Dynamic Video Research Articles

Show Membership Value Through Dynamic Video

The Membership Management ReportVolume 19, Issue 5 p. 6-6 Share the Excitement With Probable Members Show Membership Value Through Dynamic Video First published: 03 April 2023 https://doi.org/10.1002/mmr.32121Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume19, Issue5May 2023Pages 6-6 RelatedInformation

Dynamic Infrared Imaging of Vitreous Floaters.

To evaluate the use of dynamic infrared (IR) imaging as a tool for the objective evaluation of symptomatic vitreous floaters and to correlate it with the patient symptomatology. Retrospective study that examined 66 eyes of 44 patients with symptomatic and asymptomatic vitreous opacities. Patients were imaged using the Heidelberg Spectralis dynamic infrared (IR) image in video mode to record the vitreous movements and shadow artifacts within 30 degrees of the center of the macula. Patients were also asked how symptomatic their vitreous floaters were from absent to severe. After reviewing IR videos and OCT, a grading system was created to evaluate the floaters and a masked reader was asked to evaluate the videos and OCT based on the grading system created. A total of 66 eyes were identified and examined with the IR videos, 50 were symptomatic, and 16 were asymptomatic. After masked review and analysis of the IR videos, there were 4 characteristics that correlated with the patient's symptoms: size, location, movement, and density of obscuration of the OCT B Scan by the vitreous opacity. A table with grading of these characteristics was created to analyze how symptomatic patients were. A masked grader was asked to grade the videos and OCT using the grading system created. A positive correlation was found between the masked grader and the symptoms of patients. (0.70039; p < 0.00001). Dynamic IR video capture of vitreous opacities is a new imaging technique that can qualitatively assess vitreous opacities in a way that correlates to a patient's symptoms. This imaging modality can provide a qualitative assessment of the patient's severity of symptoms based on the location, density, and movement of the visualized vitreous opacities in the imaged video.

Multimedia Technologies as a Component of the Modern Educational Environment

The article demonstrates the visualization of educational information by means of multimedia technologies included in the educational environment as a vivid trend. Its importance in the actively developing modern information space is revealed. Being a high-tech work of art, realized by means of hardware and software, multimedia has quickly become an effective component of educational activities, including text, graphics, animation, static and dynamic video, sound, etc. Among many means of visualizing educational information, multimedia is an effective media tool with complementary image (visual) and audio (auditory) components. Strengthening didactic capabilities of multimedia technology is carried out at the expense of dynamic visual information and various audio effects, contributing to the perception and assimilation of educational material. All this testifies to the fact that multimedia meets one of the leading didactic principles of learning which is visualization. This includes animated films, scientific or documentary videos, which, with proper methodological use in the modern educational environment, allow you to achieve the best learning outcomes. To a certain extent, the article notes the interactivity of multimedia and its importance in enhancing cognitive activity of students

Research on Multi-Scale Feature Extraction and Working Condition Classification Algorithm of Lead-Zinc Ore Flotation Foam

To address the problems of difficult online monitoring, low recognition efficiency and the subjectivity of work condition identification in mineral flotation processes, a foam flotation performance state recognition method is developed to improve the issues mentioned above. This method combines multi-dimensional CNN (convolutional neural networks) characteristics and improved LBP (local binary patterns) characteristics. We have divided the foam flotation conditions into six categories. First, the multi-directional and multi-scale selectivity and anisotropy of nonsubsampled shearlet transform (NSST) are used to decompose the flotation foam images at multiple frequency scales, and a multi-channel CNN network is designed to extract static features from the images at different frequencies. Then, the flotation video image sequences are rotated and dynamic features are extracted by the LBP-TOP (local binary patterns from three orthogonal planes), and the CNN-extracted static picture features are fused with the LBP dynamic video features. Finally, classification decisions are made by a PSO-RVFLNs (particle swarm optimization-random vector functional link networks) algorithm to accurately identify the foam flotation performance states. Experimental results show that the detection accuracy of the new method is significantly improved by 4.97% and 6.55%, respectively, compared to the single CNN algorithm and the traditional LBP algorithm, respectively. The accuracy of flotation performance state classification was as high as 95.17%, and the method reduced manual intervention, thus improving production efficiency.

