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"abstract": "In dentistry, radiological examinations help specialists by showing structure of the tooth bones with the goal of screening embedded teeth, bone abnormalities, cysts, tumors, infections, fractures, problems in the temporomandibular regions, just to cite a few. Sometimes, relying solely in the specialist's opinion can bring differences in the diagnoses, which can ultimately hinder the treatment. Although tools for complete automatic diagnosis are no yet expected, image pattern recognition has evolved towards decision support, mainly starting with the detection of teeth and their components in X-ray images. Tooth detection has been object of research during at least the last two decades, mainly relying in threshold and region-based methods. Following a different direction, this paper proposes to explore a deep learning method for instance segmentation of the teeth. To the best of our knowledge, it is the first system that detects and segment each tooth in panoramic X-ray images. It is noteworthy that this image type is the most challenging one to isolate teeth, since it shows other parts of patient's body (e.g., chin, spine and jaws). We propose a segmentation system based on mask region-based convolutional neural network to accomplish an instance segmentation. Performance was thoroughly assessed from a 1500 challenging image data set, with high variation and containing 10 categories of different types of buccal image. By training the proposed system with only 193 images of mouth containing 32 teeth in average, using transfer learning strategies, we achieved 98% of accuracy, 88% of F1-score, 94% of precision, 84% of recall and 99% of specificity over 1224 unseen images, results very superior than other 10 unsupervised methods.",
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"abstract": "Automatic tooth instance segmentation on 3D dental models is a fundamental task for computer-aided orthodontic treatments. Existing learning-based methods rely heavily on expensive point-wise annotations. To alleviate this problem, we are the first to explore a low-cost annotation way for 3D tooth instance segmentation, i.e., labeling all tooth centroids and only a few teeth for each dental model. Regarding the challenge when only weak annotation is provided, we present a dental arch prior-assisted 3D tooth segmentation method, namely DArch. Our DArch consists of two stages, including tooth centroid detection and tooth instance segmentation. Accurately detecting the tooth centroids can help locate the individual tooth, thus benefiting the segmentation. Thus, our DArch proposes to leverage the dental arch prior to assist the detection. Specifically, we firstly propose a coarse-to-fine method to estimate the dental arch, in which the dental arch is initially generated by Bezier curve regression, and then a graph-based convolutional network (GCN) is trained to refine it. With the estimated dental arch, we then propose a novel Arch-aware Point Sampling (APS) method to assist the tooth centroid proposal generation. Meantime, a segmentor is independently trained using a patch-based training strategy, aiming to segment a tooth instance from a 3D patch centered at the tooth centroid. Experimental results on 4, 773 dental models have shown our DArch can accurately segment each tooth of a dental model, and its performance is superior to the state-of-the-art methods.",
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"content": "Automatic tooth instance segmentation on 3D dental models is a fundamental task for computer-aided orthodontic treatments. Existing learning-based methods rely heavily on expensive point-wise annotations. To alleviate this problem, we are the first to explore a low-cost annotation way for 3D tooth instance segmentation, i.e., labeling all tooth centroids and only a few teeth for each dental model. Regarding the challenge when only weak annotation is provided, we present a dental arch prior-assisted 3D tooth segmentation method, namely DArch. Our DArch consists of two stages, including tooth centroid detection and tooth instance segmentation. Accurately detecting the tooth centroids can help locate the individual tooth, thus benefiting the segmentation. Thus, our DArch proposes to leverage the dental arch prior to assist the detection. Specifically, we firstly propose a coarse-to-fine method to estimate the dental arch, in which the dental arch is initially generated by Bezier curve regression, and then a graph-based convolutional network (GCN) is trained to refine it. With the estimated dental arch, we then propose a novel Arch-aware Point Sampling (APS) method to assist the tooth centroid proposal generation. Meantime, a segmentor is independently trained using a patch-based training strategy, aiming to segment a tooth instance from a 3D patch centered at the tooth centroid. Experimental results on 4, 773 dental models have shown our DArch can accurately segment each tooth of a dental model, and its performance is superior to the state-of-the-art methods.",
