Colour analysis of degraded parchment

Multispectral imaging was employed to collect data on the degradation of an 18th century parchment by a series of physical and chemical treatments. Each sample was photographed before and after treatment by a monochrome digital camera with 21 narrow-band filters. A template-matching technique was used to detect the circular holes in each sample and a four-point projective transform to register the 21 images. Colour accuracy was verified by comparison of reconstructed spectra with measurements by spectrophotometer

Progressive Refinement Imaging

This paper presents a novel technique for progressive online integration of uncalibrated image sequences with substantial geometric and/or photometric discrepancies into a single, geometrically and photometrically consistent image. Our approach can handle large sets of images, acquired from a nearly planar or infinitely distant scene at different resolutions in object domain and under variable local or global illumination conditions. It allows for efficient user guidance as its progressive nature provides a valid and consistent reconstruction at any moment during the online refinement process. // Our approach avoids global optimization techniques, as commonly used in the field of image refinement, and progressively incorporates new imagery into a dynamically extendable and memory‐efficient Laplacian pyramid. Our image registration process includes a coarse homography and a local refinement stage using optical flow. Photometric consistency is achieved by retaining the photometric intensities given in a reference image, while it is being refined. Globally blurred imagery and local geometric inconsistencies due to, e.g. motion are detected and removed prior to image fusion. // We demonstrate the quality and robustness of our approach using several image and video sequences, including handheld acquisition with mobile phones and zooming sequences with consumer cameras

High-Dynamic-Range Lighting Estimation From Face Portraits.

We present a CNN-based method for outdoor highdynamic-range (HDR) environment map prediction from low-dynamic-range (LDR) portrait images. Our method relies on two different CNN architectures, one for light encoding and another for face-to-light prediction. Outdoor lighting is characterised by an extremely high dynamic range, and thus our encoding splits the environment map data between low and high-intensity components, and encodes them using tailored representations. The combination of both network architectures constitutes an end-to-end method for accurate HDR light prediction from faces at real-time rates, inaccessible for previous methods which focused on low dynamic range lighting or relied on non-linear optimisation schemes. We train our networks using both real and synthetic images, we compare our light encoding with other methods for light representation, and we analyse our results for light prediction on real images. We show that our predicted HDR environment maps can be used as accurate illumination sources for scene renderings, with potential applications in 3D object insertion for augmented reality

Neural BRDF Representation and Importance Sampling

Controlled capture of real-world material appearance yields tabulated sets of highly realistic reflectance data. In practice, however, its high memory footprint requires compressing into a representation that can be used efficiently in rendering while remaining faithful to the original. Previous works in appearance encoding often prioritized one of these requirements at the expense of the other, by either applying high-fidelity array compression strategies not suited for efficient queries during rendering, or by fitting a compact analytic model that lacks expressiveness. We present a compact neural network-based representation of BRDF data that combines high-accuracy reconstruction with efficient practical rendering via built-in interpolation of reflectance. We encode BRDFs as lightweight networks, and propose a training scheme with adaptive angular sampling, critical for the accurate reconstruction of specular highlights. Additionally, we propose a novel approach to make our representation amenable to importance sampling: rather than inverting the trained networks, we learn to encode them in a more compact embedding that can be mapped to parameters of an analytic BRDF for which importance sampling is known. We evaluate encoding results on isotropic and anisotropic BRDFs from multiple real-world datasets, and importance sampling performance for isotropic BRDFs mapped to two different analytic models

Comprehensive Use of Curvature for Robust and Accurate Online Surface Reconstruction

Interactive real-time scene acquisition from hand-held depth cameras has recently developed much momentum, enabling applications in ad-hoc object acquisition, augmented reality and other fields. A key challenge to online reconstruction remains error accumulation in the reconstructed camera trajectory, due to drift-inducing instabilities in the range scan alignments of the underlying iterative-closest-point (ICP) algorithm. Various strategies have been proposed to mitigate that drift, including SIFT-based pre-alignment, color-based weighting of ICP pairs, stronger weighting of edge features, and so on. In our work, we focus on surface curvature as a feature that is detectable on range scans alone and hence does not depend on accurate multi-sensor alignment. In contrast to previous work that took curvature into consideration, however, we treat curvature as an independent quantity that we consistently incorporate into every stage of the real-time reconstruction pipeline, including densely curvature-weighted ICP, range image fusion, local surface reconstruction, and rendering. Using multiple benchmark sequences, and in direct comparison to other state-of-the-art online acquisition systems, we show that our approach significantly reduces drift, both when analyzing individual pipeline stages in isolation, as well as seen across the online reconstruction pipeline as a whole

