Set Of Scenes Research Articles

Impact of surface roughness on the deposition of saliva and fingerprint residue on non-porous substrates

A diverse range of non-porous substrate surfaces routinely encountered in crime scene settings were evaluated for their affinity towards deposited saliva and fingerprint residue. Raman spectroscopy, atomic force microscopy (AFM), field-emission scanning electron microscopy (FE-SEM) and water contact angle measurements were employed to quantitatively and qualitatively evaluate the physicochemical variables impacting the deposition of touch and salivary deposits. It was established that small changes in surface roughness and physicochemical interactions, including hydrophobicity, resulted in observable impacts on the spread of salivary deposits and deposition of fingerprint residue. It was further identified that not only did the surface free energy (SFE) available to the deposit, measured through water contact angles, influence the deposition and spread of the material on the surface, but that the nature of the deposit further impacted these dynamics. This preliminary study has developed a platform to gain a better understanding of the importance of surface roughness as a consideration, when interrogating how forensically relevant deposits such as saliva and fingerprint residue interact with non-porous surfaces. This better understanding is necessary when reflecting on the successfulness of recovering DNA, when considering means to improve recovery of trace samples from crime scenes, and when improving assessment of the likelihood of transfer of a deposit from one substrate to another upon contact.

Podcasting the Pandemic: Exploring Storytelling Formats and Shifting Journalistic Norms in News Podcasts Related to the Coronavirus

ABSTRACT The coronavirus pandemic dominated headlines at the same time that mainstream news organizations began adding more podcasts to their digital storytelling platforms. This popular form of audio journalism appears to be unrestricted by traditional journalism practices and constraints of deadlines and space. Few academic studies thus far have examined the characteristics of news podcasts (i.e., shows created by professional news organizations) and where they fit within the journalism landscape. Through a content analysis of 40 news podcast episodes related to the coronavirus pandemic, this study explores the extent to which narrative storytelling devices (voice, character development, scene setting, dialogue, dramatic tension), and other elements, such as opinion, were incorporated into the sample. Results show all but one episode utilized narrative storytelling elements, but the degree of editing and production varied widely. Results also demonstrate an overall shift away from journalistic norms of objectivity and toward interpretive reporting in this emerging format. Reporters and hosts often appear as both characters and experts in the episodes, and narrative elements serve to emphasize storytelling over content.

Compressive Coded Rotating Mirror Camera for High-Speed Imaging

We develop a novel compressive coded rotating mirror (CCRM) camera to capture events at high frame rates in passive mode with a compact instrument design at a fraction of the cost compared to other high-speed imaging cameras. Operation of the CCRM camera is based on amplitude optical encoding (grey scale) and a continuous frame sweep across a low-cost detector using a motorized rotating mirror system which can achieve single pixel shift between adjacent frames. Amplitude encoding and continuous frame overlapping enable the CCRM camera to achieve a high number of captured frames and high temporal resolution without making sacrifices in the spatial resolution. Two sets of dynamic scenes have been captured at up to a 120 Kfps frame rate in both monochrome and colored scales in the experimental demonstrations. The obtained heavily compressed data from the experiment are reconstructed using the optimization algorithm under the compressive sensing (CS) paradigm and the highest sequence depth of 1400 captured frames in a single exposure has been achieved with the highest compression ratio of 368 compared to other CS-based high-speed imaging technologies. Under similar conditions the CCRM camera is 700× faster than conventional rotating mirror based imaging devices and could reach a frame rate of up to 20 Gfps.

