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High-quality video object segmentation is a challenging visual computing task. Interactive segmentation can improve segmentation results. This paper proposes a multi-round interactive dynamic propagation instance-level video object segmentation network based on click interaction. The network consists of two parts: a user interaction segmentation module and a bidirectional dynamic propagation module. A prior segmentation network was designed in the user interaction segmentation module to better segment objects of different scales that users click on. The dynamic propagation network achieves high-precision video object segmentation through the bidirectional propagation and fusion of segmentation masks obtained from multiple rounds of interaction. Experiments on interactive segmentation datasets and video object segmentation datasets show that our method achieves state-of-the-art segmentation results with fewer click interactions. [ABSTRACT FROM AUTHOR]

In the dynamic visual cabinet, the occurrence of motion blur when consumers take out commodities will reduce the accuracy of commodity detection. Recently, although Transformer-based video deblurring networks have achieved results compared to Convolutional Neural Networks in some blurring scenarios, they are still challenging for the non-uniform blurring problem that occurs when consumers pick up the commodities, such as the problem of difficult alignment of blurred video frames of small commodities and the problem of underutilizing the effective information between the video frames of commodities. Therefore, an improved Transformer video deblurring network is proposed. Firstly, a multi-scale Transformer feature extraction method is utilized for non-uniform blurring. Secondly, for the problem of difficult alignment of small-item-blurred video frames, a temporal interactive attention mechanism is designed for video frame alignment. Finally, a feature recurrent fusion mechanism is introduced to supplement the effective information of commodity features. The experimental results show that the proposed method has practical significance in improving the accuracy of commodity detection. Moreover, compared with the recent Transformer deblurring algorithm Video Restoration Transformer, the Peak Signal-to-Noise Ratio of this paper's algorithm is higher than that of the Deep Video Deblurring dataset and the Fuzzy Commodity Dataset by 0.23 dB and 0.81 dB, respectively. [ABSTRACT FROM AUTHOR]

This study evaluated the use of interactive, branched videos compared with traditional passive linear delivery for enhancing student engagement and learning in online courses. Undergraduate biology students were provided with either branched decision‐based or linear videos on cell biology and protein purification as self‐guided or consolidation activities. While the interactive branched videos did not improve learning gains, thematic analysis revealed that students found them more enjoyable and preferable for revision. However, most students felt linear passive videos were more logically structured for core content delivery. In a revised format, with clearer scaffolding, the interactive branched videos were perceived as significantly more engaging and useful when utilised for a problem‐solving activity. Students welcomed the autonomy of directing their learning path but desired support to avoid missing critical information. Overall, thoughtfully designed branched videos can increase student motivation, but their utility depends on context. Our findings indicate the importance of balancing interactivity, clear organisation and purpose when incorporating these innovative formats into online learning. Branched videos show promise for increasing engagement but require intentional instructional design tailored to learning objectives. [ABSTRACT FROM AUTHOR]

Danmaku, a real-time comment function covering the top of the video, appears from the right of the video like a bullet and slides out horizontally from the left, which is gaining popularity in Asia. In recent years, the research on the analysis of massive danmaku data has mushroomed. The danmaku data contains a host of valuable information, such as the emotional expressions, attitudes, and opinions of the people watching the video, which helps people quickly get the content and effect of the video. The information is more representative and comprehensive with the ever-increasing amount of danmaku data over time. However, extracting valuable danmaku from huge amounts of data is a challenging task. Therefore, in this paper, we introduce VisDmk, an interactive visual analysis system, to help to analyze video content and effect. VisDmk incorporates five views: the projection view to exhibit emotion distribution, the detail view to analyze specific danmaku information, the individual view to illustrate the difference between viewers, the theme-aware view to identify themes in different periods, and the video view to ascertain some inference within a video. Case studies and user observation were conducted to evaluate this system. [ABSTRACT FROM AUTHOR]

Numerous learners watch knowledgeable videos with comments or annotations anchored to the video timeline. These comments are learners' discussions with abundant informational and social content along with video timelines, but the content is usually fragmented and scattered. To extract, organize, and highlight useful information from the discussion, we adopted text mining approaches and designed an interactive visualization tool in the lecture interface for learners, including the following components along with the video timeline: (1) the relevance of comments to the lecture, (2) the comment topics throughout the lecture, and (3) the difficulty level perceived by learners. We conducted a lab experiment with 24 students to examine the effects of the visualization tool on the learning process and outcomes. We found that learners perceived a significantly higher social presence and performed better in open‐book quizzes, searching tasks, and summarizing lectures using the visualization tool. This suggests that the visualization of timeline‐anchored commenting potentially facilitates learners' participation in discussions and contributions to the learning community. [ABSTRACT FROM AUTHOR]