A high-affinity antibody against the CSP N-terminal domain lacks Plasmodium falciparum inhibitory activity

Malaria is a global health concern and research efforts are ongoing to develop a superior vaccine to RTS,S/AS01. To guide immunogen design, we seek a comprehensive understanding of the protective humoral response against Plasmodium falciparum circumsporozoite protein (PfCSP). In contrast to the well-studied responses to the repeat region and the C-terminus, the antibody response against the N-terminal domain of PfCSP (N-CSP) remains obscure. Here, we characterized the molecular recognition and functional efficacy of the N-CSP-specific monoclonal antibody 5D5. The crystal structure at 1.85 Åresolution revealed that 5D5 binds an α-helical epitope in N-CSP with high affinity through extensive shape and charge complementarity, and the unusual utilization of an N-linked glycan. Nevertheless, functional studies indicated low 5D5 binding to live Pf sporozoites, and lack of sporozoite inhibition in vitro and in mosquitoes. Overall, our data on low recognition and inhibition of sporozoites do not support the inclusion of the 5D5 epitope into the next generation of CSP-based vaccines.Summary Statement The Plasmodium falciparum sporozoite surface protein, PfCSP, is an attractive vaccine target, but the antibody response against the CSP N-terminal domain has remained understudied. Here, to guide immunogen design, Thai et al. provide insights into the binding motif and functional efficacy of the N-terminal domain-specific monoclonal antibody, 5D5

An integer representation for periodic tilings of the plane by regular polygons

We describe a representation for periodic tilings of the plane by regular polygons. Our approach is to represent explicitly a small subset of seed vertices from which we systematically generate all elements of the tiling by translations. We represent a tiling concretely by a (2+n)×4 integer matrix containing lattice coordinates for two translation vectors and n seed vertices. We discuss several properties of this representation and describe how to exploit the representation elegantly and efficiently for reconstruction, rendering, and automatic crystallographic classification by symmetry detection

Ab initio calculations of the hydrogen bond

Recent x-ray Compton scattering experiments in ice have provided useful information about the quantum nature of the interaction between H$_2$O monomers. The hydrogen bond is characterized by a certain amount of charge transfer which could be determined in a Compton experiment. We use ab-initio simulations to investigate the hydrogen bond in H$_2$O structures by calculating the Compton profile and related quantities in three different systems, namely the water dimer, a cluster containing 12 water molecules and the ice crystal. We show how to extract estimates of the charge transfer from the Compton profiles.Comment: 16 pages, 7 figures, to appear in Phys. Rev.

Palaeomicrobiology: application of ancient DNA sequencing to better understand bacterial genome evolution and adaptation

Next generation sequencing (NGS) has unlocked access to the wide range of non-cultivable microorganisms, including those present in the ancient past. The study of microorganisms from ancient sources (palaeomicrobiology) using DNA sequencing now provides a unique opportunity to examine ancient microbial genomic content, explore pathogenicity, and understand microbial evolution in greater detail than ever before. As a result, current studies have focused on reconstructing the evolutionary history of a number of human pathogens involved in ancient and historic pandemic events. These studies have opened the door for a variety of future palaeomicrobiology studies, which can focus on commensal microorganisms, species from non-human hosts, information from host-genomics, and the use of bacteria as proxies for additional information about past human health, behavior, migration, and culture. Here, we describe the origin and the historical and recent advances in the field of palaeomicrobiology, review some of the most notable ancient pathogenic microorganism studies, and provide perspectives on how NGS and whole genome information from ancient microorganisms contributes to our understanding of bacterial evolution on a broader scale. We conclude by exploring the application of newly developed tools in palaeomicrobiology and discussing how future studies can improve our current understanding of non-pathogenic microbes.Luis A. Arriola, Alan Cooper and Laura S. Weyric

Image-based Remapping of Material Appearance

Digital 3D content creation requires the ability to exchange assets across multiple software applications. For many 3D asset types, standard formats and interchange conventions are available. For material definitions, however, inter-application exchange is still hampered by different software packages supporting different BRDF models. To make matters worse, even if nominally identical BRDF models are supported, these often differ in their implementation, due to optimisations and safeguards in individual renderers. To facilitate appearance-preserving translation between different BRDF models whose precise implementation is not known (arguably the standard case with commercial systems), we propose a robust translation scheme which leaves BRDF evaluation to the targeted rendering system, and which expresses BRDF similarity in image space. As we will show, even naïve applications of a nonlinear fit which uses such an image space residual metric work well in some cases; however, it does suffer from instabilities for certain material parameters. We propose strategies to mitigate these instabilities and perform reliable parameter remappings between differing BRDF definitions. We report on experiences with this remapping scheme, both with respect to robustness and visual differences of the fits