Visual Artifacts Research Articles

Staging a Musical Self through Paper, Canvas, and The Screen: A Taxonomy of Musicians’ Self-Portraits from the Renaissance to the Digital Age

. Musicians have engaged in visual self-representation at least since the Renaissance and have continued the tradition to modern times with contemporary practices including selfies and generative technology and AI art. The practitioners include so-called classical composers (Schoenberg is a well-known case) and performers (Caruso, for instance), but also pop singers and musicians (Joni Mitchell and Patti Smith, among others). The media used varies from oil on canvas to drawings on paper, from traditional photography to digital media. In some instances, there are grave, pompous self-representations, but caricatures also abound (e.g., Donizetti). There are also some miscategorized self-portraits (i.e., portraits misattributed to their subject) and many more purposely fake or mocking self-portraits including contemporary Roman musician and comic Federico Maria Sardelli, which would indicate that the category of “self-portrait” adds value and prestige to any visual artifact. Furthermore, many visual artists, especially during the Renaissance, present themselves as faux musicians, possibly as a sign of nobility or education. Slowly but surely, women have also claimed a space in the realm of musicians’ self-portraits since many of them, belonging to the higher echelons of society, were both visual artists and active musicians (Maria Antonio of Bavaria, Ducreaux and Schröter, among others). In some instances, the musician is truly obsessed with his or her own image to the point that, in addition to visual self-representation, he or she also provides written autobiographies and even musical self-portraits in sound (Spohr and Schoenberg, for instance). In the end, any attempt to create a taxonomy of “musicians’ self-portraits” amounts to a serious interrogation of the usual categories of “self-portrait,” “musician,” and “artist” and to the staging of a vulnerable, doubtful self that wants to be reasserted. Keywords. Self-portraits, Oil on canvas, Drafts on paper, Photography, Selfies, Digital and AI art.

Lethal brands: Terrorist groups’ logos and violence

Terrorist organizations are often described as brands. However, the most important visual representations of these brands – terrorist groups’ logos – have remained unexplored. In this article, we demonstrate that logos are signalling devices that provide vital cues on the propensity to use violence. To this end, we code and analyse 562 terrorist logos (2000–16). After providing a descriptive overview of the main colours and symbols used by terrorist groups worldwide, we rely on a zero-inflated negative binomial model to analyse the relationship between these organizations’ visual choices and their deadly activities. Our results show that the presence of violent, religious, and extremist symbols in terrorist organizations’ logos, as well as the use of black as the main colour, correlates with more frequent and deadlier attacks. These findings have important policy implications, demonstrating that logos serve as behavioural cues predicting the threat posed by terrorist groups not less effectively than their ideology. By highlighting the importance of visual artifacts like logos and their amenability to quantitative research, our article also provides a novel methodological contribution to international relations, helping bridge the gap between explanatory and critical security studies.

Progressive Few-Shot Adaptation of Generative Model with Align-Free Spatial Correlation

In few-shot generative model adaptation, the model for target domain is prone to the mode-collapse. Recent studies attempted to mitigate the problem by matching the relationship among samples generated from the same latent codes in source and target domains. The objective is further extended to image patch-level to transfer the spatial correlation within an instance. However, the patch-level approach assumes the consistency of spatial structure between source and target domains. For example, the positions of eyes in two domains are almost identical. Thus, it can bring visual artifacts if source and target domain images are not nicely aligned. In this paper, we propose a few-shot generative model adaptation method free from such assumption, based on a motivation that generative models are progressively adapting from the source domain to the target domain. Such progressive changes allow us to identify semantically coherent image regions between instances generated by models at a neighboring training iteration to consider the spatial correlation. We also propose an importance-based patch selection strategy to reduce the complexity of patch-level correlation matching. Our method shows the state-of-the-art few-shot domain adaptation performance in the qualitative and quantitative evaluations.

3D Synthetic Brain MRI with Compressed Sensing: Can It Be a Promising Way Forward for Daily Neuroimaging?