Image stylization has seen significant advancement and widespread interest over the years, leading to the development of a multitude of techniques. Extending these stylization techniques, such as Neural Style Transfer (NST), to videos is often achieved by applying them on a per‐frame basis. However, per‐frame stylization usually lacks temporal consistency, expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal consistency suffer from one or more of the following drawbacks: They (1) are only suitable for a limited range of techniques, (2) do not support online processing as they require the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency control. Domain‐agnostic techniques for temporal consistency aim to eradicate flickering completely but typically disregard aesthetic aspects. For stylization tasks, however, consistency control is an essential requirement as a certain amount of flickering adds to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that stylizes video streams in real‐time at full HD resolutions while providing interactive consistency control. We develop a lite optical‐flow network that operates at 80 Frames per second (FPS) on desktop systems with sufficient accuracy. Further, we employ an adaptive combination of local and global consistency features and enable interactive selection between them. Objective and subjective evaluations demonstrate that our method is superior to state‐of‐the‐art video consistency approaches. maxreimann.github.io/stream‐consistency [ABSTRACT FROM AUTHOR]

This study proposes a recommendation system for online video resources in interactive networks to enhance reliable recommendations of tourist educational resources. The video resource recommendation algorithm uses the interactive network hardware to support the platform layer. This layer provides the network communication environment for tourism teaching by realizing information transmission. The data layer uniformly manages the online video resources and user data of tourism teaching. The network tool layer provides various tool units. The application layer invokes various tool units from the network tool layer to develop the recommendation unit of online video resources. Furthermore, an improved mixed latent factor model is designed to recommend high-quality video resources to users by combining video resources and user data in the data layer in this paper. Then, the recommendation result of video resources is presented in the form of a personalized interactive interface. Experimental results show that the proposed algorithm can effectively recommend video resources to users. Meanwhile, the popularity of video resource recommendations is low, which means the novelty degree of recommendation is high. Finally, the experimental results shows that the proposed algorithm has a better interactive performance of video resource recommendation. [ABSTRACT FROM AUTHOR]

Visual feature extraction is the key to continuous sign language recognition (CSLR). However, current CSLR methods based on single-branch networks only rely on intra-video correlation learning to facilitate visual feature optimization. Obviously, this is not conducive to obtaining more robust visual feature representations. Hence, we pioneered a novel CSLR method via intra- and inter-video correlation learning with a two-branch network, named TB-Net. TB-Net explicitly establishes intra-video correlation between glosses and the most relevant video clips at each branch and then introduces inter-video correlation to enhance visual feature extraction at the branch confluence. Specifically, we introduce a contrastive learning-based inter-video correlation module (IEM), which co-optimizes visual features from both branches by designing inter- and intra-video losses to enhance their generalization. In addition, we propose an intra-video correlation module (IAM) based on a gloss-guided attention feature generator to adaptively build mappings between glosses and video clips, which in turn contributes to the acquisition of preliminary gloss-guided visual features within a single video. Extensive experiments on four public CSLR benchmarks show the superior performance of our method. • We pioneer a CSLR method via intra- and inter-video correlation learning. • IAM directs gloss features to visual features through a cross-attention matrix. • IEM interactive optimisation of inter-video features. [ABSTRACT FROM AUTHOR]

Human-machine interactive systems show increasing demand for analysing fast moving objects in high-frame-rate videos. Robust foreground detection, which is able to reduce large amount of redundant background data from high-frame-rate video, becomes the essence to achieve ultra-high-speed human-machine interactions. This paper proposes a local spatial propagation based background model generation, a local linear illumination correction based background model update, and a regional central coordinates and edge keypoints constrained foreground region reselection. The three proposals make up a robust and hardware-friendly foreground detection method. Experimental results prove that the proposed hardware-friendly algorithm achieves high accuracy and robustness on various kinds of challenging cases. Meanwhile, the hardware implementation utilizes little hardware resources and achieves realtime processing of high-frame-rate (784 frame/second) video with the delay less than 1 ms/frame in image processing core. In addition, a practical system is implemented by combing a PC, a high-speed camera and a field programmable gate array (FPGA) for realworld applications. This work will significatively promote the development and application of high-speed human machine interaction. A demo of the proposed vision system working at 784 FPS is available at https://wcms.waseda.jp/em/5f84f75136a6. Note to Practitioners—This paper was motivated by the problem of high-frame-rate video contains large amount of redundant background pixels which makes ultra-high-speed human-machine interactions inaccessible. Existing approaches are mainly focused on designing complex background models, but processing speed, which is the most important issue for ultra-high-speed human-machine interactions, has received relatively little attention. This paper suggests a robust and hardware-friendly foreground detection algorithm which has been implemented as a hardware system by using an FPGA, a high-frame-rate camera, and a PC. We show that the hardware implementation utilizes less hardware resources and achieves real-time processing speed of 784 FPS with the delay less than 1 ms/frame in the image processing core. This work is a pioneering attempt of ultra-high-speed foreground detection, which will significatively speed up the wide applications of ultra-high-speed human machine interactions. [ABSTRACT FROM AUTHOR]

