The inverse problem of spectral reflection prediction: Problems of framework selection

Digital image processing requires substantial computations for characterization. It is since the reliable color reproduction can be achieved by establishing the correspondence between the spectral reflectance of the printed surface and the amounts of deposited inks. The processing is implemented by using different mathematical models. Most of the color prediction models engage some mathematical techniques to predict spectral reflectance for a mixture of colorants that are characterized by absorption and scattering during the light propagation. However, few attempts were made to make a model for prediction the colorants values based on an observing spectrum. This work is devoted to application of artificial neural network approach for solving the inverse problem of spectral reflection prediction. This task has been considered unsolvable as it involves solving a system of the linear differential equations, in which the number of unknowns exceeds the number of equations. Our attempt is based on the assumption that the prediction of the initial colorants from spectral data is possible by analogy with the work of the color perception system in humans. The aim of our study is to offer an approach to the framework selection. The model is built in Matlab and shows satisfactory prediction accuracy. © 2020 American Institute of Physics Inc.. All rights reserved

Study of Camera Spectral Reflectance Reconstruction Performance using CPU and GPU Artificial Neural Network Modelling

Reconstruction of reflectance spectra from camera RGB values is possible, if characteristics of the illumination source, optics and sensors are known. If not, additional information about these has to be somehow acquired. If alongside with pictures taken, RGB values of some colour patches with known reflectance spectra are obtained under the same illumination conditions, the reflectance reconstruction models can be created based on artificial neural networks (ANN). In Matlab, multilayer feedforward networks can be trained using different algorithms. In our study we hypothesized that the scaled conjugate gradient back propagation (BP) algorithm when executed on Graphics Processing Unit, is very fast, but in terms of convergence and performance, it does not match Levenberg-Marquardt algorithm (LM), which, on the other hand, executes only on CPU and is therefore much more time-consuming. We also presumed that there exists a correlation between the two algorithms and is manifested through a dependency of MSE to the number of hidden layer neurons, and therefore the faster BP algorithm could be used to narrow the search span with the LM algorithm to find the best ANN for reflectance reconstruction. The conducted experiment confirmed speed superiority of the BP algorithm but also confirmed better convergence and accuracy of reflectance reconstruction with the LM algorithm. The correlation of reflectance recovery results with ANNs modelled by both training algorithms was confirmed, and a strong correlation was found between the 3rd order polynomial approximation of the LM and BP algorithm\u27s test performances for both mean and best performance

Cuantificación de componentes biológicos en úlceras cutáneas a partir de imágenes multi-espectrales

Las úlceras cutáneas (UC) son una de las causas más frecuentes de consulta en las Unidades de atención de salud (PHU) en áreas tropicales. Sin embargo, la falta de médicos especializados en esas áreas, conduce a un diagnóstico y manejo inapropiados de los pacientes. Existe entonces la necesidad de desarrollar herramientas que permitan guiar a los médicos hacia un diagnóstico preciso. Los sistemas de imágenes multi-espectrales son una herramienta no invasiva que podrían utilizarse en el análisis de las úlceras cutáneas. Con estos sistemas es posible adquirir imágenes ópticas a diferentes longitudes de onda, que pueden ser procesadas por medio de modelos matemáticos basados en enfoques de optimización. El procesamiento de ese tipo de imágenes conduce a la cuantificación de los principales componentes de la piel. En el caso de las úlceras cutáneas, estos componentes podrían correlacionarse con las diferentes etapas de cicatrización de heridas durante el seguimiento de una úlcera cutánea. Este trabajo de grado presenta el procesamiento de una imagen multi-espectral de úlcera cutánea. La úlcera empleada en este trabajo corresponde a Leishmaniasis, una de las enfermedades más prominentes en las zonas tropicales. El procesamiento de imágenes se realiza mediante un modelo de interacción luz-tejido considerando la distribución de la piel como una capa semi-infinita. La optimización de los parámetros del modelo permite cuantificar los parámetros principales de absorción y dispersión de la luz en la piel en el espectro visible y cercano infrarrojo. Los resultados muestran diferencias entre áreas sanas y no sanas de la imagen.Ingeniero de Sistemaspregrad