Self-Supervised Facial Motion Representation Learning via Contrastive Subclips

Facial motion representation learning has become an exciting research topic, since biometric technologies are becoming more common in our daily lives. One of its applications is identity verification. After recording a dynamic facial motion video for enrollment, the user needs to show a matched facial appearance and make a facial motion the same as the enrollment for authentication. Some recent research papers have discussed the benefits of this new biometric technology and reported promising results for both static and dynamic facial motion verification tasks. Our work extends the existing approaches and introduces compound facial actions, which contain more than one dominant facial action in one utterance. We propose a new self-supervised pretraining method called contrastive subclips that improves the model performance with these more complex and secure facial motions. The experimental results show that the contrastive subclips method improves upon the baseline approaches, and the model performance for test data can reach 89.7% average precision.

Steerable pyramid with subpixel compensation for remotely measuring and magnifying dynamic vibration of moving objects

Measuring vibration generated by a moving object is difficult because the accelerometers must be mounted on the moving object. Moreover, the weight and damping ratio of the accelerometers could cause extra mass and damping effect on small moving objects, casting doubt on the accuracy of such measurements. This paper reports a novel method that can remotely measure vibration from the motion of an object captured by a high-speed camera. Aimed at magnifying subtle changes in the presence of large motions, several phase-based video process methods have been proposed previously. Although these methods can capture such subtle changes, they all have deficiencies. For instance, the pure Euler method needs to process every position of movement, making the process very slow if the video file is large. Furthermore, the methods that combine Eulerian and Lagrangian algorithms ignore the subpixel compensation of stabilization. This paper reports a novel dynamic video processing technology by combining phase-based video process technology with visual tracking. The reason for using visual tracking is to extract the target and detect its dynamic position. To compensate for the error induced during the process of video stabilization, the steerable pyramid method was applied to enhance the magnification of subtle vibration with small error. The proposed method was verified by experiments to show that it can effectively improve the signal-to-noise ratio (SNR). The results demonstrated that the vibration and motion generated from the moving object can be detected and measured with satisfactory SNR. With the help of such an innovative method, in the future, both the motion and vibration signals of fast-moving objects, like airplanes, cars, or even small structures, can be measured simultaneously and remotely using high-speed cameras that can also move.

DEEP MOVEMENT: Deep learning of movie files for management of endovascular thrombectomy

ObjectivesTreatment and outcomes of acute stroke have been revolutionised by mechanical thrombectomy. Deep learning has shown great promise in diagnostics but applications in video and interventional radiology lag behind. We aimed to develop a model that takes as input digital subtraction angiography (DSA) videos and classifies the video according to (1) the presence of large vessel occlusion (LVO), (2) the location of the occlusion, and (3) the efficacy of reperfusion.MethodsAll patients who underwent DSA for anterior circulation acute ischaemic stroke between 2012 and 2019 were included. Consecutive normal studies were included to balance classes. An external validation (EV) dataset was collected from another institution. The trained model was also used on DSA videos post mechanical thrombectomy to assess thrombectomy efficacy.ResultsIn total, 1024 videos comprising 287 patients were included (44 for EV). Occlusion identification was achieved with 100% sensitivity and 91.67% specificity (EV 91.30% and 81.82%). Accuracy of location classification was 71% for ICA, 84% for M1, and 78% for M2 occlusions (EV 73, 25, and 50%). For post-thrombectomy DSA (n = 194), the model identified successful reperfusion with 100%, 88%, and 35% for ICA, M1, and M2 occlusion (EV 89, 88, and 60%). The model could also perform classification of post-intervention videos as mTICI < 3 with an AUC of 0.71.ConclusionsOur model can successfully identify normal DSA studies from those with LVO and classify thrombectomy outcome and solve a clinical radiology problem with two temporal elements (dynamic video and pre and post intervention).Key Points• DEEP MOVEMENT represents a novel application of a model applied to acute stroke imaging to handle two types of temporal complexity, dynamic video and pre and post intervention.• The model takes as an input digital subtraction angiograms of the anterior cerebral circulation and classifies according to (1) the presence or absence of large vessel occlusion, (2) the location of the occlusion, and (3) the efficacy of thrombectomy.• Potential clinical utility lies in providing decision support via rapid interpretation (pre thrombectomy) and automated objective gradation of thrombectomy outcomes (post thrombectomy).