With IT and video technologies evolving rapidly, video services with user interaction emerge, and bring about attracting experiences. The growing bandwidth and latency demands call for advanced transmission techniques which utilize wired, wireless, and 5G cellular network, and cooperate unicast, multicast, and broadcast. Free-viewpoint video, as one of the interactive video applications, can provide users with immersion of real world scenes, and shows potential to be an essential application in future digital life. However, such an application is different from other video services in aspects of computation and communication features, and existing researches are not up to the low frame latency, smooth playback, and high service scalability challenges. In this paper, we target on the above problems, and achieve ultra-low latency and stable video transmission for free-viewpoint video services by improving the performance of multiple system components accordingly. Firstly, the key performance indicator is formulated for such services, and an edge computing based video transmission schema is proposed to achieve ultra-low frame latency. Secondly, a pair of stabilization algorithms at both server and client sides based on application features are introduced to ensure stable encoded video streams before transmission, and smooth playback after that. Thirdly, a cloud-edge-client hybrid free-viewpoint service architecture is proposed, which can provide hierarchical experiences and enlarge the service scale with restricted resources. Experimental results prove the effectiveness of the proposed transmission schema and scheduling algorithms, and feasibility of the proposed service architecture is revealed by qualitative analyses. [ABSTRACT FROM AUTHOR]

360° video has been becoming one of the major media in recent years, providing immersive experience for viewers with more interactions compared with traditional videos. Most of today's implementations rely on bulky Head-Mounted Displays (HMDs) or require touch screen operations for interactive display, which are not only expensive but also inconvenient for viewers. In this paper, we demonstrate that interactive 360° video streaming can be done with hints from gaze movement detected by the front camera of today's mobile devices (e.g., a smartphone). We design a lightweight real-time gaze point tracking method for this purpose. We integrate it with streaming module and apply a dynamic margin adaption algorithm to minimize the overall energy consumption for battery-constrained mobile devices. Our experiments on state-of-the-art smartphones show the feasibility of our solution and its energy efficiency toward cost-effective real-time 360° video streaming. [ABSTRACT FROM AUTHOR]

This paper is about recognizing multiple person actions occurring in videos, including individual actions, interactions, and group activities. In an environment, multiple people perform group actions such as walking in groups and talking by facing each other. The model develops by retrieving individual person action from video sequences by representing interactive contextual features among multiple people. The novelty of the proposed framework is the development of interactive action context descriptors (IAC) and classifying group activities using Machine Learning. Each individual person and other nearby people’s relative action score are encoded by IAC in the video frame. Individual person action descriptors are important clues for recognition of multiple person activity by developing interaction context. An action retrieval technique was formulated based on KNN for individual action classification scores. This model also introduces Fully Connected Conditional Random Field (FCCRF) to learn interaction context information among multiple people. FCCRF regularizes activity categorization by the spatial-temporal model. This paper also presents threshold processing to improve the performance of context descriptors. The experimental results compared to state-of-the-art approaches and demonstrated improvement in performance for group activity recognition. [ABSTRACT FROM AUTHOR]

Students in introductory college physics often have wide ranges of preparation, making it challenging to deliver effective instruction for all. While numerous educational products and strategies have been shown to be broadly effective, there is a need for research-based online materials that support personalized learning and focus specifically on improving problem-solving abilities. Building from our Interactive Video Vignettes (IVVs), we are developing freely available Interactive Video-Enhanced Tutorials (IVETs). These combine videos of real instructors who guide students through an expert-like problem-solving process using a series of required questions that motivate students to interact with the material. Research on the effectiveness of the IVETs is underway and preliminary results are encouraging. [ABSTRACT FROM AUTHOR]

Aiming at the problems that the existing video captioning models pay attention to incomplete information and the generation of expression text is not accurate enough, a video captioning model that integrates image, audio, and motion optical flow is proposed. A variety of large-scale dataset pretraining models are used to extract video frame features, motion information, audio features, and video sequence features. An embedded layer structure based on self-attention mechanism is designed to embed single-mode features and learn single-mode feature parameters. Then, two schemes of joint representation and cooperative representation are used to fuse the multimodal features of the feature vectors output by the embedded layer, so that the model can pay attention to different targets in the video and their interactive relationships, which effectively improves the performance of the video captioning model. The experiment is carried out on large datasets MSR-VTT and LSMDC. Under the metrics BLEU4, METEOR, ROUGEL, and CIDEr, the MSR-VTT benchmark dataset obtained scores of 0.443, 0.327, 0.619, and 0.521, respectively. The result shows that the proposed method can effectively improve the performance of the video captioning model, and the evaluation indexes are improved compared with comparison models. [ABSTRACT FROM AUTHOR]

