A hyperspectral imaging microscope system (HIMS) was recently designed for color truth establishment of histological tissue slides used for whole slide imaging (WSI) systems color performances assessment. Here, we present the estimation procedure of the colorimetrical performance of the HIMS by measuring the transmittance spectra of spatially uniform neutral density and color filters and deriving the color coordinates in the CIELAB color space. The transmittance and CIELAB results are compared to reference transmittance spectra and subsequent CIELAB coordinates provided by measurement of the same region of interest with a spectroradiometer. To measure the same region of interest, the spectroradiometer is equipped with a fiber probe whose tip is set in one of the eyepiece tubes of the microscope. The CIE 1976 color difference, ΔE*ab, is the metric used for goodness estimation.
There are not many international courses that teach color science applied to non-technical fields. Here, we want to present the experience from a master course organized by Gruppo del Colore – Associazione Italiana Colore and Politecnico of Milano: the Master in Color Design & Technology. It has the aim of training students in the use of color mainly for design purposes, and color science and colorimetry have a big role in the program. The Master is organized in three main phases: Fundamentals, Project Works and Internship. The first part is a series of lectures and frontal lessons that gives to the students the theorical and technical bases to be used in project works practical applications of and internship. In fact, in the second and third parts students are asked to design and develop different applicative design projects. After the first part of Fundamentals training, students will be able to manage disciplines such as colorimetry, visual perception, physics, chemistry, optics and psychology, to build up their future professional career in different fields.
Estimating skin color from an uncontrolled facial image is a challenging task. Many factors such as illumination, camera and shading variations directly affect the appearance of skin color in the image. Furthermore, using a color calibration target in order to correct the image pixels leads to a complex user experience. We propose a skin color estimation method from images in the wild, taken with unknown camera, under an unknown lighting, and without a calibration target. While prior methods relied on explicit intermediate steps of color correction of image pixels and skin region segmentation, we propose an end-to-end color regression model named LabNet, in which color correction and skin region segmentation are implicitly learnt by the model. Our method is based on a convolutional neural network trained on a dataset of smartphone images, labeled with L*a*b* measures of skin colors. We compare our method with standard skin color estimation approaches and found that our method over-perform these models while removing the need of color calibration target.
The accurate measurement of reflectance and transmittance properties of materials is essential in the printing and display industries in order to ensure precise color reproduction. In comparison with reflectance measurement, where the impact of different geometries (0°/45°, d/8°) has been thoroughly investigated, there are few published articles related to transmittance measurement. In this work, we explore different measurement geometries for total transmittance, and show that the transmittance measurements are highly affected by the geometry used, since certain geometries can introduce a measurement bias. We present a flexible custom setup that can simulate these geometries, which we evaluate both qualitatively and quantitatively over a set of samples with varied optical properties. We also compare our measurements against those of widely used commercial solutions, and show that significant differences exist over our test set. However, when the bias is correctly compensated, very low differences are observed. These findings therefore stress the importance of including the measurement geometry when reporting total transmittance.