Synthetic MRI can provide multiple contrast-weighted brain images with high resolution from a single scan via a 3D sequence using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). This study aimed to assess the diagnostic image quality of 3D synthetic MRI using compressed sensing (CS) in clinical practice. We retrospectively reviewed the imaging data of 47 patients who underwent brain MRI, including 3D synthetic MRI using CS in a single session, between December 2020 and February 2021. Two neuroradiologists independently evaluated the overall image quality, anatomic demarcation, and artifacts for synthetic 3D T1-weighted, T2-weighted, FLAIR, phase-sensitive inversion recovery (PSIR), and double inversion recovery images, using a 5-point Likert scale. The interobserver agreement between the two readers was assessed using percent agreement and weighted κ statistics. The overall image quality of 3D synthetic T1WI and PSIR was good to excellent, with easy or excellent anatomic demarcation and mild or no visible artifact. However, other 3D synthetic MRI-derived images showed insufficient image quality and anatomic demarcation with marked CSF pulsation artifacts. In particular, 3D synthetic FLAIR showed high-signal artifacts on the brain surface. 3D synthetic MRI, at its current status, cannot completely replace conventional brain MRI in daily clinical practice. However, 3D synthetic MRI can achieve scan-time reduction using CS and parallel imaging and may be useful for motion-prone or pediatric patients requiring 3D images where time-efficiency is important.

Desain Environment Serial Animasi Edukasi Astronomi Untuk Siswa Sekolah Dasar

Astronomy is a branch of science that studies the phenomena of objects in outer space. Astronomy can be a gateway to attract elementary school students' interest in studying science, but there are limitations such as the lack of availability of facilities to support astronomy learning. Astronomy is important to teach because it can train reasoning and imagination. There is a lot of material and practice in astronomy so learning needs to be packaged in an interesting way and in language that is easy for children to understand. The animation medium was chosen to introduce astronomy material. To support the creation of animation, the environment (background) needs to be designed to provide a more interesting viewing experience as well as to support astronomy learning. This research used in-depth interview research methods, questionnaires, observation, and literature studies. The resulting design concept was then developed through experimental studies and assistance through in-depth interviews. The results of this research are the environment concept and props design in the astronomy animation series entitled "Taman Siswa+". The design of the environment adopts the concept of "Typical Indonesia" where the output of the environment is inspired by the findings of visual artifacts in the surrounding environment. The design of the environment also adopts the concept of "Scientific Based" so that the environmental outcomes of Mercury and Venus correspond theoretically based on the latest research results.Keywords: Animation, Astronomy, Education, Environment, Elementary School

Followback Clusters, Satellite Audiences, and Bridge Nodes: Coengagement Networks for the 2020 US Election.

The 2020 United States (US) presidential election was - and has continued to be - the focus of pervasive and persistent mis- and disinformation spreading through our media ecosystems, including social media. This event has driven the collection and analysis of large, directed social network datasets, but such datasets can resist intuitive understanding. In such large datasets, the overwhelming number of nodes and edges present in typical representations create visual artifacts, such as densely overlapping edges and tightly-packed formations of low-degree nodes, which obscure many features of more practical interest. We apply a method, coengagement transformations, to convert such networks of social data into tractable images. Intuitively, this approach allows for parameterized network visualizations that make shared audiences of engaged viewers salient to viewers. Using the interpretative capabilities of this method, we perform an extensive case study of the 2020 United States presidential election on Twitter, contributing an empirical analysis of coengagement. By creating and contrasting different networks at different parameter sets, we define and characterize several structures in this discourse network, including bridging accounts, satellite audiences, and followback communities. We discuss the importance and implications of these empirical network features in this context. In addition, we release open-source code for creating coengagement networks from Twitter and other structured interaction data.

Artifacts from the Perspective of Effutu Masquerade Performance: An Aesthetic Album

Artifacts from the Perspective of Effutu Masquerade Performance: An Aesthetic Album

Robust detection of seam carving with low ratio via pixel adjacency subtraction and CNN-based transfer learning

Seam carving is an outstanding content-aware image resizing technique. When the resizing ratio is relatively lower, seam carving can achieve image resizing without leaving any visual obvious artifacts. However, it may also be used for malicious purposes, such as changing image semantic content by object removal. Seam caving changes the relationship between neighboring pixels, thus seam carved images should be different from natural ones. Inspired by this, an end-to-end seam carving detection network, namely SCDNet, is proposed to detect seam carved images. To highlight seam carving artifacts, a pre-processing layer, namely the subtractive pixel adjacency module, is designed as the first layer of SCDNet. Then, 16 residual blocks with shortcut learn features from the input, and a spatial pyramid pooling module is embedded at the end of the residual block to generate fixed-length features, which can eliminate the requirement of fixed-resolution input images for existing deep models. Finally, the features are fed into the fully-connected layer for final classification. In addition, to expose seam carved images with a small carving ratio, a transfer learning strategy is exploited to gradually clarify the artifacts. Extensive experiments demonstrate that SCDNet obtains better detection accuracy than state-of-the-art methods, especially for seam carving with small scaling ratios.