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"abstract": "Individual tooth segmentation in Cone-beam Computed Tomography (CBCT) is an essential procedure for the quantitative analysis of tooth function in patients with oral diseases. Though some automatic methods have been proposed with high segmentation accuracy, the overlapping tooth crown, complex shape variability of the tooth, and often inadequate signal-to-noise ratio pose serious challenges to such methods. In this study, we present a novel automatic individual tooth segmentation approach for CBCT based on multi-task convolutional neural networks (CNNs) and watershed transform. In the first stage, we utilize the multi-task CNN to segment the foreground and center region of all teeth from CBCT, in which the central regions are viewed as the landmark of the tooth for distinguishing different teeth. In the second stage, we input the predicted tooth foreground and landmark results into the marker-controlled watershed transform method to segment the overlapping tooth in the tooth foreground result by assigning each individual tooth with a unique label. In addition, we propose a 3D tooth merge strategy to improve the structural integrity of individual tooth segmentation results. The qualitative and quantitative results demonstrate that our approach achieves high accuracy and stability in terms of individual tooth segmentation for CBCT.",
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"content": "Individual tooth segmentation in Cone-beam Computed Tomography (CBCT) is an essential procedure for the quantitative analysis of tooth function in patients with oral diseases. Though some automatic methods have been proposed with high segmentation accuracy, the overlapping tooth crown, complex shape variability of the tooth, and often inadequate signal-to-noise ratio pose serious challenges to such methods. In this study, we present a novel automatic individual tooth segmentation approach for CBCT based on multi-task convolutional neural networks (CNNs) and watershed transform. In the first stage, we utilize the multi-task CNN to segment the foreground and center region of all teeth from CBCT, in which the central regions are viewed as the landmark of the tooth for distinguishing different teeth. In the second stage, we input the predicted tooth foreground and landmark results into the marker-controlled watershed transform method to segment the overlapping tooth in the tooth foreground result by assigning each individual tooth with a unique label. In addition, we propose a 3D tooth merge strategy to improve the structural integrity of individual tooth segmentation results. The qualitative and quantitative results demonstrate that our approach achieves high accuracy and stability in terms of individual tooth segmentation for CBCT.",
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"abstract": "The performance of image retrieval has been improved tremendously in recent years through the use of deep feature representations. Most existing methods, however, aim to retrieve images that are visually similar or semantically relevant to the query, irrespective of spatial configuration. In this paper, we develop a spatial-semantic image search technology that enables users to search for images with both semantic and spatial constraints by manipulating concept text-boxes on a 2D query canvas. We train a convolutional neural network to synthesize appropriate visual features that captures the spatial-semantic constraints from the user canvas query. We directly optimize the retrieval performance of the visual features when training our deep neural network. These visual features then are used to retrieve images that are both spatially and semantically relevant to the user query. The experiments on large-scale datasets such as MS-COCO and Visual Genome show that our method outperforms other baseline and state-of-the-art methods in spatial-semantic image search.",
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"abstract": "In this paper, a new subdivision scheme for Polar embedded Catmull-Clark mesh structure is presented. The ripple effect commonly found at high-valence extraordinary points of a CCS surface is improved by replacing high-valence CCS extraordinary faces with triangular Polar faces. The new scheme is valence independent and is stationary. By using the same subdivision mask on both the quadrilateral part and the triangular part, artifacts that have been noticed before (mismatching subdivision masks, exponentially increased Valence at Polar extraordinary points by the recursive subdivision process) are resolved. Test results show that with the new scheme, one can generate very high quality, curvature continuous subdivision surfaces on the triangular part. Combined with current CCS G2 schemes, one can generate high quality subdivision surfaces appropriate for most engineering applications on any Polar embedded Catmull-Clark control meshes.",
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"abstract": "Despite various debugging supports of the existing IDEs for programming errors and exceptions, software developers often look at web for working solutions or any up-to-date information. Traditional web search does not consider the context of the problems that they search solutions for, and thus it often does not help much in problem solving. In this paper, we propose a context-aware meta search tool, Surf Clipse, that analyzes an encountered exception and its context in the IDE, and recommends not only suitable search queries but also relevant web pages for the exception (and its context). The tool collects results from three popular search engines and a programming Q & A site against the exception in the IDE, refines the results for relevance against the context of the exception, and then ranks them before recommendation. It provides two working modes-interactive and proactive to meet the versatile needs of the developers, and one can browse the result pages using a customized embedded browser provided by the tool. Tool page: www.usask.ca/masud.rahman/surfclipse.",
