Statistics for Imaging, Optics, and Photonics

Bajorski, Peter

doi:10.1002/9781118121955

Cited by 43 publications

(40 citation statements)

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“…This is relevant to the classification of spatial reflectance patterns because those principal components having low variance are of little importance in Eq. (3) and they are often associated with spatial and temporal noise from the multispectral imaging system [37][38][39]58]. The number k of principal components is an important parameter in Eqs.…”

Section: Principal Component Analysis Similarity Factors For Reflectamentioning

confidence: 99%

“…In general, only the first k eigenvectors are taken in Eq. (1) using a stopping rule [30,37,42] that reduces the dimensionality of the original reflectance data set from 30 (as much dimensions as wavelength intervals) to k ( )…”

Section: Principal Component Analysis Of Reflectance Spectramentioning

confidence: 99%

“…The multispectral imaging system has a spatial resolution of 14.4 µm per pixel. Spectral calibration was done by using the Macbeth color checker chart [30,37,58]. Fig.…”

Section: Multispectral Imaging Acquisition Systemmentioning

confidence: 99%

“…It is a PCA-based approach that is a useful technique for pattern matching which quantifies the similarity between two data sets in many industrial applications [32,33,35]. Principal component analysis (PCA) itself is an effective statistical method that has been widely used in multivariate image analysis for dimensionality reduction, data compression, classification, visualization, noise reduction, etc [30,32,[36][37][38][39]. In color science, PCA is also widely applied to uncover the spectral bands of colorants.…”

Section: Introductionmentioning

confidence: 99%

“…In color science, PCA is also widely applied to uncover the spectral bands of colorants. PCA decomposes reflectance spectra into a linear combination of few uncorrelated basis functions or eigenvectors over a large number of reflectance data sets [32,36,37,40,41]. The first eigenvectors explain most of the variance within the data set [30,37,40,42].…”

Section: Introductionmentioning

confidence: 99%

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Classification of peacock feather reflectance using principal component analysis similarity factors from multispectral imaging data

Medina¹,

Díaz²,

Vukusic³

2015

Opt. Express

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Iridescent structural colors in biology exhibit sophisticated spatially-varying reflectance properties that depend on both the illumination and viewing angles. The classification of such spectral and spatial information in iridescent structurally colored surfaces is important to elucidate the functional role of irregularity and to improve understanding of color pattern formation at different length scales. In this study, we propose a non-invasive method for the spectral classification of spatial reflectance patterns at the micron scale based on the multispectral imaging technique and the principal component analysis similarity factor (PCASF). We demonstrate the effectiveness of this approach and its component methods by detailing its use in the study of the angle-dependent reflectance properties of Pavo cristatus (the common peacock) feathers, a species of peafowl very well known to exhibit bright and saturated iridescent colors. We show that multispectral reflectance imaging and PCASF approaches can be used as effective tools for spectral recognition of iridescent patterns in the visible spectrum and provide meaningful information for spectral classification of the irregularity of the microstructure in iridescent plumage.

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Section: Principal Component Analysis Similarity Factors For Reflectamentioning

confidence: 99%

Section: Principal Component Analysis Of Reflectance Spectramentioning

confidence: 99%

“…The multispectral imaging system has a spatial resolution of 14.4 µm per pixel. Spectral calibration was done by using the Macbeth color checker chart [30,37,58]. Fig.…”

Section: Multispectral Imaging Acquisition Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Classification of peacock feather reflectance using principal component analysis similarity factors from multispectral imaging data

Medina¹,

Díaz²,

Vukusic³

2015

Opt. Express

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Investigating color appearance in optical see‐through augmented reality

Hassani

Murdoch

2019

Color Research & Application

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Color appearance for real objects has been studied over decades and it has been well modeled. However, in augmented reality (AR) environments, virtual content is added to a real background and a mixed appearance is perceived. In this research, we studied color appearance in AR and investigated the applicability of the CAM16 color appearance model, one of the most comprehensive current color appearance models, in an AR environment. Using a benchtop optical mixing apparatus as an AR simulator, objective measurements of mixed colors in AR were performed. Then a psychophysical color matching experiment was performed with combinations of mixed foreground and background colors. The results showed that CAM16 is not accurate in predicting the color appearance in AR environment; therefore, it was modified with the addition of chromatic simultaneous contrast, resulting in an improved fit to the AR experiment data. A second psychophysical color matching experiment was performed on a single display to compare the color perception in AR with color perception in real world from a single display.

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Fast Top-k Distance-Based Outlier Detection on Uncertain Data

Shaikh

Kitagawa

2013

Web-Age Information Management

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Statistics for Imaging, Optics, and Photonics

Cited by 43 publications

References 0 publications

Classification of peacock feather reflectance using principal component analysis similarity factors from multispectral imaging data

Classification of peacock feather reflectance using principal component analysis similarity factors from multispectral imaging data

Investigating color appearance in optical see‐through augmented reality

Fast Top-k Distance-Based Outlier Detection on Uncertain Data

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