Gaze prediction based on long short-term memory convolution with associated features of video frames

Gaze prediction is a key issue for visual perception research. It can be used to infer important regions in videos to reduce the amount of computation in learning and inference of various analysis tasks. Vanilla methods for dynamic video unable to extract valid features, and the motion information among dynamic video frames are ignored, which lead to poor prediction results. We propose a gaze prediction based on LSTM convolution with associated features of video frames (LSTM-CVFAF). Firstly, by adding learnable central prior knowledge, the proposed method can effectively and accurately extract the spatial information of each frame. Secondly, the LSTM is deployed to get temporal motion gaze features. Finally, the spatial and temporal motion information is fused to generate the gaze prediction maps of the dynamic video. Compared with the state-of-art models on DHF1K dataset, the CC, AUC-j, sAUC, NSS are separately increased by 5.1%, 0.6%, 38.2% and 0.5%.

Associations between executive function and attention abilities and language and social communication skills in young autistic children.

Executive functioning describes a set of cognitive processes that affect thinking and behavior. Past research has shown that autistic individuals often have delays in the acquisition of executive function abilities. Our study explored how differences in executive function and attention abilities relate to social abilities and communication/language in 180 young autistic children. Data were gathered via caregiver report (questionnaires/interviews) and an assessment of vocabulary skills. The ability to sustain attention to a dynamic video was measured via eye tracking. We found that children with higher levels of executive function skills demonstrated lower levels of social pragmatic problems, a measure of having difficulties in social contexts. Furthermore, children who were able to sustain their attention longer to the video displayed higher levels of expressive language. Our results emphasize the importance of executive function and attention skills across multiple areas of functioning in autistic children, in particular those that involve language and social communication.

Differential neural reward reactivity in response to food advertising medium in children.

Food cues including food advertisements (ads) activate brain regions related to motivation and reward. These responses are known to correlate with eating behaviors and future weight gain. The objective of this study was to compare brain responses to food ads by different types of ad mediums, dynamic (video) and static (images), to better understand how medium type impacts food cue response. Children aged 9-12 years old were recruited to complete a functional magnetic resonance imaging (fMRI) paradigm that included both food and non-food dynamic and static ads. Anatomical and functional images were preprocessed using the fMRIPrep pipeline. A whole-brain analysis and a targeted region-of-interest (ROI) analysis for reward regions (nucleus accumbens, orbitofrontal cortex, amygdala, insula, hypothalamus, ventral tegmental area, substantia nigra) were conducted. Individual neural responses to dynamic and static conditions were compared using a paired t-test. Linear mixed-effects models were then constructed to test the differential response by ad condition after controlling for age, sex, BMI-z, physical activity, and % of kcal consumed of a participant's estimated energy expenditure in the pre-load prior to the MRI scan. A total of 115 children (mean=10.9 years) completed the fMRI paradigm. From the ROI analyses, the right and left hemispheres of the amygdala and insula, and the right hemisphere of the ventral tegmental area and substantia nigra showed significantly higher responses for the dynamic food ad medium after controlling for covariates and a false discovery rate correction. From the whole-brain analysis, 21 clusters showed significant differential responses between food ad medium including the precuneus, middle temporal gyrus, superior temporal gyrus, and inferior frontal gyrus, and all regions remained significant after controlling for covariates. Advertising medium has unique effects on neural response to food cues. Further research is needed to understand how this differential activation by ad medium ultimately affects eating behaviors and weight outcomes.

