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We study the relationship between reflectance and the degree of linear polarization of radiation that bounces off the surface of an unvarnished oil painting. We design a VNIR-SWIR (400 nm to 2500 nm) polarimetric reflectance imaging spectroscopy setup that deploys unpolarized light and allows us to estimate the Stokes vector at the pixel level. We observe a strong negative correlation between the S0 component of the Stokes vector (which can be used to represent the reflectance) and the degree of linear polarization in the visible interval (average -0.81), while the correlation is weaker and varying in the infrared range (average -0.50 in the NIR range between 780 and 1500 nm, and average -0.87 in the SWIR range between 1500 and 2500 nm). By tackling the problem with multi-resolution image analysis, we observe a dependence of the correlation on the local complexity of the surface. Indeed, we observe a general trend that strengthens the negative correlation for the effect of artificial flattening provoked by low image resolutions. [ABSTRACT FROM AUTHOR]

The main objective of this work was to optimize the spray drying of coconut milk using response surface methodology (RSM). Maltodextrin was added to the feed coconut milk as the carrier agent in the laboratory scale experiments and the same were designed using Box-Behnken design approach. The effect of the chosen input variables on the responses namely product yield, moisture content, hygroscopicity, and angle of repose of the powders were evaluated using 3D surface plots. Among the chosen input variables of the process, the temperature of the inlet air in the spray drying chamber was found to significantly affect the characteristics of the powder. The flowability properties of the synthesized powders were experimentally obtained and the values were found to be satisfactory. The functional and morphological properties, and color of the powder produced at the optimal condition were compared to those of a commercially available coconut milk powder. For the synthesized powder of the optimal condition, an enhancement of 30oC for the glass transition temperature was seen over to that of the commercial powder. [ABSTRACT FROM AUTHOR]

The work presented in this paper is part of a wider research project, which aims at documenting and analyzing stained glass windows by means of hyperspectral imaging. This technique shares some similarities with UV-VIS-IR spectroscopy, as they both provide spectral information; however, spectral imaging has the additional advantage of providing spatial information, since a spectrum can be collected in each pixel of the image. Compared to UV-VIS-IR spectroscopy, spectral imaging has rarely been used for the investigation of stained glass windows. One of the objectives of this paper is, thus, to compare the performance of these two instruments to validate the results of hyperspectral imaging. The second objective is to evaluate the potential of analyzing colored-glass pieces in reflectance modality and compare the results with those obtained in transmittance, in order to highlight the differences and similarities between the two approaches. The geometry of the systems and the backing material for the glass, as well as the characteristics of the glass pieces, are discussed. L*a*b* values obtained from the spectra, as well as the calculated color difference ΔE00, are provided, to show the degree of agreement between the instruments and the two measurement modalities. [ABSTRACT FROM AUTHOR]

The massive generation of data, which includes images and videos, has made data management, analysis, information extraction difficult in recent years. To gather relevant information, this large amount of data needs to be grouped. Real-life data may be noise corrupted during data collection or transmission, and the majority of them are unlabeled, allowing for the use of robust unsupervised clustering techniques. Traditional clustering techniques, which vectorize the images, are unable to keep the geometrical structure of the images. Hence, a robust tensor-based submodule clustering method based on l 1 2 regularization with improved clustering capability is formulated. The l 1 2 induced tensor nuclear norm (TNN), integrated into the proposed method, offers better low rankness while retaining the self-expressiveness property of submodules. Unlike existing methods, the proposed method employs a simultaneous noise removal technique by twisting the lateral image slices of the input data tensor into frontal slices and eliminates the noise content in each image, using the principles of the sparse and low rank decomposition technique. Experiments are carried out over three datasets with varying amounts of sparse, Gaussian and salt and pepper noise. The experimental results demonstrate the superior performance of the proposed method over the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The massive generation of data, which includes images and videos, has made data management, analysis, information extraction difficult in recent years. To gather relevant information, this large amount of data needs to be grouped. Real-life data may be noise corrupted during data collection or transmission, and the majority of them are unlabeled, allowing for the use of robust unsupervised clustering techniques. Traditional clustering techniques, which vectorize the images, are unable to keep the geometrical structure of the images. Hence, a robust tensor-based submodule clustering method based on l12 regularization with improved clustering capability is formulated. The l12 induced tensor nuclear norm (TNN), integrated into the proposed method, offers better low rankness while retaining the self-expressiveness property of submodules. Unlike existing methods, the proposed method employs a simultaneous noise removal technique by twisting the lateral image slices of the input data tensor into frontal slices and eliminates the noise content in each image, using the principles of the sparse and low rank decomposition technique. Experiments are carried out over three datasets with varying amounts of sparse, Gaussian and salt and pepper noise. The experimental results demonstrate the superior performance of the proposed method over the existing state-of-the-art methods.