A perennial problem for teachers of political thought is to decide what thinkers to include in the required course readings. In many cases, teachers have come to rely on an established Western canon as they seek to build a shared disciplinary identity, impart key theoretical insights and provide common points of reference. Increasingly, however, calls have been made to include more non-Western, female and otherwise marginalised voices. In response, this article presents and evaluates an interactive group video assignment by which the students are asked to identify one, hitherto excluded, political thinker and formulate arguments for his or her inclusion in the course readings. As a collaborative exercise, higher-level comprehension, analysis and synthesis are encouraged while the shared 'canon' of political thought is widened. [ABSTRACT FROM AUTHOR]

Learning to generate continuous linguistic descriptions for multi-subject interactive videos in great details has particular applications in team sports auto-narrative. In contrast to traditional video caption, this task is more challenging as it requires simultaneous modeling of fine-grained individual actions, uncovering of spatio-temporal dependency structures of frequent group interactions, and then accurate mapping of these complex interaction details into long and detailed commentary. To explicitly address these challenges, we propose a novel framework Graph-based Learning for Multi-Granularity Interaction Representation (GLMGIR) for fine-grained team sports auto-narrative task. A multi-granular interaction modeling module is proposed to extract among-subjects’ interactive actions in a progressive way for encoding both intra- and inter-team interactions. Based on the above multi-granular representations, a multi-granular attention module is developed to consider action/event descriptions of multiple spatio-temporal resolutions. Both modules are integrated seamlessly and work in a collaborative way to generate the final narrative. In the meantime, to facilitate reproducible research, we collect a new video dataset from YouTube.com called Sports Video Narrative dataset (SVN). It is a novel direction as it contains $6K$ 6 K team sports videos (i.e., NBA basketball games) with $10K$ 10 K ground-truth narratives(e.g., sentences). Furthermore, as previous metrics such as METEOR (i.e., used in coarse-grained video caption task) DO NOT cope with fine-grained sports narrative task well, we hence develop a novel evaluation metric named Fine-grained Captioning Evaluation (FCE), which measures how accurate the generated linguistic description reflects fine-grained action details as well as the overall spatio-temporal interactional structure. Extensive experiments on our SVN dataset have demonstrated the effectiveness of the proposed framework for fine-grained team sports video auto-narrative. [ABSTRACT FROM AUTHOR]

The interactive mode in e-learning can effectively stimulate students' learning motivation, especially the way of video questioning as the mainstream interactive mode of e-learning video, which is conducive to improve students' enthusiasm to participate in learning and enhance learning thinking. This study constructs a theoretical model of the impact of E-learning behavior on Students' learning effect, and analyzes the impact of video bullet problem as an intermediary variable on E-learning behavior on students' learning effect. The results show that the overall Cronbach of the questionnaire α the coefficient is 0.883 and Kaiser -- Meyer -- Olkin value is 0.822, indicating that the questionnaire has good reliability and validity. The research results of this study provide reference suggestions for exploring the main influencing factors of learning behavior, improving the effect of students' online learning, revealing the optimization strategy of video bullet problem feedback under the network teaching environment, and the production of bullet problem interactive video [ABSTRACT FROM AUTHOR]

We propose a novel and unified solution for user-guided video object segmentation tasks. In this work, we consider two scenarios of user-guided segmentation: semi-supervised and interactive segmentation. Due to the nature of the problem, available cues – video frame(s) with object masks (or scribbles) – become richer with the intermediate predictions (or additional user inputs). However, the existing methods make it impossible to fully exploit this rich source of information. We resolve the issue by leveraging memory networks and learning to read relevant information from all available sources. In the semi-supervised scenario, the previous frames with object masks form an external memory, and the current frame as the query is segmented using the information in the memory. Similarly, to work with user interactions, the frames that are given user inputs form the memory that guides segmentation. Internally, the query and the memory are densely matched in the feature space, covering all the space-time pixel locations in a feed-forward fashion. The abundant use of the guidance information allows us to better handle challenges such as appearance changes and occlusions. We validate our method on the latest benchmark sets and achieve state-of-the-art performance along with a fast runtime. [ABSTRACT FROM AUTHOR]