The effects of preconceptions on directed forgetting - the example of e-learning

This study used the item-based legal forgetting paradigm to investigate the effects of preconception type and material type on directed forgetting, and further explored the effects of preconception on directed forgetting of specific and gist memories through 2 experiments using static textual images and selected dynamic videos as experimental material in a simulated online teaching environment. The results showed that positive preconceptions induced directed forgetting of specific memory, while negative preconceptions enhanced directed forgetting of gist memory only. At the same time, static materials showed a directional forgetting effect, while dynamic materials did not show a directional forgetting effect. Static materials improve individual learning more than dynamic materials.

Human Action Recognition for Dynamic Scenes of Emergency Rescue Based on Spatial-Temporal Fusion Network

Targeting the problems of the insufficient utilization of temporal and spatial information in videos and a lower accuracy rate, this paper proposes a human action recognition method for dynamic videos of emergency rescue based on a spatial-temporal fusion network. A time domain segmentation strategy based on random sampling maintains the overall time domain structure of the video. Considering the spatial-temporal asynchronous relationship, multiple asynchronous motion sequences are increased as input of the temporal convolutional network. spatial-temporal features are fused in convolutional layers to reduce feature loss. Because time series information is crucial for human action recognition, the acquired mid-layer spatial-temporal fusion features are sent into Bidirectional Long Short-Term Memory (Bi-LSTM) to obtain the human movement features in the whole video temporal dimension. Experiment results show the proposed method fully fuses spatial and temporal dimension information and improves the accuracy of human action recognition in dynamic scenes. It is also faster than traditional methods.

Value of deep learning models based on ultrasonic dynamic videos for distinguishing thyroid nodules.

This study was designed to distinguish benign and malignant thyroid nodules by using deep learning(DL) models based on ultrasound dynamic videos. Ultrasound dynamic videos of 1018 thyroid nodules were retrospectively collected from 657 patients in Zhejiang Cancer Hospital from January 2020 to December 2020 for the tests with 5 DL models. In the internal test set, the area under the receiver operating characteristic curve (AUROC) was 0.929(95% CI: 0.888,0.970) for the best-performing model LSTM Two radiologists interpreted the dynamic video with AUROC values of 0.760 (95% CI: 0.653, 0.867) and 0.815 (95% CI: 0.778, 0.853). In the external test set, the best-performing DL model had AUROC values of 0.896(95% CI: 0.847,0.945), and two ultrasound radiologist had AUROC values of 0.754 (95% CI: 0.649,0.850) and 0.833 (95% CI: 0.797,0.869). This study demonstrates that the DL model based on ultrasound dynamic videos performs better than the ultrasound radiologists in distinguishing thyroid nodules.

Integrating audio and visual modalities for multimodal personality trait recognition via hybrid deep learning.

Recently, personality trait recognition, which aims to identify people's first impression behavior data and analyze people's psychological characteristics, has been an interesting and active topic in psychology, affective neuroscience and artificial intelligence. To effectively take advantage of spatio-temporal cues in audio-visual modalities, this paper proposes a new method of multimodal personality trait recognition integrating audio-visual modalities based on a hybrid deep learning framework, which is comprised of convolutional neural networks (CNN), bi-directional long short-term memory network (Bi-LSTM), and the Transformer network. In particular, a pre-trained deep audio CNN model is used to learn high-level segment-level audio features. A pre-trained deep face CNN model is leveraged to separately learn high-level frame-level global scene features and local face features from each frame in dynamic video sequences. Then, these extracted deep audio-visual features are fed into a Bi-LSTM and a Transformer network to individually capture long-term temporal dependency, thereby producing the final global audio and visual features for downstream tasks. Finally, a linear regression method is employed to conduct the single audio-based and visual-based personality trait recognition tasks, followed by a decision-level fusion strategy used for producing the final Big-Five personality scores and interview scores. Experimental results on the public ChaLearn First Impression-V2 personality dataset show the effectiveness of our method, outperforming other used methods.

