Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species

Burkholderia (B.) mallei, the causative agent of glanders, and B. pseudomallei, the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in human and animals make the differentiation from each other and other Burkholderia species challenging. The increased resistance against many antibiotics implies the need for fast and robust identification methods. The use of Raman microspectroscopy in microbial diagnostic has the potential for rapid and reliable identification. Single bacterial cells are directly probed and a broad range of phenotypic information is recorded, which is subsequently analyzed by machine learning methods. Burkholderia were handled under biosafety level 1 (BSL 1) conditions after heat inactivation. The clusters of the spectral phenotypes and the diagnostic relevance of the Burkholderia spp. were considered for an advanced hierarchical machine learning approach. The strain panel for training involved 12 B. mallei, 13 B. pseudomallei and 11 other Burkholderia spp. type strains. The combination of top- and sub-level classifier identified the mallei-complex with high sensitivities (>95%). The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay

An Empirical Approach to Cosmological Galaxy Survey Simulation: Application to SPHEREx Low-Resolution Spectroscopy

Highly accurate models of the galaxy population over cosmological volumes are necessary in order to predict the performance of upcoming cosmological missions. We present a data-driven model of the galaxy population constrained by deep 0.1-8 $\rm \mu m$ imaging and spectroscopic data in the COSMOS survey, with the immediate goal of simulating the spectroscopic redshift performance of the proposed SPHEREx mission. SPHEREx will obtain over the full-sky $R\sim41$ spectrophotometry at moderate spatial resolution ($\sim6"$) over the wavelength range 0.75-4.18 $\rm \mu m$ and $R\sim135$ over the wavelength range 4.18-5 $\rm \mu m$. We show that our simulation accurately reproduces a range of known galaxy properties, encapsulating the full complexity of the galaxy population and enables realistic, full end-to-end simulations to predict mission performance. Finally, we discuss potential applications of the simulation framework to future cosmology missions and give a description of released data products

N-colour separation methods for accurate reproduction of spot colours

In packaging, spot colours are used to print key information like brand logos and elements for which the colour accuracy is critical. The present study investigates methods to aid the accurate reproduction of these spot colours with the n-colour printing process. Typical n-colour printing systems consist of supplementary inks in addition to the usual CMYK inks. Adding these inks to the traditional CMYK set increases the attainable colour gamut, but the added complexity creates several challenges in generating suitable colour separations for rendering colour images. In this project, the n-colour separation is achieved by the use of additional sectors for intermediate inks. Each sector contains four inks with the achromatic ink (black) common to all sectors. This allows the extension of the principles of the CMYK printing process to these additional sectors. The methods developed in this study can be generalised to any number of inks. The project explores various aspects of the n-colour printing process including the forward characterisation methods, gamut prediction of the n-colour process and the inverse characterisation to calculate the n-colour separation for target spot colours. The scope of the study covers different printing technologies including lithographic offset, flexographic, thermal sublimation and inkjet printing. A new method is proposed to characterise the printing devices. This method, the spot colour overprint (SCOP) model, was evaluated for the n-colour printing process with different printing technologies. In addition, a set of real-world spot colours were converted to n-colour separations and printed with the 7-colour printing process to evaluate against the original spot colours. The results show that the proposed methods can be effectively used to replace the spot coloured inks with the n-colour printing process. This can save significant material, time and costs in the packaging industry

Toward data science in biophotonics: biomedical investigations-based study

Biophotonics aims to grasp and investigate the characteristics of biological samples based on their interaction with incident light. Over the past decades, numerous biophotonic technologies have been developed delivering various sorts of biological and chemical information from the studied samples. Such information is usually contained in high dimensional data that need to be translated into high-level information like disease biomarkers. This data translation is not straightforward, but it can be achieved using the advances in computer and data science. The scientific contributions presented in this thesis were established to cover two main aspects of data science in biophotonics: the design of experiments and the data-driven modeling and validation. For the design of experiment, the scientific contributions focus on estimating the sample size required for group differentiation and on evaluating the influence of experimental factors on unbalanced multifactorial designs. Both methods were designed for multivariate data and were checked on Raman spectral datasets. Thereafter, the automatic detection and identification of three diagnostic tasks were checked based on combining several image processing techniques with machine learning (ML) algorithms. In the first task, an improved ML pipeline to predict the antibiotic susceptibilities of E. coli bacteria was presented and evaluated based on bright-field microscopic images. Then, transfer learning-based classification of bladder cancer was demonstrated using blue light cystoscopic images. Finally, different ML techniques and validation strategies were combined to perform the automatic detection of breast cancer based on a small-sized dataset of nonlinear multimodal images. The obtained results exhibited the benefits of data science tools in improving the experimantal planning and the translation of biophotonic-associated data into high-level information for various biophotonic technologies

Counts and Colors of Faint Galaxies in the U and R Bands

Ground-based counts and colors of faint galaxies in the U and R bands in one field at high Galactic latitude are presented. Integrated over flux, a total of 1.2x10^5 sources per square degree are found to U=25.5 mag and 6.3x10^5 sources per square degree to R=27 mag, with d log N/dm ~ 0.5 in the U band and d log N/dm ~ 0.3 in the R band. Consistent with these number-magnitude curves, sources become bluer with increasing magnitude to median U-R=0.6 mag at 24<U<25 mag and U-R=1.2 mag at 25 < R < 26 mag. Because the Lyman break redshifts into the U band at z~3, at least 1.2x10^5 sources per square degree must be at redshifts z<3. Measurable U-band fluxes of 73 percent of the 6.3x10^5 sources per square degree suggest that the majority of these also lie at z < 3. These results require an enormous space density of objects in any cosmological model.Comment: 17 pages, MNRAS in pres

The European Large Area ISO Survey II: mid-infrared extragalactic source counts

We present preliminary source counts at 6.7um and 15um from the Preliminary Analysis of the European Large Area ISO survey, with limiting flux densities of \~2mJy at 15um & ~1mJy at 6.7um. We separate the stellar contribution from the extragalactic using identifications with APM sources made with the likelihood ratio technique. We quantify the completeness & reliability of our source extraction using (a) repeated observations over small areas, (b) cross-IDs with stars of known spectral type, (c) detections of the PSF wings around bright sources, (d) comparison with independent algorithms. Flux calibration at 15um was performed using stellar IDs; the calibration does not agree with the pre-flight estimates, probably due to effects of detector hysteresis and photometric aperture correction. The 6.7um extragalactic counts are broadly reproduced in the Pearson & Rowan-Robinson model, but the Franceschini et al. (1997) model underpredicts the observed source density by ~0.5-1 dex, though the photometry at 6.7um is still preliminary. At 15um the extragalactic counts are in excellent agreement with the predictions of the Pearson & Rowan-Robinson (1996), Franceschini et al. (1994), Guiderdoni et al. (1997) and the evolving models of Xu et al. (1998), over 7 orders of magnitude in 15um flux density. The counts agree with other estimates from the ISOCAM instrument at overlapping flux densities (Elbaz et al. 1999), provided a consistent flux calibration is used. Luminosity evolution at a rate of (1+z)^3, incorporating mid-IR spectral features, provides a better fit to the 15um differential counts than (1+z)^4 density evolution. No-evolution models are excluded, and implying that below around 10mJy at 15um the source counts become dominated by an evolving cosmological population of dust-shrouded starbursts and/or active galaxies.Comment: MNRAS in press. 14 pages, uses BoxedEPS (included). For more information on the ELAIS project see http://athena.ph.ic.ac.uk