The paper presents educational videos as an interactive method of teaching nursing and midwifery students. As a part of the methods of online education, educational videos transform the learning content into a practical necessity for the formation ofstudents' professional competencies in the considered medical specialties. Correlation analysis identifies the degree of coherence between the observed features: "training with video films" and "professional competencies formation". Using 𝝌𝟐 statistics, it is decided whether the relationship between two nominal variables is statistically significant and how the strength of the relationship i.e., the extent of the effect, is valued using the coefficients Phi and Cramer’s V. The statistical processing of the performed didactic experiment proves a "strong" correlation between the studied features. [ABSTRACT FROM AUTHOR]

Whether to embrace "flipped" pedagogy or to problem solve during a pandemic, many teachers utilize video to deliver instruction to students. Watching a video can be a passive activity without a person or a tool to facilitate active engagement. As a result, many teachers find the need to look for tools, methods, and platforms to engage learners, add elements of interactivity, and incorporate effective instructional practices into their video lessons. In this manuscript, we describe how a specific technology, Edpuzzle, can help facilitate video lessons that include explicit instruction elements such as eliciting frequent responses, offering supported practice, providing immediate affirmative and corrective feedback, and monitoring student performance. When paired with well-designed videos, teachers can use this tool to deliver effective instruction in an asynchronous environment. [ABSTRACT FROM AUTHOR]

Music Gesture can be thought of as being made up of dynamic, multisensorial lines. From this, the author draws from Ingold's "correspondence of lines" as a conception of music and a transformative compositional process, informing gestural line expressions and the development of notation systems for the body. The author outlines the technology he has developed for two compositions, involving a video scoring methodology and software that enables interactive video gestural sampling in a collaborative art music context. The author also engages with Ingold's concept of the "mesh," as lines that overflow, and imagines how the mesh could be perceived through sensory modalities. This discourse of lines is contextualized through examples of absurdist instrumental pieces and corporeally based music. [ABSTRACT FROM AUTHOR]

Museums employ a diverse range of display models. The most common forms are 'showcases' and 'multimedia displays', which include video installations and the use of interactive tablets. This poses the question of how the learning outcomes differ between showcase and multimedia displays. Focusing on 308 adult visitors across four typologies, this research explores how the learning outcomes differ when similar information is displayed using showcase and multimedia (video installation and interactive tablet) approaches in two exhibitions. Participants were randomly divided into three groups: namely, the showcase, video, and interactive groups. Interviews, questionnaires, and follow-up interviews were conducted to determine the learning outcomes. Results show statistically different learning outcomes between showcase and video installation, but no significant difference between showcase and interactive tablets. Moreover, results confirm a correlation between visitor gender and learning outcome, as well as visitor gender and typologies, but no evident correlation between learning outcomes and visitor typologies. Besides, this study explores the interaction impact on learning outcomes of 120 visitors who watched both showcase and multimedia and results show a correlation between visitors' learning outcomes and different display models (showcase, multimedia and double-channel display). [ABSTRACT FROM AUTHOR]

Purpose: By integrating the evidence of various audience theories, a comparison is made of viewers' information processing of new interactive video advertising formats (skippable video ads) with traditional, non-skippable formats. The purpose of this paper is to focus on how exposure to a brand name at different moments during online video advertisements influences viewers' recall. Design/methodology/approach: A neuroscientific pre-test supported the selection of an arousing video, and a subsequent pre-test confirmed the scenario validity. Using a broader sample, the main study applied a 3×2 experimental design of different ad designs and formats. Findings: Distinctions should be made between formats and audiences that lead to different information processing methods. Users exposed to skippable ads (active audiences) exert a higher control over advertising and tend to process the information presented in the initial part of an ad. In turn, users viewing non-skippable ads (passive audiences) should be exposed to the high arousal stimulus before presentation of the key information that the advertiser wants them to remember. Practical implications: Advertising managers and content creators can use these results to improve the design of their online videos, and to achieve greater effectiveness in terms of information recall. Originality/value: Skippable online videos have become a commonly used advertising format, but research into their effects is scarce. This study offers the first comparison of viewers' reactions towards skippable vs non-skippable formats, in terms of brand recall and economic performance. Further research might extend this underdeveloped field of study and enhance the knowledge of video format processing in social media. [ABSTRACT FROM AUTHOR]

Videos have long been used in the classroom to enhance learning. However, traditionalaged students (ages 18 to 21) have so much interactive video exposure that opportunities to actively engage them with the material continue to exist. There are multiple ways to use video to enhance student learning. Four interactive pedagogical methods of using film in the classroom with are presented along with student feedback for each. The first and second methods involve having students find video examples of class concepts; the third method asks students to provide video examples of related concepts from supplemental articles and class; and in the last method, students actually make videos demonstrating class concepts. These all require that students learn, practice, and persist in efforts which reinforce soft skills desired by employers of today's graduates. [ABSTRACT FROM AUTHOR]