Analyzing Human Observer Ability in Morphing Attack Detection—Where Do We Stand?

Morphing attacks are based on the technique of digitally fusing two (or more) face images into one, with the final visage resembling the contributing faces. Morphed images not only pose a challenge to Face-Recognition Systems (FRS) but also challenge experienced human observers due to high quality, postprocessing eliminating any visible artifacts, and further the printing and scanning process. Few studies have concentrated on examining how human observers can recognize morphing attacks, even as several publications have examined the susceptibility of automated FRS to morphing attacks and offered morphing attack detection (MAD) approaches. MAD approaches base their decisions either on a single image with no reference to compare against (Single-Image MAD (S-MAD)) or using a reference image (Differential MAD (D-MAD)). One prevalent misconception is that an examiner’s or observer’s capacity for facial morph detection depends on their subject expertise, experience, and familiarity with the issue. No works have reported the specific results of observers who regularly verify identity (ID) documents for their jobs. As human observers are involved in checking ID documents having facial images, a lapse in their competence can result in significant societal challenges. To assess the observers’ proficiency, this research first builds a new benchmark database of realistic morphing attacks from 48 different subjects, resulting in 400 morphed images. Unlike the previous works, we also capture images from Automated Border Control (ABC) gates to mimic realistic border-crossing scenarios in the D-MAD setting with 400 probe images, to study the ability of human observers to detect morphed images. A new dataset of 180 morphing images is also produced to research human capacity in the S-MAD environment. In addition to creating a new evaluation platform to conduct S-MAD and D-MAD analysis, the study employs 469 observers for D-MAD and 410 observers for S-MAD who are primarily governmental employees from more than 40 countries, along with 103 control group members who are not examiners. The analysis offers intriguing insights and highlights the lack of expertise and failure to recognize a sizable number of morphing attacks by experienced observers. Human observers tend to detect morphed images to a lower accuracy as compared to the four automated MAD algorithms evaluated in this work. The results of this study are intended to aid in the development of training programs that will prevent security failures while determining whether an image is bona fide or altered.

0086 Good quality sleep facilitates psychomotor learning via fast spindles among older adults

Abstract Introduction Sleep spindles are neural oscillations of frequency between 11 to 16 Hz during non rapid eye movement sleep. They are coordinated with hippocampal ripples and slow oscillations to facilitate memory consolidation by modulating synaptic plasticity. Fast spindles, predominant in central and parietal derivations of EEG during Stage 2 NREM sleep, are associated with overnight visuomotor skills enhancement, especially in the later part of the night. The relationship between fast and slow spindles during sleep and psychomotor ability has not been characterized well, particularly in older adults. The objective of this study was to study the relationship between sleep microstructure during various stages of sleep and cognition in good and poor sleepers. Methods This is a cross sectional observational study conducted on 16 male participants (age 63.6 ± 5.22 years). Good (n=9) and poor sleepers (n=7) were classified on the basis of PSQI score (<=5 is good). Overnight polysomnography was done and ICMR-NCTB battery was administered for assessment of cognition. In DOMINO software, after visual artifact removal, spindles were analyzed by selecting 11 - 12.99 Hz (slow) in F4, and 13 -16 Hz (fast) in C2. Statistical analysis was done using GraphPad prism (v 9.4.1). Results Fast spindle index in N2 (good sleeper: r = -0.850, p = 0.006; poor sleeper: r = 0.398, p = 0.466), fast spindle density in N2 (good sleeper: r = -0.870, p = 0.004; poor sleeper: r = -0.331, p = 0.468), total fast spindle number during deep sleep (good sleeper: r = -0.600, p = 0.031; poor sleeper: r = -0.244, p = 0.599), and total slow spindle density (good sleeper: r = -0.833, p = 0.025; poor sleeper: r = -0.080, p = 0.865) were found to be negatively correlated with psychomotor ability assessed by trail making test. Conclusion Among older adults, good sleepers, as compared to poor sleepers overall have a higher fast spindle index and density during N2 sleep and there is significant correlation with better psychomotor ability. Support (if any) All India Institute of Medical Sciences and University College of London Collaborative Grant (AIIMS-UCL 12)