Proportion Estimation for Urban Mixed Scenes Based on Nonnegative Matrix Factorization for High-Spatial Resolution Remote Sensing Images

High-spatial resolution urban scenes, which contain ecological, social, and environmental zones, are composed of diverse functional areas. Depicting urban scenes from both qualitative and quantitative aspects, i.e., scene classification and scene unmixing, are increasingly important tasks. The purpose of scene classification is to mark a scene as a separate category, and this approach is widely applied to qualitatively define scenes. However, with the rapid development of cities, urban scenes usually show a mixed nature, resulting in urban mixed scenes (UMSs). Traditional scene classification cannot describe the mix of urban scenes. Few studies have focused on the quantitative measurements of UMSs, which aim at discovering and analyzing the proportion of different subscenes. In this article, a scene unmixing framework based on nonnegative matrix factorization for UMSs (UnUMS) is proposed to estimate the mixing ratio of urban scenes. In the UnUMS, a deep feature representation is used for the high-dimensional representation of the image, and then a nonnegative matrix factorization strategy is used to estimate the proportion of UMSs. Finally, the UMS can be described by the proportion of each subscene. To confirm the robust generalizability of UnUMS, the WH-center Mix dataset with a set of small mixed scenes and the WH-hy LMix dataset with a large UMS image are utilized. The root-mean-square error and the newly proposed vector angular distance are utilized to quantitatively evaluate the effect of scene unmixing from different aspects. Experiments with the two datasets demonstrate that UnUMS can accurately estimate the proportions of different scenes for UMS.

Retracted] Application of Photoshop Graphics and Image Processing in the Field of Animation

With the rapid development of digital art and machine science and technology, the production form of modern animation tends to be diversified, and with the important role of animation color in the animation film foil and rendering, more and more attention was paid by the animation design industry. Computer graphic technology is a new type of artistic creation method, and its birth and development are closely related to computer technology. The purpose of this paper is to make some analysis and induction from the perspective of science and technology and culture and to explore the application of Photoshop graphics and image processing technology in the field of animation. First, the definition of “animation” and the extension of the animation category are introduced. Then, the main functions of Photoshop image processing technology are described in detail. According to the research content, this paper designs a self‐made experimental short film “future city.” After the simulation of experimental animation film and the setting and positioning of the experimental scene, Photoshop software was used to show the animation scene. The experimental results show that the color changes in the short film “future city.” After the saturation of sunny scenery is reduced by 3 points and the brightness is reduced by 20 points and the filter, mode change, and color adjustment are carried out, a complete rainy scenery can be obtained. The self‐made experimental short film has guiding significance for the creation and practice.

John Akomfrah’s Nine Muses: A Reading with Glissant’s “Relation” and Quantum Entanglement

John Akomfrah's film The Nine Muses (2010, United Kingdom) deals with the theme of the journey across the great works of literature. In particular the film sets the works of the Western canon, from Homer's epic Odyssey to Joyce's Malloy, within the visual frame of archival footage dealing with the Windrush Generation, that is, Caribbean Black migration to England following World War II. In counterpoint to that set of visuals Akomfrah has created a futurist set of scenes set in Alaska, and the frozen northlands, to which the concept of exile from the tropical settings of the Caribbean or Africa might seem most extreme. This article will provide an analysis of the journey and exile motifs utilizing the framing of Glissant's notion of Relation, primarily, and secondarily the quantum concept of entanglement.

Improved Exemplar based Image Inpainting for Partial Instance Occlusion Handling with K-means Clustering and YCbCr Color Space

The images acquired in real time outdoor environment are often subject to uneven illumination conditions, cloudy weather, lighting conditions. The instances of partial occlusions deteriorate the background modeling of such scenes. Varying illumination outdoor scene set of images with partial occlusions are addressed through this investigative work. There is a need for a restoration method that finds improved subjective perception and execution time. The proposed work focuses on novel amended exemplar model to improve the subjective perception. The exemplar inpainting method is improved through the color quantization with K-means clustering approach in YCbCr color space. Experimental validations and proposed method results show better improvement in qualitative and objective measures than existing methods. The average “Peak Signal to Noise Ratio (PSNR)” as 28.2869 and “Structural SIMilarity index (SSIM)” as 0.9759 of the proposed method has shown better results respectively both visually and with a tradeoff in time.

