Mehdi Rezagholizadeh

Edge-Based and Efficient Chromaticity Spatio-spectral Models for Color Constancy

Fast and accurate estimation of the transformation imposed by the illuminant to the colors of an image taken under that illuminant is of crucial importance in real-time computational color constancy applications. To this end, we present an edge based and an efficient chromaticity spatiospectral model which are modified versions of the spatiospectral method introduced by Chakrabarti et al [1]. As compared with the conventional color constancy methods, the spatio-spectral model improves the accuracy of estimation at the cost of increasing the execution time and storage dramatically. This increase makes the spatio-spectral model impractical and inappropriate for real-time applications. Our proposed methods aim at reducing the computational burden and required storage for the spatio-spectral modeling while retaining its accuracy of estimation. Evaluation of the performance of the proposed methods on a synthetic color image database and also the “Color Checker” database [2] are presented.

Photon Detection and Color Perception at Low Light Levels

Working under low light conditions is of particular interest in machine vision applications such as night vision, tone-mapping techniques, low-light imaging, photography, and surveillance cameras. This work aims at investigating the perception of color at low light situations imposed by physical principles governing photon emission. The impact of the probabilistic nature of photon emission on our color perception becomes more significant at low light levels. In this regard, physical principles are leveraged to develop a framework to take into account the effects of low light level on color vision. Results of this study shows that the normalized spectral power distribution of light changes with light intensity and becomes more uncertain at low light situation as a result of which the uncertainty of color perception increases. Furthermore, a color patch at low light levels give rise to uncertain color measurements whose chromaticities form an elliptic shape inside the chromaticity diagram around the high intensity chromaticity of the color patch. The size of these ellipses is a function of the light intensity and the chromaticity of color patches however the orientation of the ellipses depends only on the patch chromaticity and not on the light level. Moreover, the results of this work indicate that the spectral composition of light is a determining factor in the size and orientation of the ellipses. The elliptic shape of measured samples is a result of the Poisson distribution governing photon emission together with the form of human cone spectral sensitivity functions and can partly explain the elliptic shape of MacAdam ellipses.

Image Sensor Modeling: Noise and Linear Transformation Impacts on the Color Gamut

Linear transformations are widely used in the color science. Linear transformation can not ideally map the source and destination color matching functions and this issue induces some errors in the process of conversion. This error is usually deemed negligible for a noise-free system. However, in practice, imaging devices, displays, and printers employ linear transformations to move between color spaces and at the same time they are subject to noise which might magnify the linear transformation errors. The induced error by this phenomenon can bring about colorcasts and hampering the image quality. In this study, the effects of noise and linear transformation on the colorgamut are investigated. In this regard, a typical image sensor is modelled and employed for this study. A detailed model of noise is considered in the process of implementing the image sensor model to guarantee the precision of the results. Several experiments have been performed over the implemented framework and the results show that the imperfections of linear transformation combined with the image sensor noise shrinks the gamut area of output images.

A probabilistic partial order theory approach to IT infrastructure selection for Smart Grid

In this research, a multi criteria decision making problem within the Smart Grid has been solved using a modified application of partial order theory (POT), which is an analytical way of dealing with decision making problems. The proposed approach incorporates data of decision matrices as its input. Consequently, by using Monte Carlo simulation, the Ranking Probability Matrix (RPM) is produced which leads us to a total order ranking of the MCDM alternatives. The aim of this paper is to resolve some disadvantages of several conventional methods of determining the best alternative. Thus, the deficiencies of older methods are discussed and their incapability in handling large numbers of alternatives and attributes are resolved. In addition, the uncertainty factor is going to play its role in decision making. We have illustrated the efficiency of our method by implementing it to an IT infrastructure selection problem in Smart Grid.