This article proposes the use of teaching units in Natural Sciences to promote the development of scientific competencies in the use of scientific language, explanation of phenomena, and inquiry, as established for the fourth grade of elementary school in the E. E. Marco Fidel Suárez in Medellín (in the vicinity of the Cuarta Brigada headquarters). It is a case report in which a pretest and a post-test were applied at the respective moments of the teachinglearning process. Digital educational resources were included, such as blogs, videos and interactive games, which increased the motivation of students to deal with the educational processes of their scientific competences.; in addition, they were given the chance to increase their capacities and desire to investigate, to learn from the interrelationship between the living beings, to develop curiosity for the exploration and analysis of natural phenomena, and to propose alternatives that help improve both their current living conditions and their environment. [ABSTRACT FROM AUTHOR]

The presented paper is about the evaluation of products in digital styling formats, such as tutorials, hauls and tops&flops. Therein, we provide an approach to analyze specific para-interactive processes specifically addressing a teenage audience. Semiotic as well as interactional approaches are considered to demonstrate, based on video material, how YouTube influencers use multimodal potentials of various text types para-interactively to integrate their (female) recipients into the video itself. This phenomenon leads to the fact, that the audience may not only share the perception of presented products, but also, the influencers' evaluation. [ABSTRACT FROM AUTHOR]

In numerous video segmentation algorithms, shape and color priors from previous frames are propagated to successive frames for processing. One prime issue of the existing algorithms is how priors are modeled and propagated effectively. This paper proposes a novel algorithm for accurate and robust foreground prediction via a local appearance model based on shape and color. In the shape estimation process, instead of performing global matching, a local search mechanism is developed to capture complex motions. In addition, global information is used to facilitate local matching for the final shape estimation. Color cues from multiple frames are used to estimate the foreground (background) pixel distribution in the current image. Furthermore, the contribution of each frame is weighted based on its reliability and discriminative strength. Based on the proposed local appearance model, a graph cut algorithm is used to generate segmentation results. The experimental results show that accurate video segmentation can be obtained by the proposed algorithm with fewer user interactions. [ABSTRACT FROM PUBLISHER]

This article has been retracted by Hindawi following an investigation undertaken by the publisher [[1]]. We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. [Extracted from the article]

Traditional image and video enhancement techniques do not consider the subjective preferences of the user, who might be interested in modifying or highlighting specific elements of a scene. In fact, most image or video post-processing techniques are applied to the entire image or frame. In this work, a framework for selective video enhancement is presented. It is based on an adaptive multi-resolution edge enhancement technique performed in the Laguerre–Gauss complex wavelet domain. In the proposed scheme, edges are selectively enhanced or attenuated according to the inputs of the user, taking into account the characteristics of the content, the response of the human visual system, and the masking effects induced by textured background. Two scenarios have been implemented and tested: (i) an interactive video editing system, where the user selects the objects to enhance through a graphical interface and the system automatically propagates the selection through the following video frames belonging to the same shot, (ii) a perceptually-driven technique to improve the quality of RGB plus Depth videos, which uses depth maps as additional features to guide the enhancement. • A framework for selective video enhancement is presented. • Multi-resolution edge enhancement is implemented in the Laguerre–Gauss domain. • Interactive object-based editing and automatic RGB-D enhancement are demonstrated. [ABSTRACT FROM AUTHOR]

One way to reduce bandwidth requirement in video-on-demand system is using periodic broadcasting for popular videos. It periodically broadcasts video segments on several channels and clients need to wait some time to access the video. Many periodic broadcasting schemes have been proposed to reduce server’s bandwidth and client’s waiting time. However, most of these schemes assume all clients have enough reception bandwidth which is, in fact, different in various network environment. To solve this problem, two types of broadcasting schemes have been proposed: 1) reception bandwidth adaptable (RBA) and 2) reception bandwidth limited (RBL) approaches. RBA includes HeRO, BroadCatch, CAR, FBR, and MBC. RBL contains CCA, CCA+, CCA++, and GDB. In this paper, we propose a novel RBL approach called delay receiving (DeRe) to accomodate the heterogeneous receiving problem. By limiting client’s reception bandwidth to two or three channels, we achieve relatively low service latency for all users compared to other schemes. Also, DeRe scheme is allowed to adjust buffer requirement directly, which is a unique property among all broadcasting schemes. The performance evaluation results of DeRe scheme with buffer requirement of 15% and 20% of video size are presented in this paper. [ABSTRACT FROM PUBLISHER]