Icelandic Kami

Utamakura is a traditional Japanese technique of recognizing, interpreting, and utilizing the web of intertextual meanings which have accrued around particular place names over centuries of poetic practice. In general, these utamakura places were originally (in the 7th-9th centuries) associated with Shintō gods (kami), though in later periods the web of meanings in most cases came to include (and often became dominated by) secular rather than spiritual associations. Japanese poet Takahashi Mutsuo, who has published both poetic and theoretical works on the subject of utamakura, seeks to recover the original spiritual power of utamakura place names. He has also expanded the concept to include places of mythic spiritual importance outside of Japan, mostly in the Greco-Roman world.
 Taking inspiration from Takahashi's revivification of this mediaeval poetic device, I am currently in the midst of a three-year project to write a series of (at this point seven) multimedia chamber music pieces called the utamakura series, pieces inspired alternately by traditional Japanese locations and locations in Northern Europe. My 2018 piece utamakura 2: Arnardalar for violin, piano, and fixed audiovisual media is an exploration of the Icelandic valley of Arnardulr in the Westfjords, the setting of a key early scene in the Fóstbræðra saga. My work draws on both the saga's descriptions of the place and the current place as it is today, highlighting the flux of time and exploring the power of art to infuse itself into – and change perceptions of – physical locations.
 In this paper, I will explain the conceptual processes involved in writing the piece, with an emphasis on the intercultural aesthetic of my work and how Japanese philosophy of art and religion can offer a creative new perspective on the Scandinavian lands which are the settings of the North's oldest literature.

Deep Learning Approach to the Detection of Scattering Delay in Radar Images

In radar imaging, stochastic target models are routinely used to describe distributed scatterers. In such models, the reflectivity of a target or clutter is a realization of a stochastic process with certain autocorrelation properties. While most targets reflect the impinging electromagnetic radiation instantaneously, some targets with complicated geometry and/or material composition may exhibit delayed scattering. Detecting such delays will provide valuable data for target identification. However, the scattering delay can be confused with the signal propagation delay, and this difference is sometimes rather subtle. Due to the stochastic nature of the radar data, the classification errors are inevitable. The misclassification rate depends on the parameters characterizing the radar system, imaging scene, and observation settings. A convolutional neural network is applied to the problem of discrimination between the instantaneous and delayed targets in synthetic aperture radar images. A trained neural network demonstrates the discrimination quality commensurate with that of the benchmark maximum likelihood-based classifier.

PTZ camera calibration based on improved DLT transformation model and vanishing Point constraints

High-precision camera calibration is the key to obtaining accurate 3D information of the target from the image. Due to the shortcomings of both the classical simplified linear model and the complete distortion model in the algorithm, a PTZ camera autocalibration method is proposed, it is an improved DLT method based on three vanishing point constraints and virtual identification point constraints. This method uses the two vanishing points obtained by cascading Hough transform of vehicle trajectory line and car body edge line and the third vanishing point obtained by geometric relationship of three vanishing points to complete the initial parameter calibration of the camera, and calculate the intrinsic parameters, rotation matrix and translation matrix in order. Then artificially filtered spatial virtual identification points are used to establish a camera parameter constraint with the actual scene non-virtual reference points, and the DLT direct linear transformation method is used for final correction. This study is tested under different rotation angles and pitch angles of traffic scene, and it uses the differences between the actual physical distance of lane line in the scene and its tested distance as the measure of the calibration error. The error of proposed method at different conditions is less than 2.5%. The test results of multiple sets of highway scenes show that the method proposed in this paper can meet the application requirements for the physical measurement error of the highway, and it has greater accuracy and better stability compared with the reference method. The obtained camera parameters can be used to accurately calculate vehicle speed and estimate spatial position.

