also be applied to remap the color distribution of imagery resulting from existing color fusion methods [2][3][4][5][6][7][8][9][10][11][12][13][14] . A large number of full color examples with scenes of different composition is given elsewhere 24 .…”

Section: Discussionmentioning

confidence: 99%

“…The rapid development of multi-band infrared and visual nightvision systems has led to an increased interest in color fused ergonomic representations of multiple sensor signals [2][3][4][5][6][7][8][9][10][11][12][13][14] . Simply mapping multiple spectral bands of imagery into a three dimensional color space already generates an immediate benefit, since the human eye can discern several thousand colors, whereas it can only distinguish about 100 shades of grey at any instance.…”

Section: Introductionmentioning

confidence: 99%

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<title>Color the night: applying daytime colors to nighttime imagery</title>

Toet

2003

SPIE Proceedings

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We present a method to give (fused) multiband night-time imagery a natural day-time color appearance. For input, the method requires a false color RGB image that is produced by mapping 3 individual bands (or the first 3 principal components) of a multiband nightvision system to the respective channels of an RGB image. The false color RGB nightvision image is transformed into a perceptually decorrelated color space. In this color space the first order statistics of a natural color image (target scene) are transferred to the multiband nightvision image (source scene). To obtain a natural color representation of the multiband night-time imagery, the compositions of the source and target scenes should resemble each other to some degree. The inverse transformation to RGB space yields a nightvision image with a day-time color appearance. The luminance contrast of the resulting color image can be enhanced by replacing its luminance component by a grayscale fused representation of the three input bands.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

<title>Color the night: applying daytime colors to nighttime imagery</title>

Toet

2003

SPIE Proceedings

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“…The second role of retina processing is to fuse the responses of independent channels in a way that either extracts complementary information (decorrelation) or enhances channel similarities [34]. This inter-channel fusion is also performed using a model of center-surround competition in a fashion similar to the color opponent process between rods and cones in the human eye.…”

Section: Fig 16mentioning

confidence: 99%

System-On-Chip for Biologically Inspired Vision Applications

Park

Maashri

Irick

et al. 2012

IPSJ Transactions on System LSI Design Methodology

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Neuromorphic vision algorithms are biologically-inspired computational models of the primate visual pathway. They promise robustness, high accuracy, and high energy efficiency in advanced image processing applications. Despite these potential benefits, the realization of neuromorphic algorithms typically exhibit low performance even when executed on multi-core CPU and GPU platforms. This is due to the disparity in the computational modalities prominent in these algorithms and those modalities most exploited in contemporary computer architectures. In essence, acceleration of neuromorphic algorithms requires adherence to specific computational and communicational requirements. This paper discusses these requirements and proposes a framework for mapping neuromorphic vision applications on a System-on-Chip, SoC. A neuromorphic object detection and recognition on a multi-FPGA platform is presented with performance and power efficiency comparisons to CMP and GPU implementations.

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“…This opponent-sensor color image fusion algorithm has proven successful, not only for processing multi-sensor remote sensing imagery, but also for creating a color night vision capability through fusing visible and thermal infrared imagery [8,9,10].…”

Section: Hvs Models For Image Fusionmentioning

confidence: 99%