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14th International conference on Advanced visual Interfaces (AVI)

作者： Langner, Ricardo Horak, Tom Dachselt, Raimund Tech Univ Dresden Interact Media Lab Dresden Germany

ISBN: (纸本)9781450356169

We demonstrate the prototype of the conceptual VisTiles framework. VisTiles allows exploring multivariate data sets by using multiple coordinated views that are distributed across a set of mobile devices. This setup allows users to benefit from dynamic and user-defined interface arrangements and to easily initiate co-located data exploration sessions. The current web-based prototype runs on commodity devices and is able to determine the spatial device arrangement by either a cross-device pinch gesture or an external tracking system. Multiple data sets are provided that can be explored by different visualizations (e.g., scatterplots, parallel coordinate plots, stream graphs). With this demonstration, we showcase the general concepts of VisTiles and discuss ideas for enhancements as well the potential for application cases beyond data analysis.

关键词： visualization

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Quantification analysis of Behavioral Changes after Sciatic Nerve Ligation in Rats

Quantification Analysis of Behavioral Changes after Sciatic ...

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Asia-Pacific Signal and Information Processing Association Annual Summit and conference (APSIPA)

作者： Panyawut Sri-iesaranusorn Saeka Shimochi Naoki Ono Emrah Yatkin Hidehiro iida Kazushi Ikeda Junichiro Yoshimoto Nara Institute of Science and Technology Nara Japan University of Turku Finland

ISBN: (数字)9789881476883

ISBN: (纸本)9781728181301

Measuring behavior changes in humans and animals is ongoing research in both bioscience and neuroscience, due to its applications in nerve injury recovery. Previous studies focused on the sciatic nerve and its recovery process because partial damage to this nerve leads to paralyzed symptoms of entire lower body parts. Rats have been successfully used as a model for neuropathic pain such as partial sciatic nerve injury. Numerous behavioral tests have been used to measure pain behavior and determine the extent of damage in animals with induced nerve injury. However, data collection using behavioral tests is highly time-consuming and costly, because of visual inspection-based tests, manual labor, and the need for complicated equipment. The real-time video data can be obtained in animals' natural environment e.g. own cage, without the need for interacting with the animal, which potentially can influence the test results. DeepLabCut provides easy methods for observing and recording animal behavior in diverse settings using video data. In this study, to examine the behavioral changes in rats after sciatic nerve ligation, we (i) recorded videos of 9 rats during the first and fifth day after surgery, (ii) used DeepLabCut to track the rat's positions throughout the videos, and (iii) calculated behavior indexes based on spatio-temporal patterns that measure the behavioral changes in rats. The results in this study confirm our assumption that there were significant changes in the behavior of rats with partial sciatic nerve injury. These findings motivate us to investigate physiological processes accompanied by the recovery, and implement real-time monitoring and analysis for measuring behavior changes in rats.

关键词： Rats Indexes Legged locomotion Trajectory Surgery Injuries Tools

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EVALUATION OF VISION-BASED LOCALIZATION AND MAPPING TECHNIQUES IN A SUBSEA METROLOGY SCENARIO

EVALUATION OF VISION-BASED LOCALIZATION AND MAPPING TECHNIQU...

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conference on Underwater 3D Recording and Modelling - A Tool for Modern Applications and CH Recording

作者： Menna, F. Torresani, A. Nocerino, E. Nawaf, M. M. Seinturier, J. Remondino, F. Drap, P. Chemisky, B. COMEX SA Innovat Dept COMEX 36 Bd OceanCS 80143 F-13275 Marseille France Bruno Kessler Fdn FBK 3D Opt Metrol 3DOM Unit Trento Italy Aix Marseille Univ CNRS ENSAM Univ ToulonLIS UMR 7020 Domaine Univ St JeromeBatiment Polytech F-13397 Marseille France Swiss Fed Inst Technol Inst Theoret Phys CH-8093 Zurich Switzerland

Metrology is fundamental in all the applications that require to qualify, verify and validate measured data according to standards or, in other words, to assess their compliance with predefined tolerances. At sea, metrology is commonly associated with the process of measuring underwater structures, mainly pipeline elements widely used in offshore industry. Subsea operations are very expensive;optimizing time and money resources are the core factors driving innovation in the subsea metrology industry. In this study, the authors investigate the use of state-of-art vision-based algorithms, i.e. ORB-SLAM2 and visual Odometry, as a navigation tool to assist and control a Remotely Operated Vehicle (ROV) while performing subsea metrology operations. In particular, the manuscript will focus on methods for assessing the accuracy of both trajectory and tie points provided by the tested approaches and evaluating whether the preliminary real time reconstruction meets the tolerances defined in typical subsea metrology scenarios.