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"abstract": "This paper addresses the problem of extracting perceptually dominant color names of images. Our approach is motivated by the principle that the pixels corresponding to one dominant color name identified by human are often context dependent, spatially connected and form a perceptually meaningful region. Our algorithm first learns the probabilistic mapping from a RGB color to a color name. Then, a double-threshold approach is utilized to determine the color name of a RGB pixel in a specific image by considering its neighboring pixels. This scheme effectively deals with the pixels ambiguously belonging to several dominant color names. Last, the saliency information is combined to extract perceptually dominant colors. Experiments on our labeled image data set and the Ebay image set demonstrate the effectiveness of our approach.",
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"abstract": "We study practical approaches to solving the token swapping (TSWAP) problem optimally in this paper. In TSWAP, we are given an undirected graph with colored vertices. A colored token is placed in each vertex. A pair of tokens can be swapped between adjacent vertices. The goal is to perform a sequence of swaps so that token and vertex colors agree across the graph. The minimum number of swaps is required in the optimization variant of the problem. We observed similarities between the TSWAP problem and multi-agent path finding (MAPF) where instead of tokens we have multiple agents that need to be moved from their current vertices to given unique target vertices. The difference between both problems consists in local conditions that state transitions (swaps/moves) must satisfy. We developed two algorithms for solving TSWAP optimally by adapting two different approaches to MAPF - conflict-based search (CBS) and SAT-based approach that uses multi-value decision diagrams (MDD-SAT). This constitutes the first attempt to design optimal solving algorithms for TSWAP. Experimental evaluation on various types of graphs shows that the reduction to SAT scales better than CBS in optimal TSWAP solving. It has been also demonstrated that TSWAP instances are easier to solve than corresponding similar MAPF instances.",
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"abstract": "This paper presents a novel quad-based remeshing scheme, which can be regarded as a replacement for the triangle-based improvement step of an existing image-based mesh generation technique called Imesh. This remeshing scheme makes it possible for the algorithm to generate good quality quadrilateral meshes directly from imaging data. The extended algorithm combines two ingredients: (1) a template-based triangulation-to-quadrangulation conversion strategy and (2) an optimization-based smoothing procedure. Examples of meshes generated by the extended algorithm, and an evaluation of the quality of those meshes are presented as well.",
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"abstract": "In this paper, Aloha accident consequence simulation software was used to simulate the accident consequence of liquid chlorine tank leakage. The influence of wind speed, leakage aperture, ambient temperature and humidity on the hazard range of liquid chlorine leakage was discussed. The results showed that when the temperature increases, the cloud coverage decreases, the area of leakage hole increases, the height of leakage hole decreases and the roughness of diffusion terrain decreases, the dangerous diffusion area of liquid chlorine leakage will increase, otherwise it will decrease. When the wind speed is 1-3m / s, the dangerous concentration area of liquid chlorine leakage increases with the increase of wind speed, while when the wind speed is 3-10m / s, the dangerous concentration area of liquid chlorine leakage decreases with the increase of wind speed.",
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"title": "Text Visualization - What Colors Tell About a Text",
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"abstract": "This article introduces an approach to text visualization by colors. I suggest a color code that displays word classes: noun (black), verb (red), adjective (green), determiner (grey), particle (brown), conjunction (blue), and interjection (yellow). The colored words provide some details about 1. text genre, 2. sentence structure, and 3. writing style. Samples with fictional narratives and scientific articles (both in German) show that fictional texts have a brighter color pattern compared to scientific texts. The color pattern might be related to the hidden sound or melody of a text. Therefore, text visualization could be useful both as a future method to analyze different text genres and as a method helping laypersons getting a better feeling for texts, identifying troublesome phrases, and perhaps improving their writing style.",