Multiple Description Coding for Best-Effort Delivery of Light Field Video Using GNN-Based Compression

In recent years, Light Field (LF) video has grabbed much attention as an emerging form of immersive media. LF collects, through a lens matrix, light information emanating in every direction, and obtains rich information about the scene, providing users with an immersive 6 Degrees of Freedom (DoF) experience. The visual content between different viewpoints is highly homogenized, suggesting the possibility of good compression and encoding. However, most fixed-structure LF coding schemes are difficult to adapt to the real-time requirements of different LF applications and best-effort network conditions causing packet loss. In this paper, we propose a dynamic adaptive LF video transmission scheme that can achieve high compression and yet provide near-distortion-free LF video when the network condition is stable. Additionally, for unstable network conditions a description scheduling algorithm is proposed, which can decode the LF video with the highest possible quality even if partial data cannot be received completely and/or timely. We achieve this by designing a Multiple Description Coding (MDC) based solution to transport the LF video compressed by a Graph Neural Network (GNN) model. Experimental results show that the scheduling algorithm can improve the quality of the decoding results by 3% to 15%. Compared with other similar schemes, our system greatly improves the reliability of the video streaming system against packet loss/error and supports heterogeneous receivers.

Methodology to Control IoT Smart Devices Driven by Digital TV Video Scene

This paper presents a novel methodology to control Internet of Things (IoT) devices synchronized with digital TV (DTV) signal dynamic video flow. Through this approach, broadcasters can send commands to digital TV receivers and produce multiples physical effects on IoT devices paired with TV sets. Among IoT devices examples, are: smart lamps, robotic seats, smart watches, wearable devices, etc. Those devices will synchronously react with the video scene, according to the IoT command type received by TV set. With this methodology, broadcasters may multiplex multiple IoT commands and be able to produce several effects at same time, through different IoT devices, creating a home multi-dimensional environment. In case there is any command that does not correspond to any paired device on receiver side, or the command is corrupted, the same is simply discarded. The validation of this methodology is based on the development of a receiver embedded system to detect signalization of IoT commands, Data filtering and IoT device control through Smart TV. The IoT devices control is sync with video scene and is based at time stamps added to the DTV signal. Furthermore, to check transceiver channel behavior, a transport stream benchmark, composed by IoT commands data flow multiplexed with audio and video medias in multiple complexity levels, was generated. Lab experiments with transmission tests and IoT devices control results shown technical feasibility to use this methodology in a real digital TV environment.

Towards Real-Time Video Caching at Edge Servers: A Cost-Aware Deep Q-Learning Solution

Given the rapid growth of user-generated videos, internet traffic has been heavily dominated by online video streaming. Caching videos on edge servers in close proximity to users has been an effective approach to reduce the backbone traffic and the request response time, as well as to improve the video quality on the user side. Video popularity, however, can be highly dynamic over time. The cost of cache replacement at edge servers, particularly that related to service interruption during replacement, is not yet well understood. This paper presents a novel lightweight video caching algorithm for edge servers, seeking to optimize the hit rate with real-time decisions and minimized cost. Inspired by recent advances in deep Q-learning, our DQN-based online video caching (DQN-OVC) makes effective use of the rich and readily available information from users and networks. We decompose the Q-value function as a product of the video value function and the action function, which significantly reduces the state space. We instantiate the action function for cost-aware caching decisions with low complexity so that the cached videos can be updated continuously and instantly with dynamic video popularity. We used video traces from Tencent, one of the largest online video providers in China, to evaluate the performance of our DQN-OVC and to compare it with state-of-the-art solutions. The results demonstrate that DQN-OVC significantly outperforms the baseline algorithms in the edge caching context.