0263 Feasibility of Measuring Sleep Spindles Using a Wearable EEG Headband in Adolescents with Neurodevelopmental Disorders

Abstract Introduction There is compelling evidence that sleep spindles, electroencephalogram (EEG) signatures of non-rapid eye movement sleep, are reliable markers of thalamocortical dysfunction in Schizophrenia. While promising, this research is limited by traditional sleep EEG methods, which cultivate small, non-representative samples. Wearable EEG headbands could be used to study naturalistic sleep across several nights in individuals who cannot tolerate standard sleep assessments, but this has yet to be tested. We aim to test feasibility of using a dry-EEG headband to measure sleep spindles in adolescents with a neurodevelopmental disorder, 22q11.2 Deletion Syndrome (22q11DS). Methods Adolescents with 22q11DS (n=7; 3 Females; Age: 15-28 years) were asked to sleep in their own bed wearing a dry-EEG headband (Dreem 3). Feasibility of data acquisition was determined by the ability of the participants to record at least 3 consecutive nights. We performed visual artifact detection and examined the ability to detect sleep spindles using the well-validated Ferrarelli Spindle Detection Algorithm on the F7-O2 electrode. Results All subjects completed at least 3 nights of recording (range: 3-5 nights), suggesting the device was well-tolerated. The data contained minimal artifact; 17 out of the 22 nights available for analysis contained artifact in < 20% of sleep epochs. The best night contained artifact in only 6% of sleep epochs. Sleep spindles were successfully detected using an automatic detector (M= 1.92 spindles/minute). Conclusion This pilot study suggests dry-EEG headbands offer a feasible method to measure sleep spindles in individuals with neurodevelopmental disorders. This new technology could allow for scalable biomarkers of atypical neurodevelopment associated with psychiatric disorders. Support (if any) RO1 MH085953; U01MH10171; UCLA Center for Autism Research Pilot Grant; Dreem Coup de Coeur Award

Making the Fantastic Real: Exploring Transmedial Aspects of Cosplay Costumes

Abstract Cosplayers create elaborate costumes with fantastic details and effects imitating the visual and material culture artifacts and features of fictional characters. As such, cosplay costumes balance wearability for the cosplayer and accuracy to the selected character’s appearance. Further challenges to accurate portrayals are costumes that need to be animated or made from materials resembling the visuals originally created using animation or CGI. This article employs Tzvetan Todorov’s theory that suggests the fantastic is a liminal moment of hesitation between belief and disbelief. The author also draws on the concepts of Steven Shaviro’s “tactile convergence” and Sarah Gilligan’s “tactile transmediality,” where the material real-world qualities of cosplayers’ costumes cross between the fictional imaginative space of the story and the lived material body. This analysis further highlights how cosplayers spend extraordinary amounts of time and money on their costumes to portray fantastic characters with transmedial qualities and narratives regarding the character’s appearance.

Plot extraction and the visualization of narrative flow

Abstract This article discusses the development of an automated plot extraction system for narrative texts. Acknowledging the distinction between plot, as an object of study with its own rich history and literature, and features of a text that may be automatically extractable, we begin by characterizing a text’s scatter plot of entities. This visualization of a text reveals entity density patterns characterizing the particular telling of the story under investigation and leads to effective scene partitioning. We then introduce the concept of narrative flow, a graph representation of the narrative ordering of scenes (the syuzhet) that includes how entities move through scenes from the text, and investigate the degree to which narrative flow can be automatically extracted given a glossary of plot-important objects, actors, and locations. Our subsequent analysis then explores the correlation between subjective notions of plot and the information extracted through these visualizations. In particular, we discuss narrative structures commonly found within the graphs and make comparisons with ground truth narrative flow graphs, showing mixed results highlighting the difficulty of plot extraction. However, the visual artifacts and common structural relationships seen in the graphs provide insight into narrative and its underlying plot.