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"abstract": "Over the past decade the field of robotic soccer has been the focus of intensive research. This paper proposes an efficient planning strategy for robotic soccer using case based reasoning (CBRRoboSoc) to build prototypes for a team of two soccer playing robots in the RoboCup small size league. The proposed CBRRoboSoc system develops an efficient strategy to plan both offense and defense team behavior. The system along with its field simulation, prediction and command generation phases, employing Bayesian classifier, help the robots to plan individual moves, recognize game states, as well as to model the world of the playing field. Our model supports game play selection in key game situations, which should in turn significantly advantage the team. Further more by incorporating case base for offense team, simulation of four keep away trials illustrate optimized offense behavior.",
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"content": "Over the past decade the field of robotic soccer has been the focus of intensive research. This paper proposes an efficient planning strategy for robotic soccer using case based reasoning (CBRRoboSoc) to build prototypes for a team of two soccer playing robots in the RoboCup small size league. The proposed CBRRoboSoc system develops an efficient strategy to plan both offense and defense team behavior. The system along with its field simulation, prediction and command generation phases, employing Bayesian classifier, help the robots to plan individual moves, recognize game states, as well as to model the world of the playing field. Our model supports game play selection in key game situations, which should in turn significantly advantage the team. Further more by incorporating case base for offense team, simulation of four keep away trials illustrate optimized offense behavior.",
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"abstract": "Although the collection of player and ball tracking data is fast becoming the norm in professional sports, large-scale mining of such spatiotemporal data has yet to surface. In this paper, given an entire season's worth of player and ball tracking data from a professional soccer league (≈400,000,000 data points), we present a method which can conduct both individual player and team analysis. Due to the dynamic, continuous and multi-player nature of team sports like soccer, a major issue is aligning player positions over time. We present a \"role-based\" representation that dynamically updates each player's relative role at each frame and demonstrate how this captures the short-term context to enable both individual player and team analysis. We discover role directly from data by utilizing a minimum entropy data partitioning method and show how this can be used to accurately detect and visualize formations, as well as analyze individual player behavior.",
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"abstract": "For professional soccer clubs, performance and video analysis are an integral part of the preparation and post-processing of games. Coaches, scouts, and video analysts extract information about strengths and weaknesses of their team as well as opponents by manually analyzing video recordings of past games. Since video recordings are an unstructured data source, it is a complex and time-intensive task to find specific game situations and identify similar patterns.In this paper, we present a novel approach to detect patterns and situations (e.g., playmaking and ball passing of midfielders) based on trajectory data. The application uses the metaphor of a tactic board to offer a graphical query language. With this interactive tactic board, the user can model a game situation or mark a specific situation in the video recording for which all matching occurrences in various games are immediately displayed, and the user can directly jump to the corresponding game scene. Through the additional visualization of key performance indicators (e.g., the physical load of the players), the user can get a better overall assessment of situations. With the capabilities to find specific game situations and complex patterns in video recordings, the interactive tactic board serves as a useful tool to improve the video analysis process of professional sports teams.",
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"normalizedAbstract": "Soccer is one of the most successful sports of our times, and for various reasons. One such reason is its level of unpredictability: surprising results can occur, and it is still unclear what are the secrets that make a team winning or losing within a single game. In this paper we tackle the problem of soccer analysis from a different perspective: we transform every team, together with the playfield, into a network structure (the Team Brain), and we shift perspective, turning soccer games into battles of minds. We show how this approach proves to be successful, allowing to better understand what the recipes for the success of a team can be, and also getting a new, somehow surprising, view of what are the really important critical areas of a soccer field.",
