Background free imaging of upconversion nanoparticle distribution in human skin

Widespread applications of nanotechnology materials have raised safety concerns due to their possible penetration through skin and concomitant uptake in the organism. This calls for systematic study of nanoparticle transport kinetics in skin, where high-resolution optical imaging approaches are often preferred. We report on application of emerging luminescence nanomaterial, called upconversion nanoparticles (UCNPs), to optical imaging in skin that results in complete suppression of background due to the excitation light back-scattering and biological tissue autofluorescence. Freshly excised intact and microneedle-treated human skin samples were topically coated with oil formulation of UCNPs and optically imaged. In the first case, 8- and 32-nm UCNPs stayed at the topmost layer of the intact skin, stratum corneum. In the second case, 8-nm nanoparticles were found localized at indentations made by the microneedle spreading in dermis very slowly (estimated diffusion coefficient, D-np = 3-7 x 10(-12) cm(2) . s(-1)). The maximum possible UCNP-imaging contrast was attained by suppressing the background level to that of the electronic noise, which was estimated to be superior in comparison with the existing optical labels. (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE)

Cross-Spectral Full and Partial Face Recognition: Preprocessing, Feature Extraction and Matching

Cross-spectral face recognition remains a challenge in the area of biometrics. The problem arises from some real-world application scenarios such as surveillance at night time or in harsh environments, where traditional face recognition techniques are not suitable or limited due to usage of imagery obtained in the visible light spectrum. This motivates the study conducted in the dissertation which focuses on matching infrared facial images against visible light images. The study outspreads from aspects of face recognition such as preprocessing to feature extraction and to matching.;We address the problem of cross-spectral face recognition by proposing several new operators and algorithms based on advanced concepts such as composite operators, multi-level data fusion, image quality parity, and levels of measurement. To be specific, we experiment and fuse several popular individual operators to construct a higher-performed compound operator named GWLH which exhibits complementary advantages of involved individual operators. We also combine a Gaussian function with LBP, generalized LBP, WLD and/or HOG and modify them into multi-lobe operators with smoothed neighborhood to have a new type of operators named Composite Multi-Lobe Descriptors. We further design a novel operator termed Gabor Multi-Levels of Measurement based on the theory of levels of measurements, which benefits from taking into consideration the complementary edge and feature information at different levels of measurements.;The issue of image quality disparity is also studied in the dissertation due to its common occurrence in cross-spectral face recognition tasks. By bringing the quality of heterogeneous imagery closer to each other, we successfully achieve an improvement in the recognition performance. We further study the problem of cross-spectral recognition using partial face since it is also a common problem in practical usage. We begin with matching heterogeneous periocular regions and generalize the topic by considering all three facial regions defined in both a characteristic way and a mixture way.;In the experiments we employ datasets which include all the sub-bands within the infrared spectrum: near-infrared, short-wave infrared, mid-wave infrared, and long-wave infrared. Different standoff distances varying from short to intermediate and long are considered too. Our methods are compared with other popular or state-of-the-art methods and are proven to be advantageous

Red Supergiants in the Andromeda Galaxy (M31)

Red supergiants are a short-lived stage in the evolution of moderately massive stars (10-25Mo), and as such their location in the H-R diagram provides an exacting test of stellar evolutionary models. Since massive star evolution is strongly affected by the amount of mass-loss a star suffers, and since the mass-loss rates depend upon metallicity, it is highly desirable to study the physical properties of these stars in galaxies of various metallicities. Here we identify a sample of red supergiants in M31 (the most metal-rich of the Local Group galaxies) and derive their physical properties by fitting MARCS atmosphere models to moderate resolution optical spectroscopy, and from V-K photometry.Comment: Accepted for publication in the Astrophysical Journa

Fundamental Studies into the Chemical and Physical Properties of Latent Fingermarks

This thesis describes physical and chemical investigations performed on latent fingermarks deposited on non-porous surfaces in order to provide the necessary fundamental underpinnings for future fingermark research. Variation of the physical properties over time was investigated using a novel imaging mode of atomic force microscopy. Spatial distribution of fingermark components was investigated using high-resolution vibrational microspectroscopy techniques. Time-course transformation of squalene in fingermarks stored under different storage conditions was examined using liquid chromatography-high-resolution mass spectrometry