The InflateSAR Campaign: Evaluating SAR Identification Capabilities of Distressed Refugee Boats

Most of the recent research in the field of marine target detection has been concentrating on ships with large metallic parts. The focus of this work is on much more challenging targets represented by small rubber inflatables. They are of importance, since in recent years they have largely been used by migrants to cross the Mediterranean Sea between Libya and Europe. The motivation of this research is to mitigate the ongoing humanitarian crisis at Europe’s southern borders. These boats, packed with up to 200 people, are in no way suitable to cross the Mediterranean Sea or any other big water body and are in distress from the moment of departure. The establishment of a satellite-based surveillance infrastructure could considerably support search and rescue missions in the Mediterranean Sea, reduce the number of such boats being missed and mitigate the ongoing death in the open ocean. In this work we describe and analyze data from the InflateSAR acquisition campaign, wherein we gathered multiple-platform SAR imagery of an original refugee inflatable. The test site for this campaign is a lake which provides background clutter that is more predictable. The analysis considered a sum of experiments, enabling investigations of a broad range of scene settings, such as the vessel’s orientation, superstructures and speed. We assess their impact on the detectability of the chosen target under different sensor parameters, such as polarimetry, resolution and incidence angle. Results show that TerraSAR-X Spotlight and Stripmap modes offer good capabilities to potentially detect those types of boats in distress. Low incidence angles and cross-polarization decrease the chance of a successful identification, whereas a fully occupied inflatable, orthogonally oriented to the line of sight, seems to be better visible than an empty one. The polarimetric analyses prove the vessel’s different polarimetric behavior in comparison with the water surface, especially when it comes to entropy. The analysis considered state-of-the-art methodologies with single polarization and dual polarization channels. Finally, different metrics are used to discuss whether and to which extent the results are applicable to other open ocean datasets. This paper does not introduce any vessel detection or classification algorithm from SAR images. Rather, its results aim at paving the way to the design and the development of a specially tailored detection algorithm for small rubber inflatables.

Sparse Representations for Object- and Ego-Motion Estimations in Dynamic Scenes.

Disentangling the sources of visual motion in a dynamic scene during self-movement or ego motion is important for autonomous navigation and tracking. In the dynamic image segments of a video frame containing independently moving objects, optic flow relative to the next frame is the sum of the motion fields generated due to camera and object motion. The traditional ego-motion estimation methods assume the scene to be static, and the recent deep learning-based methods do not separate pixel velocities into object- and ego-motion components. We propose a learning-based approach to predict both ego-motion parameters and object-motion field (OMF) from image sequences using a convolutional autoencoder while being robust to variations due to the unconstrained scene depth. This is achieved by: 1) training with continuous ego-motion constraints that allow solving for ego-motion parameters independently of depth and 2) learning a sparsely activated overcomplete ego-motion field (EMF) basis set, which eliminates the irrelevant components in both static and dynamic segments for the task of ego-motion estimation. In order to learn the EMF basis set, we propose a new differentiable sparsity penalty function that approximates the number of nonzero activations in the bottleneck layer of the autoencoder and enforces sparsity more effectively than L1- and L2-norm-based penalties. Unlike the existing direct ego-motion estimation methods, the predicted global EMF can be used to extract OMF directly by comparing it against the optic flow. Compared with the state-of-the-art baselines, the proposed model performs favorably on pixelwise object- and ego-motion estimation tasks when evaluated on real and synthetic data sets of dynamic scenes.

High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery

Deep metric learning has recently received special attention in the field of remote sensing (RS) scene characterization, owing to its prominent capabilities for modeling distances among RS images based on their semantic information. Most of the existing deep metric learning methods exploit pairwise and triplet losses to learn the feature embeddings with the preservation of semantic-similarity, which requires the construction of image pairs and triplets based on the supervised information (e.g., class labels). However, generating such semantic annotations becomes a completely unaffordable task in large-scale RS archives, which may eventually constrain the availability of sufficient training data for this kind of models. To address this issue, we reformulate the deep metric learning scheme in a semi-supervised manner to effectively characterize RS scenes. Specifically, we aim at learning metric spaces by utilizing the supervised information from a small number of labeled RS images and exploring the potential decision boundaries for massive sets of unlabeled aerial scenes. In order to reach this goal, a joint loss function, composed of a normalized softmax loss with margin and a high-rankness regularization term, is proposed, as well as its corresponding optimization algorithm. The conducted experiments (including different state-of-the-art methods and two benchmark RS archives) validate the effectiveness of the proposed approach for RS image classification, clustering and retrieval tasks. The codes of this paper are publicly available.