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"abstract": "Low neuroticism and high extraversion seem to be predictors for people performing well during and after extremely stressful circumstances. Here we investigate whether these personality factors are linked to stress sensitivity. Stress was induced through negative feedback on gaming performance and stress sensitivity was determined as the difference in baseline physiological variables (skin conductance, heart rate and heart rate variability) before and after the game, as well as this difference in subjectively reported stress. Our second research question was whether neuroticism and extraversion are linked to baseline physiology. While physiological results suggested that the game had indeed induced stress, subjective reports did not. Perhaps due to this weak experienced stress, we did not find correlations between stress sensitivity and personality. For the baseline measurements, we did find the expected correlations between extraversion and both heart rate and heart rate variability (negative and positive respectively). The negative correlation between neuroticism and skin conductance was opposite of what we expected. While the exact mechanisms are not clear yet, our study suggests that for healthy individuals, there are measurable links between physiology and personality, not only for heart rate variability (a variable that is already often used to probe the long lasting state of an individual) but also for heart rate and skin conductance.",
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"content": "Low neuroticism and high extraversion seem to be predictors for people performing well during and after extremely stressful circumstances. Here we investigate whether these personality factors are linked to stress sensitivity. Stress was induced through negative feedback on gaming performance and stress sensitivity was determined as the difference in baseline physiological variables (skin conductance, heart rate and heart rate variability) before and after the game, as well as this difference in subjectively reported stress. Our second research question was whether neuroticism and extraversion are linked to baseline physiology. While physiological results suggested that the game had indeed induced stress, subjective reports did not. Perhaps due to this weak experienced stress, we did not find correlations between stress sensitivity and personality. For the baseline measurements, we did find the expected correlations between extraversion and both heart rate and heart rate variability (negative and positive respectively). The negative correlation between neuroticism and skin conductance was opposite of what we expected. While the exact mechanisms are not clear yet, our study suggests that for healthy individuals, there are measurable links between physiology and personality, not only for heart rate variability (a variable that is already often used to probe the long lasting state of an individual) but also for heart rate and skin conductance.",
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"abstract": "In this paper we profile the stress responses of patients diagnosed with post-traumatic stress disorder (PTSD) to individual events in the game-based PTSD stress inoculation and exposure virtual environment StartleMart. Thirteen veterans suffering from PTSD play the game while we record their skin conductance. Game logs are used to identify individual events, and continuous decomposition analysis is applied to the skin conductance signals to derive event-related stress responses. The extracted skin conductance features from this analysis are used to profile each individual player in terms of stress response. We observe a large degree of variation across the 13 veterans which further validates the idiosyncratic nature of PTSD physiological manifestations. Further to game data and skin conductance signals we ask PTSD patients to indicate the most stressful event experienced (class-based annotation) and also compare the stress level of all events in a pairwise preference manner (rank-based annotation). We compare the two annotation stress schemes by correlating the self-reports to individual event-based stress manifestations. The self-reports collected through class-based annotation exhibit no correlation to physiological responses, whereas, the pairwise preferences yield significant correlations to all skin conductance features extracted via continuous decomposition analysis. The core findings of the paper suggest that reporting of stress preferences across events yields more reliable data that capture aspects of the stress experienced and that features extracted from skin conductance via continuous decomposition analysis offer appropriate predictors of stress manifestation across PTSD patients.",
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"content": "In this paper we profile the stress responses of patients diagnosed with post-traumatic stress disorder (PTSD) to individual events in the game-based PTSD stress inoculation and exposure virtual environment StartleMart. Thirteen veterans suffering from PTSD play the game while we record their skin conductance. Game logs are used to identify individual events, and continuous decomposition analysis is applied to the skin conductance signals to derive event-related stress responses. The extracted skin conductance features from this analysis are used to profile each individual player in terms of stress response. We observe a large degree of variation across the 13 veterans which further validates the idiosyncratic nature of PTSD physiological manifestations. Further to game data and skin conductance signals we ask PTSD patients to indicate the most stressful event experienced (class-based annotation) and also compare the stress level of all events in a pairwise preference manner (rank-based annotation). We compare the two annotation stress schemes by correlating the self-reports to individual event-based stress manifestations. The self-reports collected through class-based annotation exhibit no correlation to physiological responses, whereas, the pairwise preferences yield significant correlations to all skin conductance features extracted via continuous decomposition analysis. The core findings of the paper suggest that reporting of stress preferences across events yields more reliable data that capture aspects of the stress experienced and that features extracted from skin conductance via continuous decomposition analysis offer appropriate predictors of stress manifestation across PTSD patients.",
