Spectral Reflectance Of Object Research Articles

Visualization of Lighting Quality and Object Appearance When Using Multichannel Light Sources

ABSTRACT The availability of quasi-monochromatic Light Emitting Diodes (LEDs) with peak wavelengths distributed over the visible spectrum has opened the way to the development of multichannel LED light sources generating tunable illumination spectra. As the color appearance of an object is strongly influenced by the spectral power distribution of the light source, these multichannel light sources are also commonly used as a research tool in color quality studies. In a number of studies, spectrum optimization algorithms are applied in order to generate a target spectrum directly. Other papers report on the calculation of an illumination spectrum with the aim to optimize one or a set of general quality metrics such as the luminous efficacy of radiation and the color rendering index Ra or Rf. The approach followed in this paper is to explore and visualize, for a given multichannel LED light source, the lighting quality parameters which can be reached within the chromaticity gamut for a selected object. For any predefined target white point chromaticity, the illumination metamers are calculated analytically within the constraints defined by the multichannel LED light source under consideration. The solution space is sampled with a brute force method and the values of any general or specific color quality index can be evaluated. For a predefined object spectral reflectance (or one typical spectral reflectance representative for a set of objects), the object chromaticity gamut is derived. Several object chromaticity targets within the gamut, such as the memory color of familiar objects, can be selected and the impact of “local” variations of the hue and chroma within this gamut on the general and special lighting quality indices and on the user preference can be checked. This can simplify the selection of an object-based optimum illumination spectrum. The approach is illustrated in practice using a five-channel LED light source which was developed in-house.

CIELAB color space boundaries under theoretical spectra and 99 test color samples

AbstractA color space is a three‐dimensional representation of all the possible color percepts. The CIE 1976 L*a*b* is one of the most widely used object color spaces. In CIELAB, lightness L* is limited between 0 and 100, while a* and b* coordinates have no fixed boundaries. The outer boundaries of CIELAB have been previously calculated using theoretical object spectral reflectance functions and the CIE 1931 and 1964 observers under the CIE standard illuminants D50 and D65. However, natural and manufactured objects reflect light smoothly as opposed to theoretical spectral reflectance functions. Here, data generated from a linear optimization method are analyzed to re‐evaluate the outer boundaries of the CIELAB. The color appearance of 99 test color samples under theoretical test spectra has been calculated in the CIELAB using CIE 1931 standard observer. The lightness L* boundary ranged between 6 and 97, redness‐greenness a* boundary ranged between −199 and 270, and yellowness‐blueness b* boundary ranged between −74 and 161. The boundary in the direction of positive b* (yellowness) was close to the previous findings. While the positive a* (redness) boundary exceeded previously known limits, the negative a* (greenness) and b* (blueness) boundaries were lower than the previously calculated CIELAB boundaries. The boundaries found here are dependent on the color samples used here and the spectral shape of the test light sources. Irregular spectral shapes and more saturated color samples can result in extended boundaries at the expense of computational time and power.

Spectral reflectance recovery using optimal illuminations.

The spectral reflectance of objects provides intrinsic information on material properties that have been proven beneficial in a diverse range of applications, e.g., remote sensing, agriculture and diagnostic medicine, to name a few. Existing methods for the spectral reflectance recovery from RGB or monochromatic images either ignore the effect from the illumination or implement/optimize the illumination under the linear representation assumption of the spectral reflectance. In this paper, we present a simple and efficient convolutional neural network (CNN)-based spectral reflectance recovery method with optimal illuminations. Specifically, we design illumination optimization layer to optimally multiplex illumination spectra in a given dataset or to design the optimal one under physical restrictions. Meanwhile, we develop the nonlinear representation for spectral reflectance in a data-driven way and jointly optimize illuminations under this representation in a CNN-based end-to-end architecture. Experimental results on both synthetic and real data show that our method outperforms the state-of-the-arts and verifies the advantages of deeply optimal illumination and nonlinear representation of the spectral reflectance.

Spectral reflectance estimation from one RGB image using self-interreflections in a concave object.

Light interreflections occurring in a concave object generate a color gradient that is characteristic of the object's spectral reflectance. In this paper, we use this property in order to estimate the spectral reflectance of matte, uniformly colored, V-shaped surfaces from a single RGB image taken under directional lighting. First, simulations show that using one image of the concave object is equivalent to, and can even outperform, the state-of-the-art approaches based on three images taken under three lightings with different colors. Experiments on real images of folded papers were performed under unmeasured direct sunlight. The results show that our interreflection-based approach outperforms existing approaches, even when the latter are improved by a calibration step. The mathematical solution for the interreflection equation and the effect of surface parameters on the performance of the method are also discussed in this paper.

Spectral reflectance luminous efficacy

Due to the lack of quantitative evaluation of lighting efficiency and lack of theoretical guidance on spectrum mechanism, there exists huge energy consumption in landscape lighting. This study aims to quantify landscape lighting effect and define efficiency evaluation indicator by studying the relationship between light sources’ spectrum and object's spectral reflectance curves from spectrum aspect. Traditional landscape lighting and Gaussian models of light sources’ spectrum's effect on objects show general rules. Relative high saturated light sources are good for reshaping lighting, while low saturated light sources for reproducing lighting. As most landscapes in a small region are very saturated but less in color types, the concept of spectral reflectance luminous efficacy rather than traditional luminous efficacy is proposed for evaluating landscape lighting efficiency. And comprehensive spectral reflectance luminous efficacy considering weights of landscapes is used to evaluate relative complex scenes. For different landscapes with different spectral reflectance curves, traditional high luminous efficacy lighting may not always achieve better efficiency. For some landscapes, matching light sources’ efficiency would be much higher than traditional light sources. Landscape lighting effect and lighting efficiency could be quantitatively predicted and evaluated.

Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations. Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data, which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects. After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals, an interval-valued fuzzy c-means (FCM) clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study. Such a process can significantly improve the clustering effect; specifically, the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results. Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region, China, and the TM sensor for Yushu, Qinghai, China, were conducted, as well as experiments involving the conventional FCM algorithm, the results of which were used for comparative analysis. Next, a supervised classification method was used to validate the clustering results. The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.

Recovery of spectral reflectances of imaged objects by the use of features of spectral reflectances

Recovery of spectral reflectances of objects being imaged through the use of sensor responses is important to reproduce color images under various illuminations. Although the Wiener estimation is usually used for the recovery, the recovery performance of the estimation depends on the autocorrelation matrix of the spectral reflectances and the noise present in an image acquisition system. The purpose of the present paper is to show that the Wiener estimation with the noise variance estimated by the previous proposal [IEEE Trans. Image Process. 16, 1848 (2006)] and with the autocorrelation matrix that uses the features of the spectral reflectances recovered by the previous method is very effective in greatly improving the performance.

Optimal illumination for discriminating objects with different spectra

Color differences are determined by illumination, the spectral reflectance of objects, and the spectral sensitivity of the imaging sensor. We explore the optimal illumination conditions that best separate one object from another. Given two objects with distinct spectra, we derive the optimal illumination spectrum to maximize their color distance with a plain RGB camera. In practice, it is crucial to compose the most appropriate illuminations using available lighting sources, since creating an arbitrary illumination spectrum is unrealistic. Therefore, we derive the optimal linear combination of the provided illumination sources. Finally, we verify the effectiveness of the methods through experiments.

Recovery of spectral reflectances of objects being imaged by multispectral cameras

Acquisition of spectral information of objects being imaged through the use of sensor responses is important to reproduce color images under various illuminations. In the past several models have been proposed to recover the spectral reflectances from sensor responses. The accuracy of the spectral reflectances recovered by five different models is compared by using multispectral cameras. It is shown that the Wiener estimation that uses the noise variance estimated as proposed in IEEE Trans. Image Process.15, 1848 (2006) recovers the spectral reflectances more accurately than the others when the test samples are different from learning samples.

Fundamental Considerations related to Chromatic Adaptation

There remain some important issues related to the von Kries model which have not been clarified up until now. This article discusses fundamental mathematical characteristics related to one of the issues of the model. The important issue which have not been clarified and will be discussed in this paper is what is the conditions in the wavelength range for the best von Kries model fitting? Because of the multiplication between illuminant spectrums and object spectral reflectances, the conditions in the wavelength range give various resultant tristimulus values though the von Kries model is a model with only three parameters. In the removal of colored illuminants, the model fitting has uncertainty. As the result of this paper, it is shown that the simplest von Kries model provides the best chromatic adaptation for sets of illuminant spectrum and object spectral reflectance which generate the centroid tristimulus values in the tristimulus region corresponding to the uncertainty.

Recovering fluorescent spectra with an RGB digital camera and color filters using different matrix factorizations

The aim of a multispectral system is to recover a spectral function at each image pixel, but when a scene is digitally imaged under a light of unknown spectral power distribution (SPD), the image pixels give incomplete information about the spectral reflectances of objects in the scene. We have analyzed how accurately the spectra of artificial fluorescent light sources can be recovered with a digital CCD camera. The red-green-blue (RGB) sensor outputs are modified by the use of successive cutoff color filters. Four algorithms for simplifying the spectra datasets are used: nonnegative matrix factorization (NMF), independent component analysis (ICA), a direct pseudoinverse method, and principal component analysis (PCA). The algorithms are tested using both simulated data and data from a real RGB digital camera. The methods are compared in terms of the minimum rank of factorization and the number of sensors required to derive acceptable spectral and colorimetric SPD estimations; the PCA results are also given for the sake of comparison. The results show that all the algorithms surpass the PCA when a reduced number of sensors is used. The experimental results suggest a significant loss of quality when more than one color filter is used, which agrees with the previous results for reflectances. Nevertheless, an RGB digital camera with or without a prefilter is found to provide good spectral and colorimetric recovery of indoor fluorescent lighting and can be used for color correction without the need of a telespectroradiometer.

Evaluation of a multispectral image acquisition system aimed at reconstruction of spectral reflectances

The reconstruction of spectral reflectances of objects being imaged is important in reproducing a color under a variety of viewing illuminants. In this work, a simple formula is derived to evaluate the quality of a set of color image sensors aimed at reconstruction of spectral reflectances, and it is applied to multispectral image acquisition systems. Since the quality depends not only on the spectral sensitivities but also on the noise present in the systems, it is impossible to evaluate a set of sensors without prior knowledge of the noise present in it. Therefore, the noise variance of the multispectral cameras is estimated by a new proposal, and it is applied to the evaluation for the first time. It is shown that the experimental results agree well with the predictions by the evaluation model, and that the method to estimate the noise variance is useful for the evaluation.

Non-Parametric Classification of Remotely Sensed Multispectral Image Data by Means of Matrix Representation of Multidimensional Histograms

The computer framing of land use maps using remotely sensed multispectral image data is identical with pattern classification for spectral reflectance of objects on earth's surface. In particular, the classification by the maximum likelihood method is the most popular method because it theoretically gives the highest correct classification rate on the condition that the statistical distribution of the image data be normal. However, the histogram of real image data is not a normal distribution. Actual histograms show the proper distributions to classes. This fact means that a histogram gives a spatial property of the class statistically. This paper described a newly developed non-parametric method by means of the matrix representations of multidimensional histograms and subimages.