Cognitive Image Fusion and Assessment

Toet, Alexander

doi:10.5772/14787

Cited by 17 publications

(22 citation statements)

References 97 publications

(101 reference statements)

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“…The TSSIM metric was developed in order to provide a measure of image quality within the domain of digital imagery, and has been shown to predict certain aspects of performance in natural scene images [21,44]. TSSIM is a measure of target-background similarity, and is computed between image areas of the same size (in pixels).…”

Section: Target-background Tssimmentioning

confidence: 99%

“…In addition to visual search, some studies have required subjects to make orientation judgements about objects in the scene [5] or perform scene categorization [5,19]. Within this body of research are cases for which fused images demonstrated superior performance to their component images [5,[17][18][19]21,22], and also cases where the fused images were no better [24] or even worse than their component images [5,20]. These differences across studies are likely due to differences in component image source sensors (e.g.…”

Section: Introductionmentioning

confidence: 96%

“…The most common viewing task used for this purpose is visual search, where the dependent measure is the latency or likelihood to detect a target within an image. For example, studies have examined subjects' performance in detecting and/or localizing human targets [5,[18][19][20][21][22][23], vehicles [5,[23][24][25], and buildings or other terrain features [17,23] within fused images. In addition to visual search, some studies have required subjects to make orientation judgements about objects in the scene [5] or perform scene categorization [5,19].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of fused imagery using eye movement-based measures of perceptual processing

et al. 2018

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Section: Target-background Tssimmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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Evaluation of fused imagery using eye movement-based measures of perceptual processing

et al. 2018

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“…For example, that of a retinotopic electro-optical (EO)/ infrared (IR) image fusion of a snake [12]. The human cognitive interest in image fusion over a variety of sensor modalities [13] must be appropriately assessed to ensure the perceptual fused image quality [14,15], such as that of satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

LANDSAT satellite image fusion metric assessment

Blasch

Liu

2011

Proceedings of the 2011 IEEE National Aerospace and Electronics Conference (NAECON)

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Image fusion is an important component of many applications such as inspection, night vision, medical diagnosis, and electro-optical (multispectral) targeting. In this paper, we build upon our previous results for image fusion metrics by applying the methods to satellite imagery versus night vision (visual and infrared imagery) and single-metric biomedical image fusion analysis. Multispectral examples include satellite imagery for land-cover analysis. In this paper, we explore a variety of metrics that have been used to quantify the performance of the fused image products. The exhaustive conclusive analysis over all possibilities is not realizable, so in this paper we group the various metrics into categories and demonstrate an application of the metrics to satellite LANDSAT imagery using an entropy-based image fusion method. From the many metrics analyzed, we found the Image Structural Similarity Based (ISSB) metrics are most useful.

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“…5,6 It appears that color makes it significantly easier to distinguish more details and evaluate a scene. 7 Mappings emphasizing hot targets facilitate the detection of persons and vehicles in a scene. The inherent color constancy provided by the method gives dynamic imagery a stable appearance when the sensor suite pans over or moves through a scene.…”

mentioning

confidence: 99%

Color remapping turns night into day

Toet¹

2012

SPIE Newsroom

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A real-time mapping technique enables full-color rendering of multispectral nighttime images, making them as clear and colorful as daylight ones.Night vision cameras are a vital source of information for a wide range of critical military and law enforcement applications such as surveillance, reconnaissance, intelligence gathering, and security. Currently, monochrome display of night imagery is still the standard. However, monochrome images often do not look natural, provide lower feature contrast, and they tend to induce visual illusions and fatigue. Intuitive color representations of night-vision imagery may alleviate these problems.The increasing availability of multi-spectral, night-vision systems has led to a growing interest in the color display of night imagery. Color may improve feature contrast and reduce visual clutter, thus enabling better scene recognition, object detection, and depth perception. Most current techniques to colorize multi-band, night-time imagery are computationally expensive or do not yield natural and stable color settings. To resolve these issues, we developed a simple color remapping technique that provides colored night-time imagery with an intuitive and stable appearance. 1 The method is computationally efficient and can easily be deployed in real-time.Our color remapping technique assumes a fixed relation between false color tuples and natural color triplets for bands near the visual spectrum. This allows its implementation as a simple color-table swapping operation. For bands that are not correlated with the visual spectrum, color remapping can be used to enhance the detectability of targets through contrast enhancement and color highlighting.We achieved color remapping by associating the multi-band sensor signal to an indexed false color image and swapping its color table with that of a regular daylight color image of a similar scene (see Figure 1). A wide range of environments can be represented with only a limited number of color tables. These tables need to be constructed only once before the system is deployed. The derivation of the color transformation requires a color photograph representing the intended operating theater or a similar, but not necessarily the same, environment. Then there are two options: either transfer the color statistics of this photograph to the false color multi-spectral image (when both images represent different but similar scenes), or establish a samplebased mapping between corresponding pixel values. 1 To achieve an efficient real-time implementation, we used indexed, color-image representations and performed all required Continued on next page

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Cognitive Image Fusion and Assessment

Cited by 17 publications

References 97 publications

Evaluation of fused imagery using eye movement-based measures of perceptual processing

Evaluation of fused imagery using eye movement-based measures of perceptual processing

LANDSAT satellite image fusion metric assessment

Color remapping turns night into day

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