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"affiliation": "Center for Computer Games Research, IT University of Copenhagen, Rued Langgaards Vej 7, Copenhagen, Denmark",
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"affiliation": "Institute of Digital Games, University of Malta, Msida MSD 2080, Malta",
"fullName": "Georgios N. Yannakakis",
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"affiliation": "Institute of Digital Games, University of Malta, Msida MSD 2080, Malta",
"fullName": "Hector P. Martinez",
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"affiliation": "Research and Knowledge Centre, Danish Veteran Centre, Garnisonen 1, Ringsted, Denmark",
"fullName": "Karen-Inge Karstoft",
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"abstract": "The Outdoor visual localization can get the position (the spatial location and direction) of the query target through semantic information, three-dimensional point cloud, images, geometric information, and other data [1]. It has been widely used in robot Localization and navigation, autonomous vehicle navigation, augmented reality, 3D reconstruction, SLAM (Simultaneous Localization and Mapping), etc. At present, the most advanced visual localization method is still the traditional localization based on three-dimensional, which has the best localization accuracy. However, due to the 2D-3D matching mechanism, the accuracy of it is seriously reduced in challenging scenes with great scene changes like viewpoint variation, illumination variation. For the problem, in this article, we come up with a model based on the semantic error image which is based on the semantic information of 2D images and 3D point clouds. Based on the original structure-based localization method, we introduce the semantic error image, we use the semantic data of the image sequence to construct the semantic error image based on the semantic information of 2D and 3D points, and utilize these information as a correction signal to eliminate the wrong match and improve the accuracy of localization. We test and compare these methods on two challenging datasets with large scene changes, the experimental results showed that our well method based on semantic error image was significantly better than the method based on 3D structure on the challenging datasets, and the localization accuracy was significantly improved.",
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"content": "The Outdoor visual localization can get the position (the spatial location and direction) of the query target through semantic information, three-dimensional point cloud, images, geometric information, and other data [1]. It has been widely used in robot Localization and navigation, autonomous vehicle navigation, augmented reality, 3D reconstruction, SLAM (Simultaneous Localization and Mapping), etc. At present, the most advanced visual localization method is still the traditional localization based on three-dimensional, which has the best localization accuracy. However, due to the 2D-3D matching mechanism, the accuracy of it is seriously reduced in challenging scenes with great scene changes like viewpoint variation, illumination variation. For the problem, in this article, we come up with a model based on the semantic error image which is based on the semantic information of 2D images and 3D point clouds. Based on the original structure-based localization method, we introduce the semantic error image, we use the semantic data of the image sequence to construct the semantic error image based on the semantic information of 2D and 3D points, and utilize these information as a correction signal to eliminate the wrong match and improve the accuracy of localization. We test and compare these methods on two challenging datasets with large scene changes, the experimental results showed that our well method based on semantic error image was significantly better than the method based on 3D structure on the challenging datasets, and the localization accuracy was significantly improved.",
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"abstract": "Monocular visual simultaneous localization and mapping (SLAM) performs effectively in camera pose estimation and 3D sparse reconstruction of natural scenes. However, in monocular endoscopic environment, serious distortion of the images and the inconstant illumination, even the lack of surface texture, make SLAM-based tracking and 3D dense reconstruction still a challenge. In response to the above problems, it is proposed to use local features to match adjacent frames in ORB-SLAM system for the endoscopic poses estimation and keyframes selection, then combined with the probabilistic monocular stereo technology to calculate the dense depth map from keyframes, and finally complete the 3D dense reconstruction of the endoscopic scene. The experimental results proved that this method can track the endoscope robustly and reconstruct a 3D point cloud with high density and smoothness.",
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{
