A colorimeter comprises a colorimetric value obtaining means to obtain a colorimetric value by performing color measurement on a measuring sample; a reference value obtaining means to obtain a reference value; a computing means to, using a color difference formula, ΔE*.sub.94, compute ΔL*.sub.94, Δa*.sub.94, and Δb*.sub.94 with reference to the colorimetric value obtained by the colorimetric value obtaining portion and the reference value obtained by the reference value obtaining portion, the ΔL*.sub.94, Δa*.sub.94, and Δb*.sub.94 having a relation of

ΔE*.sub.94=[(ΔL*.sub.94).sup.2+(Δa*.sub.94).sup.2*(Δb*.sub.94).sup.2].sup.1/2

where the ΔL*.sub.94 corresponds to a difference in lightness, the Δa*.sub.94 corresponds to a difference in red and green, and the Δb*.sub.94 corresponds to a difference in blue and yellow; and a display means to display computational results obtained by the computing means.

Apparatuses and methods for approximating a 5-angle color difference model are provided, where the 5-angle color difference model utilizes a 5-angle equation. In an exemplary embodiment, an apparatus includes a storage device for storing instructions and one or more processors configured to execute the instructions. The processor(s) are configured to receive 3-angle standard and test color measurements, and enter the 3-angle standard measurement into a neural network empirical model. The neural network empirical model includes a plurality of input nodes, a plurality of hidden nodes connected to the input nodes, and a plurality of output nodes connected to the hidden nodes. The neural network empirical model is configured to output 3-angle tolerance values, and to calculate a 3-angle color difference value using the 5-angle equation for at least one of the 3 color measurement angles using the 3-angle standard and test color measurements and the 3-angle tolerance values.

An image-sensing device is disclosed, the image-sensing device comprising a multispectral sensor and a processor communicably coupled to the multispectral sensor. The processor is configured to determine an ambient light source classification based on a comparison of predefined spectral data to data corresponding to an output of the multispectral sensor. Also disclosed is a method of classifying an ambient light source by sensing a spectrum of light with a multispectral sensor; and determining an ambient light source classification based on a comparison of predefined spectral data to data corresponding to an output of the multispectral sensor. An associated computer program, computer-readable medium and data processing apparatus are also disclosed.

Methods and systems for determining a radar compatible coating are provided. In one example, the method includes obtaining a reflectance measurement of a target coating to characterize a color of the target coating. One or more candidate formulas are generated to determine color matching to the color of the target coating. A corresponding color and a corresponding radar property for each of the one or more candidate formulations is predicted. A radar compatible coating composition that is the same or substantially similar in appearance to the target coating is generated. Generating the radar compatible coating composition is based at least in part on the corresponding color and the corresponding radar property for one of the one or more candidate formulations.

A method of determining a color formula for a target color begins with generating a plurality of candidate color formulas to reproduce the target color. For each candidate formula, a predicted delta E indicating a difference from a predicted color for the candidate formula from the target color is determined. Also for each candidate formula, a confidence value in the predicted delta E is generated by summing weighted figures of merit for each colorant in the candidate formula. The weighting of the figures of merit represents each colorant's proportion in the candidate formula. A formula is then selected based on the predicted delta E and confidence value. The target color may be defined in terms of a multi-dimensional color space.

First relational equations which represent characteristics of respective sample colors are obtained, and for the respective sample colors, prediction values of spectral characteristics of characteristics-acquired gradation values for a prediction target color are obtained using the first relational equations. Difference values between the prediction values and actual measurement values are obtained, and a sample color for which a minimum difference value is obtained is selected as a reference color. A second relational equation that represents characteristics of the reference color is obtained, and a prediction value of spectral characteristics of a prediction target gradation value for the prediction target color is obtained using the second relational equation.

A method of digital measuring the color of fabrics based on digital camera, includes: making plain fabric samples; obtaining ground-truth color of plain fabrics using a spectrophotometer; capturing a raw format digital image of the plain fabrics using the digital camera and extracting raw camera responses of the plain fabrics; capturing a raw format digital image of a target fabric and extracting the raw camera responses of a ROI in the target fabric; calculating a Euclidean distance and a similarity coefficient between the raw camera responses of the ROI in the target fabric and the plain fabrics; normalizing the Euclidean distance and the similarity coefficient; calculating a weighting coefficient of each color data of the plain fabrics based on the normalized Euclidean distance and similarity coefficient; weighting every color data of plain fabrics with a corresponding weighting coefficient; and summing the weighted color data of the plain fabrics.

In one embodiment, a method is disclosed that includes selecting a first color sample within a target area in a first image of a first object displayed by a display device; selecting a second color sample within a target area in a second image of a second object displayed in the display device; comparing the first color sample against the second color sample to determine a measure of color difference or a measure of color equivalence between the first color sample of the first object and the second color sample of the second object; and displaying the results of the comparison to a user in the display device. One or more of these functions may be performed with a processor.

An embodiment a method for measuring an amount of manganese in an aqueous sample, including: reducing, using a dechlorination reagent, wherein the dechlorination reagent comprises iron(II) and potassium iodide; oxidizing, under an alkaline condition using sodium hydroxide, Mn(II) to Mn(IV) in the aqueous sample, and chelating, using etidronic acid (HEDP), Fe(II) and Fe(III) in the aqueous sample, oxidizing an amount of 3,3′,5,5′-tetramethylbenzidine (TMB) with Mn(IV); and measuring, using a colorimetric indicator, the amount of manganese within the aqueous sample, by measuring an absorbance intensity at a wavelength of the oxidized amount of 3,3′,5,5′-tetramethylbenzidine (TMB). Other aspects are described and claimed.

Disclosed herein is a method and a device that can measure an unknown target coating; can search, based on the measured data of the target coating, a database for one or more best matching coating formulas, i.e. one or more preliminary matching formulas, within the database; and that can refine the search using an image similarity metric between images of the one or more best matching coating formulas on the one side and images of the target coating on the other side, using deep learning techniques.