Recorded lectures have rapidly gained momentum in teaching and learning, whether online or "offline" in traditional face-to-face classrooms. We appreciate the remarkable accessibility and flexibility brought by recorded lectures, but we must ponder its adequacy and effectiveness. After all, a YouTube recording is different from a live concert; and video watching cannot substitute active learning. Recording absorption does not necessarily mean critical thinking and conceptual understanding. Using the tools for producing and sharing interactive video lessons or by strategic design, passive video watching can be readily transformed into active learning experiences. [ABSTRACT FROM AUTHOR]

This paper stems from the increasing role CLIL (Content and Language Integrated Learning) methodology is playing in the Italian schools, having been recently introduced in the upper secondary school curricula. After a brief theoretical framework, the role of videos in a CLIL environment will be dealt with, both for learners and for teachers. In particular some examples of possible use of videos in a CLIL class will be mentioned, also considering the new role of the learner as an active user of the web and as a content generator. As for the teachers' perspective, video clips can play an important role in a teacher-training pathway, both for Initial Teacher Education and for Continuous Professional Development. The LOCIT model (Coyle), inspired by the CIT model (Tripp), will be explored as a possible way to reflect and share ideas and opinions about a CLIL lesson among the community of teachers, practitioners, trainers, educators. [ABSTRACT FROM AUTHOR]

To move beyond the current "talking heads" paradigm and enable truly natural and immersive shared experiences, video-mediated communication systems must be able to understand the context of the shared activity as well as the social layer of interaction. [ABSTRACT FROM PUBLISHER]

The advances in wireless communications and mobile devices have resulted in a massive growth of the video services over mobile networks. In this article, we study the potential of peer-assisted video delivery in WiFi mobile networks aimed at reducing servers? load. Our study is based on a real measurement of mobile video viewing logs from a leading Internet video provider for 14 days. We analyze viewing behavior of users with respect to three main factors for the design of peer-assisted delivery network: viewing time, user population, and user locality. We then discuss the implications of the observations. Finally, we apply the findings on a BitTorrent-like VoD system and perform experiments with the collected viewing logs to demonstrate the benefits of peer-assisted video delivery. The peer assistance can reduce the servers? load by as much as 50 percent for popular videos on average. [ABSTRACT FROM PUBLISHER]

We construct a unified interactive video retargeting system for video summarization, completion, and reshuffling. Our system combines the advantages of both video warping and summarizing processing. We first warp the video to present initial editing results, then refine the results using patch-based summarizing optimization, which mainly eliminates possible distortion produced in the warping step. We develop a Mean Value Coordinate (MVC) warping method due to its simplicity and efficiency used in the initialization. For refining processing, the summarization optimization is built on a 3D bidirectional similarity measure between the original and edited video, to preserve the coherence and completeness of the final editing result. We further improve the quality of summarization by applying a color histogram matching during the optimization, and accelerate the summarization optimization by using a constrained 3D Patch-Match algorithm. Experiment results show that the proposed video retargeting system effectively supports video summarization, completion, and reshuffling while avoiding issues like texture broken, video jittering, and detail losing. [ABSTRACT FROM AUTHOR]

We present an interactive video stylization system for transforming an input video into a painterly animation. The system consists of two phases: a content extraction phase to obtain semantic objects, i.e., recognized content, in a video and establish dense feature correspondences, and a painterly rendering phase to select, place, and propagate brush strokes for stylized animations based on the semantic content and object motions derived from the first phase. Compared with the previous work, the proposed method has the following three advantages. First, we propose a two-pass rendering strategy and brush strokes with mixed colors in order to render expressive visual effects. Second, the brush strokes are warped according to global object deformations, so that the strokes appear to be naturally attached to the object surfaces. Third, we propose a deferred rendering and backward completion method to draw brush strokes on emerging regions and simulate a damped system to reduce stroke scintillation effect. Moreover, we discuss the graphics processing unit-based implementation of our system, which is demonstrated to greatly improve the efficiency of producing stylized videos. In experiments, we verify this system by applying it to a number of video clips to produce expressive oil-painting animations and compare it with the state-of-the-art approaches. [ABSTRACT FROM PUBLISHER]

Error concealment in video communication is becoming increasingly important because of the growing interest in video delivery over unreliable channels such as wireless networks and the Internet. A subclass of this error concealment in video communication is known as motion vector recovery (MVR). MVR techniques try to retrieve the lost motion information in the compressed video streams based on the available information in the locality (both spatial and temporal) of the lost data. The activities and practice in the area of MVRbased error concealment during the last two decades has been mainly elaborated here. A performance comparison of the prominent MVR techniques has also been presented. [ABSTRACT FROM AUTHOR]