Looking tight: Visual judgments of knot strength reveal the limits of physical scene understanding

While early studies cataloged striking errors of physical scene understanding in naive observers, a resurgence of research has instead revealed a remarkable ability to intuit how physical scenes will unfold. Properties such as stability, mass, gravity and kinematics may all elicit accurate intuitions, especially when presented in naturalistic settings. A leading interpretation of these findings is that physical scene understanding recruits a general-purpose “physics engine”. Under this theory, the mind simulates how a scene will unfold by modeling or approximating its physics, and only fails for physics problems that are contrived or presented without context. But might there be tasks that persistently strain physical reasoning, even when presented naturalistically and in realistic contexts? Here, 5 experiments explore this question by evaluating intuitions about the strength of simple knots. Knots are naturalistic stimuli that are ubiquitous across cultures and time periods, and in many contexts evaluating them correctly can spell the difference between safety and peril. Despite this, here we show that naive observers fail to discern even striking differences in strength between knots. In a series of two-alternative forced-choice tasks, observers viewed simple “bends” (i.e., knots joining two lengths of string) and decided which would require more force to undo. Though the relative strength of these bends is well-documented, observers’ judgments completely failed to reflect these distinctions—including in naturalistic photographs (E1), idealized renderings (E2), dynamic videos (E3), and even when accompanied by schematic diagrams of the knots’ structures (E4). Moreover, these failures persisted despite observers demonstrating visual understanding of the topological differences between the knots (E5); in other words, even when observers knew exactly what kind of knot they were viewing, they failed to extract or predict its physical behavior. These results expose a blindspot in physical reasoning, posing a new kind of challenge to general-purpose theories of scene understanding.

Fusing surveillance videos and three‐dimensional scene: A mixed reality system

AbstractAugmented Virtual Environments (AVE) or Virtual‐Reality Fusion systems fuse dynamic videos with static three‐dimensional (3D) models of a virtual environment to provide an optimal solution for visualizing and understanding multichannel surveillance systems. However, texture distortion caused by viewpoint changes in such systems is a critical issue that needs to be addressed. To minimize texture fusion distortion, this paper presents a novel virtual environment system in two phases, offline and online phases, to dynamically fuse multiple surveillance videos with a virtual 3D scene. In the offline phase, a static virtual environment is obtained by performing a 3D photogrammetric reconstruction from the input images of the scene. In the online phase, the virtual environment is augmented by fusing multiple videos through two optional strategies. One strategy is to dynamically map images of different videos onto a 3D model of the virtual environment, and the other is to extract moving objects and represent them as billboards. The system can be used to visualize a 3D environment from any viewpoint augmented by real‐time videos. Experiments and user studies in different scenarios demonstrate the superiority of our system.

LVE-S2D: Low-Light Video Enhancement From Static to Dynamic

Recently, deep-learning-based low-light video enhancement methods have drawn wide attention and achieved remarkable performance. However, limited by the difficulty in collecting dynamic low-light and well-lighted video pairs in real scenes, how to construct video sequences for supervised learning and design a low-light enhancement network for real dynamic video remains a challenge. In this paper, we propose a simple yet effective low-light video enhancement method (LVE-S2D), which generates dynamic video training pairs from static videos, and enhances the low-light video by mining dynamic temporal information. To obtain low-light and well-lighted video pairs, a sliding window-based dynamic video generation mechanism is designed to produce pseudo videos with rich dynamic temporal information. Then, a siamese dynamic low-light video enhancement network is presented, which effectively utilizes temporal correlation between adjacent frames to enhance the video frames. Extensive experimental results demonstrate that the proposed method not only achieves superior performance on static low-light videos, but also outperforms the state-of-the-art methods on real dynamic low-light videos.