Spectral image analysis of mutual illumination between florescent objects

Tominaga, Shoji; Kato, Keiji; Hirai, Keita; Horiuchi, Takahiko

doi:10.1364/josaa.33.001476

Cited by 8 publications

(9 citation statements)

References 9 publications

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“…Until now, imaging systems have been developed separately as spectral imaging, omnidirectional imaging, and HDR imaging with different applications. For instance, with regards to spectral imaging, various types of multiband camera systems were proposed for the purposes of digital archiving, medicine, and natural scene analysis [6][7][8][9][10][11][12]. Omnidirectional imaging systems were proposed mainly for surveillance, intelligent transport systems, scene archiving [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

High-Dynamic-Range Spectral Imaging System for Omnidirectional Scene Capture

et al. 2018

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Omnidirectional imaging technology has been widely used for scene archiving. It has been a crucial technology in many fields including computer vision, image analysis and virtual reality. It should be noted that the dynamic range of luminance values in a natural scene is quite large, and the scenes containing various objects and light sources consist of various spectral power distributions. Therefore, this paper proposes a system for acquiring high dynamic range (HDR) spectral images for capturing omnidirectional scenes. The system is constructed using two programmable high-speed video cameras with specific lenses and a programmable rotating table. Two different types of color filters are mounted on the two-color video cameras for six-band image acquisition. We present several algorithms for HDR image synthesis, lens distortion correction, image registration, and omnidirectional image synthesis. Spectral power distributions of illuminants (color signals) are recovered from the captured six-band images based on the Wiener estimation algorithm. In this paper, we present two types of applications based on our imaging system: time-lapse imaging and gigapixel imaging. The performance of the proposed system is discussed in detail in terms of the system configurations, acquisition time, artifacts, and spectral estimation accuracy. Experimental results in actual scenes demonstrate that the proposed system is feasible and powerful for acquiring HDR spectral scenes through time-lapse or gigapixel omnidirectional imaging approaches. Finally, we apply the captured omnidirectional images to time-lapse spectral Computer Graphics (CG) renderings and spectral-based relighting of an indoor gigapixel image.

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Section: Introductionmentioning

confidence: 99%

High-Dynamic-Range Spectral Imaging System for Omnidirectional Scene Capture

et al. 2018

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“…Thus, assumptions or preprocessing can be required to make this task treatable. Some assumptions are upon the spectral reflectances of the surfaces, for example the use of uniformly colored surfaces [9,10,12,17]. Others are light-related regarding the number of light sources, and their spectral power distributions [9,12,16].…”

Section: Number Of Bouncesmentioning

confidence: 99%

“…In spectral imaging, Tominaga et al [17,28] proposed to use interreflections between fluorescent objects in order to characterize the spectral image component from the observed spectral radiance. Their method is based on a two-bounce bispectral interreflection model.…”

Section: Other Approachesmentioning

confidence: 99%

Interreflections in Computer Vision: A Survey and an Introduction to Spectral Infinite-Bounce Model

Deeb¹,

Muselet²,

Hébert³

et al. 2017

J Math Imaging Vis

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Interreflections are observed on concave objects or when multiple objects are located closely. In a vision system, interreflections can largely affect color values captured by the camera. Due to this fact, modeling interreflections is important for many vision applications. In this paper, we consider the problem of treating and modeling interreflections in the domain of computer vision. First, a survey of existing approaches in the state of the art is given. These approaches are detailed, discussed and compared. Most of the state of the art models take into consideration only two bounces of light between surface elements. We, afterwards, introduce a new interreflection model based on radiometric definitions. This model is the first one that takes into consideration an infinite number of light bounces between surface elements while providing image RGB values as a result. The accuracy of our model is studied by comparing it to real camera outputs. Thanks to our new model, the importance of using infinite bounces of light while studying interreflection, instead of only two bounces is demonstrated.

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“…This is due to the illumination factor that degrades the recognition performance. The Illumination indicates the amount of the light that is reflected or is radiated from a peculiar area in an image [18], [19]. The NIR and the VIS imaging have nonuniform brightness due to varying light conditions, causing degradation.…”

Section: Introductionmentioning

confidence: 99%

Advances in Cross-Spectral Iris Recognition Using Integrated Gradientface-Based Normalization

et al. 2019

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Cross-spectral iris recognition represents the ability of the system to identify the iris images acquired in different electromagnetic spectrums. An iris captured in the near-infrared spectrum (NIR) is matched with an iris obtained in the visual light spectrum (VIS) to boost the recognition performance. In cross-spectral iris recognition, the illumination factor between NIR and VIS images significantly degrades the recognition performance. Therefore, the existing method only achieved recognition performance with an equal error rate (EER) larger than 5%, and it is a challenging issue for cross-spectral performance to have EER below 5%. In this paper, we improve iris recognition performance by concatenating the Gradientfacesbased normalization technique (GRF) to a standard (conventional) iris recognition method to alleviate the illumination effect. In addition, we integrate the GRF with a Gabor filter, a difference of Gaussian (DoG) filter, and texture descriptors, namely a binary statistical image feature (BSIF) and a local binary pattern (LBP). The experimental results show that the GRF can boost the cross-spectral iris recognition performance with an EER equals to 1.69%. In addition, the best cross-spectral iris recognition performance is achieved when the GRF is integrated with the Gabor filter and the BSIF.

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Spectral image analysis of mutual illumination between florescent objects

Cited by 8 publications

References 9 publications

High-Dynamic-Range Spectral Imaging System for Omnidirectional Scene Capture

High-Dynamic-Range Spectral Imaging System for Omnidirectional Scene Capture

Interreflections in Computer Vision: A Survey and an Introduction to Spectral Infinite-Bounce Model

Advances in Cross-Spectral Iris Recognition Using Integrated Gradientface-Based Normalization

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