This paper describes an approach to exploit the implicit user feedback gathered during interactive video retrieval tasks. We propose a framework, where the video is first indexed according to temporal, textual, and visual features and then implicit user feedback analysis is realized using a graph-based methodology. The generated graph encodes the semantic relations between video segments based on past user interaction and is subsequently used to generate recommendations. Moreover, we combine the visual features and implicit feedback information by training a support vector machine classifier with examples generated from the aforementioned graph in order to optimize the query by visual example search. The proposed framework is evaluated by conducting real-user experiments. The results demonstrate that significant improvement in terms of precision and recall is reported after the exploitation of implicit user feedback, while an improved ranking is presented in most of the evaluated queries by visual example. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we provide a new formulation for video queries as structured combination of concept threads, contributing to the general query-by-concept paradigm. Occupying a low-dimensional region in the concept space, concept thread defines a ranked list of video documents ordered by their combined concept predictions. This localized representation incorporates the previous concept based formulation as a special case and extends the restricted AND concept combination logic to a two-level concept inference network. We apply this new formulation to interactive video retrieval and utilize abundant feedback information to mine the latent semantic concept threads for answering complex query semantics. Simulative experiments which are conducted on two years’ TRECVID data sets with two sets of concept lexicons demonstrate the advantage of the proposed formulation. The proposed query formulation offers some 60% improvements over the simple browsing search baseline in nearly real time. It has clear advantages over c-tf-idf and achieves better results over the state-of-the-art online ordinal reranking approach. Meanwhile, it not only alleviates user’s workload significantly but also is robust to user mislabeling errors. [Copyright &y& Elsevier]

Due to the complexity of H.264/AVC, it is very challenging to apply this standard to design a conversational video communication system. This problem is addressed in this paper by using region-of-interest (ROI) based bit allocation and computational power allocation schemes. In our system, the ROI is first detected by using the direct frame difference and skin-tone information. Several coding parameters including quantization parameter, candidates for mode decision, the number of referencing frames, accuracy of motion vectors and the search range of motion estimation are adaptively adjusted at the macroblock (MB) level according to the relative importance of each MB. Subsequently, the encoder could allocate more resources such as bits and computational power to the ROI, and the decoding complexity is also optimized at the encoder side by utilizing an ROI based rate-distortion-complexity (R-D-C) cost function. The encoder is thus simplified and decoding-friendly, and the overall subjective visual quality can also be improved. [ABSTRACT FROM AUTHOR]

In this paper, a novel pipelined intra prediction scheme with prediction mode scheduling is proposed to design H.264/A VC video encoder for high-definition TV (HDTV) applications such as ND camcorder. Since the intra prediction of the current 4×4 block cannot be performed before the reconstruction of a previous 4×4 block, intra 4×4 prediction cannot be pipdined for high speed implementation. By rearranging the encoding order of intra 4×4 prediction, the proposed scheme can be pipelined while maintaining consistency with the standard. In order to further increase the hardware utilization, the prediction order of the nine intra 4×4 prediction modes is scheduled depending on the required pixels to generate the predicted images. With the proposed scheme, the intra 4×4 prediction can be pipelined, and the bubble between the blocks can be decreased without the performance degradation. Since the processing time is reduced by up to 41%, it can save the hardware resource. Thus, the proposed scheme can be a good solution for real-time ND sized encoding applications. [ABSTRACT FROM AUTHOR]

The Digital Millennium Copyright Act (DMCA) has made ripping Macrovision- or CSS-protected DVDs illegal, but university instructors and students need accessible videos in a digital format for pedagogical purposes. This legal analysis has compared the DMCA with the TEACH Act and the fair use doctrine in the Copyright Act in an attempt to find a viable solution to this dilemma. The study has provided alternative and creative approaches to DVD ripping while finding it necessary to pursue a legal amendment to the DMCA. [ABSTRACT FROM AUTHOR]

Research into practices applied by the Hollywood studios on the DVD market requires analysis of the economic logic of the market and the rhetoric of various paratexts accompanying the movies on DVDs, as well as consideration of practices of the television industry. At stake are not only the principles of economy of scope and the strategy of repurposing content that DVD accomplishes, but also the way the studios use DVD like the TV industry uses the internet for an old media's 'augmentation', to use John Caldwell's term. Additionally, the extras on DVDs create a frame of reception through their exploitation of institutional practices which are familiar to the viewer from television and home video. The movies on DVD are marketed by the Hollywood cinema in a double mode: the DVD offers theatrical spectacularity as well as the intimacy and 'imperfection' of television and home video. DVDs with their high-budgeted franchises provide the best opportunity for combining spectacle, narrative openness and complexity with a sense of intimacy and authenticity. [ABSTRACT FROM AUTHOR]