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3D facial surface imaging is a useful tool in dentistry and in terms of diagnostics and treatment planning. Between-group PCA (bgPCA) is a method that has been used to analyse shapes in biological morphometrics, although various "pathologies" of bgPCA have recently been proposed. Monte Carlo (MC) simulated datasets were created here in order to explore "pathologies" of multilevel PCA (mPCA), where mPCA with two levels is equivalent to bgPCA. The first set of MC experiments involved 300 uncorrelated normally distributed variables, whereas the second set of MC experiments used correlated multivariate MC data describing 3D facial shape. We confirmed results of numerical experiments from other researchers that indicated that bgPCA (and so also mPCA) can give a false impression of strong differences in component scores between groups when there is none in reality. These spurious differences in component scores via mPCA decreased significantly as the sample sizes per group were increased. Eigenvalues via mPCA were A underestimated this quantity.When large vessels such as container ships are approaching their destination port, they are required by law to have a maritime pilot on board responsible for safely navigating the vessel to its desired location. The maritime pilot has extensive knowledge of the local area and how currents and tides affect the vessel's navigation. In this work, we present a novel end-to-end solution for estimating time-to-collision time-to-collision (TTC) between moving objects (i.e., vessels), using real-time image streams from aerial drones in dynamic maritime environments. Our method relies on deep features, which are learned using realistic simulation data, for reliable and robust object detection, segmentation, and tracking. Furthermore, our method uses rotated bounding box representations, which are computed by taking advantage of pixel-level object segmentation for enhanced TTC estimation accuracy. We present collision estimates in an intuitive manner, as collision arrows that gradually change its color to red to indicate an imminent collision. A set of experiments in a realistic shipyard simulation environment demonstrate that our method can accurately, robustly, and quickly predict TTC between dynamic objects seen from a top-view, with a mean error and a standard deviation of 0.358 and 0.114 s, respectively, in a worst case scenario.Single-object visual tracking aims at locating a target in each video frame by predicting the bounding box of the object. Recent approaches have adopted iterative procedures to gradually refine the bounding box and locate the target in the image. In such approaches, the deep model takes as input the image patch corresponding to the currently estimated target bounding box, and provides as output the probability associated with each of the possible bounding box refinements, generally defined as a discrete set of linear transformations of the bounding box center and size. At each iteration, only one transformation is applied, and supervised training of the model may introduce an inherent ambiguity by giving importance priority to some transformations over the others. This paper proposes a novel formulation of the problem of selecting the bounding box refinement. It introduces the concept of non-conflicting transformations and allows applying multiple refinements to the target bounding box at each iteration without introducing ambiguities during learning of the model parameters. Empirical results demonstrate that the proposed approach improves the iterative single refinement in terms of accuracy and precision of the tracking results.Automatic dating tools for historical documents can greatly assist paleographers and save them time and effort. This paper describes a novel method for estimating the date of historical Arabic documents that employs hierarchical fusions of multiple features. A set of traditional features and features extracted by a residual network (ResNet) are fused in a hierarchical approach using joint sparse representation. To address noise during the fusion process, a new approach based on subsets of multiple features is being considered. Following that, supervised and unsupervised classifiers are used for classification. We show that using hierarchical fusion based on subsets of multiple features in the KERTAS dataset can produce promising results and significantly improve the results.This paper proposes an objective glossiness index for objects in halftone color images. In the proposed index, we consider the characteristics of the human visual system (HVS) and associate the image's structure distortion and statistical information. According to the difference in the number of strategies adopted by the HVS in judging the difference between images, it is divided into single and multi-strategy modeling. In this study, we advocate multiple strategies to determine glossy or non-glossy quality. We assumed that HVS used different visual mechanisms to evaluate glossy and non-glossy objects. For non-glossy images, the image structure dominated, so the HVS tried to use structural information to judge distortion (a strategy based on structural distortion detection). For glossy images, the glossy appearance dominated; thus, the HVS tried to search for the glossiness difference (an appearance-based strategy). Herein, we present an index for glossiness assessment that attempts to explicitly model the structural dissimilarity and appearance distortion. We used the contrast sensitivity function to account for the mechanism of halftone images when viewed by the human eye. We estimated the