University of Central Florida

About: University of Central Florida is a education organization based out in Orlando, Florida, United States. It is known for research contribution in the topics: Laser & Population. The organization has 18822 authors who have published 48679 publications receiving 1234422 citations. The organization is also known as: UCF.

Dispersion and band-gap scaling of the electronic Kerr effect in solids associated with two-photon absorption.

Abstract: Measurements of the nonlinear refractive index of several semiconductors using beam-distortion methods and four-wave mixing show a strong systematic dispersion in the bound-electronic nonlinearity (electronic Kerr effect ${\mathit{n}}_{2}$) near the two-photon-absorption edge. This eventually turns from positive to negative at higher frequencies. We find that by using the two-photon-absorption spectrum as predicted by a two-parabolic-band model, we can predict the observed universal dispersion, scaling, and values of ${\mathit{n}}_{2}$ that range over 4 orders of magnitude and change sign, using a simple Kramers-Kronig analysis (i.e., relating the real and imaginary parts of the third-order susceptibility). The resulting scaling rule correctly predicts the value of ${\mathit{n}}_{2}$ for all the 26 different materials we have examined. This includes wide-gap dielectrics which have 3 to 4 orders of magnitude smaller values of ${\mathit{n}}_{2}$ than semiconductors.

Enhanced-alignment Measure for Binary Foreground Map Evaluation

Abstract: The existing binary foreground map (FM) measures to address various types of errors in either pixel-wise or structural ways. These measures consider pixel-level match or image-level information independently, while cognitive vision studies have shown that human vision is highly sensitive to both global information and local details in scenes. In this paper, we take a detailed look at current binary FM evaluation measures and propose a novel and effective E-measure (Enhanced-alignment measure). Our measure combines local pixel values with the image-level mean value in one term, jointly capturing image-level statistics and local pixel matching information. We demonstrate the superiority of our measure over the available measures on 4 popular datasets via 5 meta-measures, including ranking models for applications, demoting generic, random Gaussian noise maps, ground-truth switch, as well as human judgments. We find large improvements in almost all the meta-measures. For instance, in terms of application ranking, we observe improvementrangingfrom9.08% to 19.65% compared with other popular measures.