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"content": "Monocular visual simultaneous localization and mapping (SLAM) performs effectively in camera pose estimation and 3D sparse reconstruction of natural scenes. However, in monocular endoscopic environment, serious distortion of the images and the inconstant illumination, even the lack of surface texture, make SLAM-based tracking and 3D dense reconstruction still a challenge. In response to the above problems, it is proposed to use local features to match adjacent frames in ORB-SLAM system for the endoscopic poses estimation and keyframes selection, then combined with the probabilistic monocular stereo technology to calculate the dense depth map from keyframes, and finally complete the 3D dense reconstruction of the endoscopic scene. The experimental results proved that this method can track the endoscope robustly and reconstruct a 3D point cloud with high density and smoothness.",
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"normalizedAbstract": "Monocular visual simultaneous localization and mapping (SLAM) performs effectively in camera pose estimation and 3D sparse reconstruction of natural scenes. However, in monocular endoscopic environment, serious distortion of the images and the inconstant illumination, even the lack of surface texture, make SLAM-based tracking and 3D dense reconstruction still a challenge. In response to the above problems, it is proposed to use local features to match adjacent frames in ORB-SLAM system for the endoscopic poses estimation and keyframes selection, then combined with the probabilistic monocular stereo technology to calculate the dense depth map from keyframes, and finally complete the 3D dense reconstruction of the endoscopic scene. The experimental results proved that this method can track the endoscope robustly and reconstruct a 3D point cloud with high density and smoothness.",
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"Endoscopes",
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"affiliation": "School of Information Engineering Wuhan University of Technology,Wuhan,China",
"fullName": "Weishan Chen",
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"affiliation": "Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences,Shenzhen,China",
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"affiliation": "Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences,Shenzhen,China",
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"affiliation": "The Chinese University of Hong Kong",
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"abstract": "Face presentation attack detection (PAD) has been an urgent problem to be solved in the face recognition systems. Conventional approaches usually assume the testing and training are within the same domain; as a result, they may not generalize well into unseen scenarios because the representations learned for PAD may overfit to the subjects in the training set. In light of this, we propose an efficient disentangled representation learning for cross-domain face PAD. Our approach consists of disentangled representation learning (DR-Net) and multi-domain learning (MD-Net). DR-Net learns a pair of encoders via generative models that can disentangle PAD informative features from subject discriminative features. The disentangled features from different domains are fed to MD-Net which learns domain-independent features for the final cross-domain face PAD task. Extensive experiments on several public datasets validate the effectiveness of the proposed approach for cross-domain PAD.",
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"affiliation": "Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing , China; Peng Cheng Laboratory, Shenzhen, China",
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"affiliation": "Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing , China; University of Chinese Academy of Sciences, Beijing, China",
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"abstract": "A simultaneous walking experience in virtual and real spaces can provide a high sense of presence. However, users may face challenges when walking within a large virtual space while walking in a small and complex real space. Several methods such as Redirected Walking (RDW) and Substitutional Reality (SR) have been proposed as different approaches to this problem. However, the users must “reset” their movement direction at unpredictable moments to avoid collision in a small and complex real space when using subtle RDW that does not maintain the correspondence between virtual and real space. Contrarily, exploration through the SR has a limitation in that the VR scene is restricted to a controlled area. In this paper, we propose Reset at Fixed Positions (RFP), a method that combines RDW with the advantage of the SR and matches walkable real space with walkable virtual space. To utilize RFP, we defined Guaranteed Space Block (GSB), a unit space that constitutes a walkable virtual space. This space is obtained through the point reflection of the GSB utilizing the reset position within the GSB. RFPs can be implemented by two methods: Generating Virtual Space Using RFP (G-RFP) and Implementing Given Virtual Space Using RFP (I-RFP). G-RFP can create an infinitely large virtual space for exploration. On the other hand, I-RFP can conFigure a given virtual environment to make users walk. We observed that G-RFP provides higher presence, immersion and a higher mean distance traveled between resets compared to the existing RDW method in a complex real space through a user study. In addition, exploration through I-RFP provided a higher immersion, a comparable presence, and a similar number of resets.",