Effects of age on listening and postural control during realistic multi-tasking conditions.

Successful performance of balance-related activities requires the effective integration of sensory, cognitive, and motor processes that can be affected by age-related changes. Of these age-related sensory changes, the effects of declines in hearing on balance have not been well-studied despite the fact that hearing loss has now been acknowledged as a significant risk factor for falls. The goal of this study was to evaluate age-related differences in a "standing while listening" task within increasingly challenging conditions resembling those that are often encountered in realistic, everyday situations. This study used a dual-task paradigm in an immersive Virtual Reality street scene setting in which postural load (firm, compliant), listening load (number of talkers), and visual load (eyes open/closed) were manipulated. A multi-talker divided attention listening task was used. Postural performance was assessed using center of pressure (COP) path length, while listening performance was assessed using spoken word recognition accuracy. Results demonstrated that age-related differences were observed in postural performance when postural demands were the highest and in listening performance when listening demands were the highest. Proportional dual-task costs were more pronounced for postural task performance compared to listening task performance and were more pronounced for older compared to younger adults. Postural dual-task costs increased as a function of increasing listening loads. Removal of visual information improved listening task performance across both groups and reduced the dual-task costs to listening in older adults when listening demands were highest (resulting in dual-task benefits). Taken together, the findings support previously documented age-related declines in postural control and auditory processing, demonstrate that increasing listening demands may result in poorer balance, particularly in older adults, and provide additional insights into the interactive effects of age-related declines when sensory, motor, and cognitive challenges are incremented factorially.

Come‐hither looks: the Hollywood vamp and the function of cinema

Come‐hither looks: the Hollywood vamp and the function of cinema

The “Lost” Scenes fromThe Merry Wives of Windsor: Robertson Davies’s Shakespearean Hoax

This article introduces a hitherto unpublished dramatic script by Robertson Davies: a set of scenes interpolated into the Stratford Festival’s 1956 production of The Merry Wives of Windsor. Although these pseudo-Elizabethan additions were first performed as an elaborate prank on Stratford audience members and theatre critics, this article argues that Davies’s work represents a major unacknowledged contribution to the performance history of Merry Wives. Working in collaboration with director Michael Langham, Davies drew on the hypotheses of early twentieth-century textual editors to reconstruct a more narratively “complete” version of the play. In its integration of scholarship and playwriting, Davies’s script not only anticipated later scholarly reconstructions of plays such as Pericles and Cardenio, but it also remains a significant untapped resource for theatre directors today.

Dissociating central and auditory attention: Is there a shared bottleneck for response selection and auditory search?

Central and auditory attention are limited in capacity. In dual-tasks, central attention is required to select the appropriate response, but because central attention is limited in capacity, response selection can only be carried out for one task at a time. In auditory search tasks, search time to detect the target sound increases with the number of distractor sounds added to the auditory scene (set sizes), indicating that auditory attention is limited in capacity. Here, we investigated whether central and auditory attention relied on common or distinct capacity limitations using a dual-task paradigm. In two experiments, participants completed a visual choice discrimination task (task 1) together with an auditory search task (task 2), and the two tasks were separated by an experimentally modulated stimulus onset asynchrony (SOA). Analysing auditory search time as a function of SOA and set sizes (locus-of-slack method) revealed that the auditory search process in task 2 was performed after response selection in a visual two-choice discrimination task 1 (Experiment 1), but concurrently with response selection in a visual four-choice discrimination task 1 (Experiment 2). Hence, although response selection in the visual four-choice discrimination task demanded more central attention as compared with response selection in the two-choice discrimination task, the auditory search process was performed in parallel. Distribution analyses of inter-response time further indicated that parallel processing of response selection and auditory search was not influenced by response grouping. Taken together, the two experiments provided evidence that central and auditory attention relied on distinct capacity limitations.