structure distortion for the first strategy by using local luminance and contrast masking; meanwhile, local statistics changing in the spatial frequency components for skewness and standard deviation were used to estimate the appearance distortion for the second strategy. Experimental results showed that these two mixed-distortion measurement strategies performed well in consistency with the subjective ratings of glossiness in halftone color images.Hyperspectral imaging (HSI) in situ core scanning has emerged as a valuable and novel tool for rapid and non-destructive biogeochemical analysis of lake sediment cores. Variations in sediment composition can be assessed directly from fresh sediment surfaces at ultra-high-resolution (40-300 μm measurement resolution) based on spectral profiles of light reflected from sediments in visible, near infrared, and short-wave infrared wavelengths (400-2500 nm). Here, we review recent methodological developments in this new and growing field of research, as well as applications of this technique for paleoclimate and paleoenvironmental studies. Hyperspectral imaging of sediment cores has been demonstrated to effectively track variations in sedimentary pigments, organic matter, grain size, minerogenic components, and other sedimentary features. These biogeochemical variables record information about past climatic conditions, paleoproductivity, past hypolimnetic anoxia, aeolian input, volcanic eruptions, earthquake and flood frequencies, and other variables of environmental relevance. HSI has been applied to study seasonal and inter-annual environmental variability as recorded in individual varves (annually laminated sediments) or to study sedimentary records covering long glacial-interglacial time-scales (&gt;10,000 years).Photo Response Non-Uniformity (PRNU) is reputed the most successful trace to identify the source of a digital video. However, its effectiveness is mainly limited by compression and the effect of recently introduced electronic image stabilization on several devices. In the last decade, several approaches were proposed to overcome both these issues, mainly by selecting those video frames which are considered more informative. However, the two problems were always treated separately, and the combined effect of compression and digital stabilization was never considered. This separated analysis makes it hard to understand if achieved conclusions still stand for digitally stabilized videos and if those choices represent a general optimum strategy to perform video source attribution. In this paper, we explore whether an optimum strategy exists in selecting frames based on their type and their positions within the groups of pictures. We, therefore, systematically analyze the PRNU contribute provided by all frames belonging to either digitally stabilized or not stabilized videos. Results on the VISION dataset come up with some insights into optimizing video source attribution in different use cases.Dysfunction of neurons in the central nervous system is the primary pathological feature of Parkinson's disease (PD). Despite different triggering, emerging evidence indicates that neuroinflammation revealed through microglia activation is critical for PD. Moreover, recent investigations sought a potential relationship between Lrrk2 genetic mutation and microglia activation. In this paper, neuroinflammation in sporadic PD, Lrrk2-PD and unaffected Lrrk2 mutation carriers were investigated. The principal component analysis (PCA) and the subject's residual profile (SRP) techniques were performed on multiple groups and regions of interest in 22 brain-regions. The 11C-PBR28 binding profiles were compared in four genotypes depending on groups, i.e., HC, sPD, Lrrk2-PD and UC, using the PCA and SPR scores. The genotype effect was found as a principal feature of group-dependent 11C-PBR28 binding, and preliminary evidence of a MAB-Lrrk2 mutation interaction in manifest Parkinson's and subjects at risk was found.Cure rates for kidney cancer vary according to stage and grade; hence, accurate diagnostic procedures for early detection and diagnosis are crucial. Some difficulties with manual segmentation have necessitated the use of deep learning models to assist clinicians in effectively recognizing and segmenting tumors. Deep learning (DL), particularly convolutional neural networks, has produced outstanding success in classifying and segmenting images. [https://www.selleckchem.com/products/isoxazole-9-isx-9.html ISX-9] Simultaneously, researchers in the field of medical image segmentation employ DL approaches to solve problems such as tumor segmentation, cell segmentation, and organ segmentation. Segmentation of tumors semantically is critical in radiation and therapeutic practice. This article discusses current advances in kidney tumor segmentation systems based on DL. We discuss the various types of medical images and segmentation techniques and the assessment criteria for segmentation outcomes in kidney tumor segmentation, highlighting their building blocks and various strategies.Conversion of standard cameras to enable them to capture images in the ultraviolet (UV) and infrared (IR) spectral regions has applications ranging from purely artistic to science and research. Taking the modification of the camera a step further and removing the color filter array (CFA) results in the formation of a monochrome camera. The spectral sensitivities of a range of cameras with different sensors which were converted to monochrome were measured and compared with standard multispectral camera conversions, with an emphasis on their behavior from the UV through to the IR regions.