A novel LTPO AMOLED pixel circuit and driving scheme for variable refresh rate

This paper proposes a novel pixel circuit and driving scheme that adopts low-temperature polycrystalline silicon and oxide thin-film transistors (LTPO TFTs) for mobile devices using active-matrix organic light-emitting diode (AMOLED) displays. The proposed pixel circuit and driving scheme provide uniform luminance and render flicker invisible at variable refresh rates (VRRs) from 1 to 120 Hz. Using the proposed pixel circuit with extended compensation time (tCOMP) improves the luminance uniformity at a high-frame rate. The proposed driving scheme applies a voltage to the driving TFTs (D-TFTs) higher than the programmed data voltage during the skip frame. This reduces the flicker caused by the hysteresis of D-TFTs during low-frame rate driving. A 6.0-inch quad high-definition (QHD) LTPO-based AMOLED display was fabricated using the new pixel circuit and driving scheme. Experimental results of the proposed pixel circuit show that the standard deviation of luminance was reduced from 0.056 to 0.008 by extending tCOMP from 2 to 8 µs. The flicker level was −51 dB, so there was no visual artifact during 1 Hz driving. A flicker-free LTPO-based AMOLED display with low power consumption is possible; driving can proceed in 1–120 Hz range.

Improving Compressed Video Using Single Lightweight Model with Temporal Fusion Module.

Video compression algorithms are commonly used to reduce the number of bits required to represent a video with a high compression ratio. However, this can result in the loss of content details and visual artifacts that affect the overall quality of the video. We propose a learning-based restoration method to address this issue, which can handle varying degrees of compression artifacts with a single model by predicting the difference between the original and compressed video frames to restore video quality. To achieve this, we adopted a recursive neural network model with dilated convolution, which increases the receptive field of the model while keeping the number of parameters low, making it suitable for deployment on a variety of hardware devices. We also designed a temporal fusion module and integrated the color channels into the objective function. This enables the model to analyze temporal correlation and repair chromaticity artifacts. Despite handling color channels, and unlike other methods that have to train a different model for each quantization parameter (QP), the number of parameters in our lightweight model is kept to only about 269 k, requiring only about one-twelfth of the parameters used by other methods. Our model applied to the HEVC test model (HM) improves the compressed video quality by an average of 0.18 dB of BD-PSNR and -5.06% of BD-BR.

Behind the Words: Visual Insights into Students’ Perception of University Study of Tourism, Leisure, and Recreation

ABSTRACT The loss of previously distinct contours in the area of tourism, leisure, and recreation is evident through the penetration of health, sport, hospitality, economic, and management elements into the discipline. Here we use the term “recreology” and ask how students understand and depict it in visual representations. The analysis of visual artifacts (n = 47) reveals a rich thematic and semantic breadth of elements, phenomena, and symbols depicted. The study of recreology can then be presented in three main types of representations, the first as sport (adventure) tourism, the second as personal development, and the third in a strongly abstract and diverse treatment of variedness. This richness makes it possible to use in tourism education not only Aristotle’s terms for the intellectual virtues techné, phronesis, and episteme, but also the hitherto unused nous and sophia.

A deep learning-based reconstruction approach for accelerated magnetic resonance image of the knee with compressed sense: evaluation in healthy volunteers