Facial Makeup Detection Using HSV Color Space and Texture Analysis

Facial Makeup Detection Using HSV Color Space and Texture Analysis In recent decades, 2D and 3D face analyses in digital systems have become increasingly important because of their vast applications in security systems or any digital systems that interact with humans. In fact the human face expresses many of the individual’s characteristics such as gender, ethnicity, emotion, age, beauty and health. Makeup is one of the common techniques used by people to alter the appearance of their faces. Analyzing face beauty by computer is essential to aestheticians and computer scientists. The objective of this research is to detect makeup on images of human faces by image processing and pattern recognition techniques. Detecting changes of face, caused by cosmetics such as eye-shadow, lipstick and liquid foundation, are the targets of this study. Having a proper facial database that consists of the information related to makeup is necessary. Collecting the first facial makeup database was a valuable achievement for this research. This database consists of almost 1290 frontal pictures from 21 individuals before and after makeup. Along with the images, meta data such as ethnicity, country of origin, smoking habits, drinking habits, age, and job is provided. The uniqueness of this database stems from, first being the only database that has images of women both before and after makeup, and second because of having light-source from different angles as well as its meta data collected during the process. Selecting the best features that lead to the best classification result is a challenging issue, since any variation in the head pose, lighting conditions and face orientation can add complexity to a proper evaluation of whether any makeup has been applied or not. In addition, the similarity of cosmetic’s color to the skin color adds another level of difficulty. In this effort, by choosing the best possible features, related to edge information, color specification and texture characteristics this problem was addressed. Because hue and saturation and intensity can be studied separately in HSV (Hue, Saturation, and Value) color space, it is selected for this application. The proposed technique is tested on 120 selected images from our new database. A supervised learning model called SVM (Support Vector Machine) classifier is used and the accuracy obtained is 90.62% for eye-shadow detection, 93.33% for lip-stick and 52.5% for liquid foundation detection respectively. A main highlight of this technique is to specify where makeup has been applied on the face, which can be used to identify the proper makeup style for the individual. This application will be a great improvement in the aesthetic field, through which aestheticians can facilitate their work by identifying the type of makeup appropriate for each person and giving the proper suggestions to the person involved by reducing the number of trials

Correlation between Porcine and Human Skin Models by Optical Methods

Background: Topical photodynamic therapy (PDT) using 5-aminolevulinic acid (ALA) and methyl aminolevulinate (MAL) as precursors of protoporphyrin IX (PPIX) have been used in skin cancer treatment and other skin diseases. To establish new topical PDT, protocols are necessary first to conduct studies in vivo using animal skin models. The goal of this study is to evaluate the robust correlation between porcine and human skin models in vivo by optical methods to confirm the suitability of porcine skin models to predict drug behavior in the human skin on topical PDT protocols. Methods: The study was performed in vivo using porcine and human skin models. In human skin, ALA and MAL cream mixture samples were applied to the inner arm in a circular area of 1 cm2. In porcine skin, the cream was applied on the back in an area of 4 cm2, over which an occlusive dressing was placed. PPIX production was monitored for up to 5 h using widefield fluorescence imaging and fluorescence spectroscopy techniques. Results: Human skin models showed similar behavior to porcine skin models, which indicates high similarity between both models and confirms that porcine skin is an adequate model to establish new clinical PDT protocols in human volunteers

Determination of the optimal pre-processing technique for spectral data of oil palm leaves with respect to nutrient

Precision agriculture with regard to crop science was introduced to apply only the required and optimal amount of fertiliser, which inspired the present study of nutrient prediction for oil palm using spectroradiometer with wavelengths ranging from 350 to 2500 nm. Partial least square (PLS) method was used to develop a statistical model to interpret spectral data for nutrient deficiency of nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca) and boron (B) of oil palm. Prior to the development of the PLS model, pre-processing was conducted to ensure only the smooth and best signals were studied, which includes the multiplicative scatter correction (MSC), first and second derivatives and standard normal variate (SNV), Gaussian filter and Savitzky-Golay smoothing. The MSC technique was the optimal overall pre-treatment method for nutrients in this study, with highest prediction R2 of 0.91 for N and lowest RMSEP value of 0.00 for P