Mary in the Apostolic Church in the Light of the Lucan Writings

Lukan Mariology does not present a holistic picture of Jesus’ Mother, although no doubt it is fundamentally important for the narrative of Luke-Acts. The Infancy Narrative and the three further traditions disseminated in the main bulk of the Lukan work (Lk 8:21; 11:27-28; Acts 1:14) show that her person cannot be limited to her faith or motherhood only. The author of the third Gospel composed a careful set of scenes in order to share with his readers much fuller picture of Mary. Being a Mother of the Lord, she is also a disciple following God’s will and the teaching of her Son. In this study the author is looking for some fresh insights from the text applying methodology presented recently by C. Bennema and other scholars interested in the Gospels’ characters and their characterisation.

Research on DC Cross-regional Accommodation Model Based on Scene Analysis

In order to cope with the impact of uncertainty of the renewable energy forecasting in two regions on the cross-regional accommodation of renewable energy, a DC cross-regional accommodation model based on scene analysis is established. Firstly, the ARMA model, scene reduction and scene combination concept are used to construct the scene set describing the uncertainty of the renewable energy forecasting in two regions. Then, the model is used to obtain the unit start-stop plan in two regions and the DC line transmission plan with the goal of smallest renewable energy power abandonment statistically. Finally, based on the measured data of renewable energy power generation, the unit power generation schedule with the goal of smallest renewable energy power abandonment in the actual operation of the power grid is obtained. The validity of the model is verified by a cross-regional system example of actual data reconstruction in a province.

Visual Information flow in Wilson-Cowan networks

In this paper, we study the communication efficiency of a psychophysically tuned cascade of Wilson-Cowan and divisive normalization layers that simulate the retina-V1 pathway. This is the first analysis of Wilson-Cowan networks in terms of multivariate total correlation. The parameters of the cortical model have been derived through the relation between the steady state of the Wilson-Cowan model and the divisive normalization model. The communication efficiency has been analyzed in two ways: First, we provide an analytical expression for the reduction of the total correlation among the responses of a V1-like population after the application of the Wilson-Cowan interaction. Second, we empirically study the efficiency with visual stimuli and statistical tools that were not available before 1) we use a recent, radiometrically calibrated, set of natural scenes, and 2) we use a recent technique to estimate the multivariate total correlation in bits from sets of visual responses, which only involves univariate operations, thus giving better estimates of the redundancy. The theoretical and the empirical results show that, although this cascade of layers was not optimized for statistical independence in any way, the redundancy between the responses gets substantially reduced along the neural pathway. Specifically, we show that 1) the efficiency of a Wilson-Cowan network is similar to its equivalent divisive normalization model; 2) while initial layers (Von Kries adaptation and Weber-like brightness) contribute to univariate equalization, and the bigger contributions to the reduction in total correlation come from the computation of nonlinear local contrast and the application of local oriented filters; and 3) psychophysically tuned models are more efficient (reduce more total correlation) in the more populated regions of the luminance-contrast plane. These results are an alternative confirmation of the efficient coding hypothesis for the Wilson-Cowan systems, and, from an applied perspective, they suggest that neural field models could be an option in image coding to perform image compression.NEW & NOTEWORTHY The Wilson-Cowan interaction is analyzed in total correlation terms for the first time. Theoretical and empirical results show that this psychophysically tuned interaction achieves the biggest efficiency in the most frequent region of the image space. This is an original confirmation of the efficient coding hypothesis and suggests that neural field models can be an alternative to divisive normalization in image compression.