To evaluate the feasibility of combining compressed sense (CS) with a newly developed deep learning-based algorithm (CS-AI) using convolutional neural networks to accelerate 2D MRI of the knee. In this prospective study, 20 healthy volunteers were scanned with a 3T MRI scanner. All subjects received a fat-saturated sagittal 2D proton density reference sequence without acceleration and four additional acquisitions with different acceleration levels: 2, 3, 4 and 6. All sequences were reconstructed with the conventional CS and a new CS-AI algorithm. Two independent, blinded readers rated all images by seven criteria (overall image impression, visible artifacts, delineation of anterior ligament, posterior ligament, menisci, cartilage, and bone) using a 5-point Likert scale. Signal- and contrast-to-noise ratios were calculated. Subjective ratings and quantitative metrics were compared between CS and CS-AI with similar acceleration levels and between all CS/CS-AI images and the non-accelerated reference sequence. Friedman and Dunn´s multiple comparison tests were used for subjective, ANOVA and the Tukey Kramer test for quantitative metrics. Conventional CS images at the lowest acceleration level (CS2) were already rated significantly lower than reference for 6/7 criteria. CS-AI images maintained similar image quality to the reference up to CS-AI three for all criteria, which would allow for a reduction in scan time of 64% with unchanged image quality compared to the unaccelerated sequence. SNR and CNR were significantly higher for all CS-AI reconstructions compared to CS (all p < 0.05). AI-based image reconstruction showed higher image quality than CS for 2D knee imaging. Its implementation in the clinical routine yields the potential for faster MRI acquisition but needs further validation in non-healthy study subjects. Combining compressed SENSE with a newly developed deep learning-based algorithm using convolutional neural networks allows a 64% reduction in scan time for 2D imaging of the knee. Implementation of the new deep learning-based algorithm in clinical routine in near future should enable better image quality/resolution with constant scan time, or reduced acquisition times while maintaining diagnostic quality.

A Companion to Early Modern Istanbul

A Companion to Early Modern Istanbul

Super-resolution network with dynamic cleanup and temporal–spatial attention for compressed videos

Video super-resolution methods focus on restoring high-resolution video frames from low-resolution videos with pre-defined degradations, and few consider compression. However, the videos on the Internet are compressed to reduce the massive size of video data. Thus, they also contain visual noises and artifacts due to the lossy compression. In this paper, we propose a video super-resolution model specific for compressed videos to suppress artifacts and restore high-resolution contents. We propose a two-stage super-resolution network for compressed videos that contains three new modules: cleanup and local alignment in the first stage, 3D-convolution temporal–spatial attention in the second stage. Specifically, the cleanup module is designed in stage-1 to suppress the artifacts and noises of low-quality inputs. The subsequent local alignment is designed to aggregate adjacent frames to maintain high-frequency details. The 3D-convolution attention is used in stage-2 to refine the weights of the feature maps. In addition, we propose the new CrfVideos dataset to facilitate fair comparisons, containing HEVC/H.265 compressed videos of different compression levels. Extensive experiments substantiate that our method outperforms state-of-the-art methods on the HEVC/H.265 compressed benchmark with medium to high compression rates by 0.3–0.7 dB in terms of PSNR and exceeds state-of-the-art methods on benchmark Vid4 by 0.2–0.8 dB. The code will be released at https://github.com/cvygkhv/decompression.

CyTran: A cycle-consistent transformer with multi-level consistency for non-contrast to contrast CT translation

We propose a novel approach to translate unpaired contrast computed tomography (CT) scans to non-contrast CT scans and the other way around. Solving this task has two important applications: (i) to automatically generate contrast CT scans for patients for whom injecting contrast substance is not an option, and (ii) to enhance the alignment between contrast and non-contrast CT by reducing the differences induced by the contrast substance before registration.Our approach is based on cycle-consistent generative adversarial convolutional transformers, for short, CyTran. Our neural model can be trained on unpaired images, due to the integration of a multi-level cycle-consistency loss. Aside from the standard cycle-consistency loss applied at the image level, we propose to apply additional cycle-consistency losses between intermediate feature representations, which enforces the model to be cycle-consistent at multiple representations levels, leading to superior results. To deal with high-resolution images, we design a hybrid architecture based on convolutional and multi-head attention layers. In addition, we introduce a novel data set, Coltea-Lung-CT-100W, containing 100 3D triphasic lung CT scans (with a total of 37,290 images) collected from 100 female patients (there is one examination per patient). Each scan contains three phases (non-contrast, early portal venous, and late arterial), allowing us to perform experiments to compare our novel approach with state-of-the-art methods for image style transfer.Our empirical results show that CyTran outperforms all competing methods. Moreover, we show that CyTran can be employed as a preliminary step to improve a state-of-the-art medical image alignment method. We release our novel model and data set as open source at: https://github.com/ristea/cycle-transformer.Our qualitative and subjective human evaluations reveal that CyTran is the only approach that does not introduce visual artifacts during the translation process. We believe this is a key advantage in our application domain, where medical images need to precisely represent the scanned body parts.