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"content": "A simultaneous walking experience in virtual and real spaces can provide a high sense of presence. However, users may face challenges when walking within a large virtual space while walking in a small and complex real space. Several methods such as Redirected Walking (RDW) and Substitutional Reality (SR) have been proposed as different approaches to this problem. However, the users must “reset” their movement direction at unpredictable moments to avoid collision in a small and complex real space when using subtle RDW that does not maintain the correspondence between virtual and real space. Contrarily, exploration through the SR has a limitation in that the VR scene is restricted to a controlled area. In this paper, we propose Reset at Fixed Positions (RFP), a method that combines RDW with the advantage of the SR and matches walkable real space with walkable virtual space. To utilize RFP, we defined Guaranteed Space Block (GSB), a unit space that constitutes a walkable virtual space. This space is obtained through the point reflection of the GSB utilizing the reset position within the GSB. RFPs can be implemented by two methods: Generating Virtual Space Using RFP (G-RFP) and Implementing Given Virtual Space Using RFP (I-RFP). G-RFP can create an infinitely large virtual space for exploration. On the other hand, I-RFP can conFigure a given virtual environment to make users walk. We observed that G-RFP provides higher presence, immersion and a higher mean distance traveled between resets compared to the existing RDW method in a complex real space through a user study. In addition, exploration through I-RFP provided a higher immersion, a comparable presence, and a similar number of resets.",
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"abstract": "In this paper, we describe the creation, deployment and validation study of WildAR, a networked AR system that can be used to conduct user studies over long periods of time “in-the-wild”, such as in museum settings. By combining four components: an AR game platform for implicit scaffolding of interactions, a web-based platform editor for real-time control of experiment variables, an automated participatory sensing system through smartphone sensors feeding back to an online database, and virtual experiment scene re-creation in VR using the data from the sensors, WildAR enables researchers to conduct user studies in museums efficiently and effectively.",
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"content": "In this paper, we present ARCHIE, a framework for testing augmented reality applications in the wild. ARCHIE collects user feedback and system state data in situ to help developers identify and debug issues important to testers. It also supports testing of multiple application versions (called \"profiles\") in a single evaluation session, prioritizing those versions which the tester finds more appealing. To evaluate ARCHIE, we implemented four distinct test case applications and used these applications to examine the performance overhead and context switching cost of incorporating our framework into a pre-existing code base. With these, we demonstrate that ARCHIE provides no significant overhead for AR applications, and introduces at most 2% processing overhead when switching among large groups of testable profiles.",
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"normalizedAbstract": "In this paper, we present ARCHIE, a framework for testing augmented reality applications in the wild. ARCHIE collects user feedback and system state data in situ to help developers identify and debug issues important to testers. It also supports testing of multiple application versions (called \"profiles\") in a single evaluation session, prioritizing those versions which the tester finds more appealing. To evaluate ARCHIE, we implemented four distinct test case applications and used these applications to examine the performance overhead and context switching cost of incorporating our framework into a pre-existing code base. With these, we demonstrate that ARCHIE provides no significant overhead for AR applications, and introduces at most 2% processing overhead when switching among large groups of testable profiles.",
"fno": "09089472",
"keywords": [
"Testing",
"Interviews",
"Smart Phones",
"Augmented Reality",
"Google",
"User Interfaces",
"Debugging",
"Software And Its Engineering X 2014 Software Testing And Debugging",
"Human Centered Computing X 2014 User Interface Toolkits",
"Human Centered Computing X 2014 Mixed Augmented Reality"
],
"authors": [
{
"affiliation": "Temple University",
"fullName": "Sarah M. Lehman",
"givenName": "Sarah M.",
"surname": "Lehman",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Stony Brook University",
"fullName": "Haibin Ling",
"givenName": "Haibin",
"surname": "Ling",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Temple University",
"fullName": "Chiu C. Tan",
"givenName": "Chiu C.",
"surname": "Tan",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "vr",
"isOpenAccess": false,
"showRecommendedArticles": true,
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"pubDate": "2020-03-01T00:00:00",
"pubType": "proceedings",
"pages": "903-912",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-5608-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
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