关键词： Underwater photogrammetry SLAM visual odometry subsea metrology ROV deep underwater exploration deformation analysis accuracy evaluation

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Skeleton-based Scagnostics

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 542-552页

作者： Matute, Jose Telea, Alexandru C. Linsen, Lars Univ Munster Inst Comp Sci Munster Germany Univ Groningen Johann Bernoulli Inst Math & Comp Sci POB 407 NL-9700 AK Groningen Netherlands

Scatterplot matrices (SPLOMs) are widely used for exploring multidimensional data. Scatterplot diagnostics (scagnostics) approaches measure characteristics of scatterplots to automatically find potentially interesting plots, thereby making SPLOMs more scalable with the dimension count. While statistical measures such as regression lines can capture orientation, and graph-theoretic scagnostics measures can capture shape, there is no scatterplot characterization measure that uses both descriptors. Based on well-known results in shape analysis, we propose a scagnostics approach that captures both scatterplot shape and orientation using skeletons (or medial axes). Our representation can handle complex spatial distributions, helps discovery of principal trends in a multiscale way, scales visually well with the number of samples, is robust to noise, and is automatic and fast to compute. We define skeleton-based similarity metrics for the visual exploration and analysis of SPLOMs. We perform a user study to measure the human perception of scatterplot similarity and compare the outcome to our results as well as to graph-based scagnostics and other visual quality metrics. Our skeleton-based metrics outperform previously defined measures both in terms of closeness to perceptually-based similarity and computation time efficiency.

关键词： Multidimensional data (primary keyword) High-Dimensional data

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What Moves Players? visual data exploration of Twitter and Gameplay data 18

What Moves Players? Visual Data Exploration of Twitter and G...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Drescher, Christian Wallner, Guenter Kriglstein, Simone Sifa, Rafet Drachen, Anders Pohl, Margit TU Wien Vienna Austria Fraunhofer IAIS St Augustin Germany Univ York DC Labs York N Yorkshire England

ISBN: (纸本)9781450356206

In recent years, microblogging platforms have not only become an important communication channel for the game industry to generate and uphold audience interest but also a rich resource for gauging player opinion. In this paper we use data gathered from Twitter to examine which topics matter to players and to identify influential members of a game's community. By triangulating in-game data with Twitter activity we explore how tweets can provide contextual information for understanding fluctuations in in-game activity. To facilitate analysis of the data we introduce a visual data exploration tool and use it to analyze tweets related to the game Destiny. In total, we collected over one million tweets from about 250,000 users over a 14-month period and gameplay data from roughly 3,500 players over a six-month period.

关键词： Twitter Gameplay data Online Community Destiny

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exploration of User Groups in VEXUS 34

Exploration of User Groups in VEXUS

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34th IEEE International conference on data Engineering Workshops (ICDEW)

作者： Amer-Yahia, Sihem Omidvar-Tehrani, Behrooz Comba, Joao Moreira, Viviane Zegarra, Fabian Colque Univ Grenoble Alpes Grenoble France CNRS Paris France Univ Fed Rio Grande do Sul Porto Alegre RS Brazil

ISBN: (纸本)9781538655207

We demonstrate VEXUS, an interactive visualization framework for exploring user data to fulfill tasks such as finding a set of experts, forming discussion groups and analyzing collective behaviors. User data is characterized by a combination of demographics like age and occupation, and actions such as rating a movie, writing a paper or following a medical treatment. The ubiquity of user data requires tools that help explorers, be they specialists or novice users, acquire new insights. VEXUS lets explorers interact with user data via visual primitives and builds an exploration profile to recommend the next exploration steps. VEXUS combines state-of-the-art visualization techniques with appropriate indexing of user data to provide fast and relevant exploration.

关键词： data visualization Histograms Navigation Brushes Task analysis visualization Motion pictures

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14th International Symposium on visual Computing, ISVC 2019

14th International Symposium on Visual Computing, ISVC 2019

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14th International Symposium on visual Computing, ISVC 2019

ISBN: (纸本)9783030337193

The proceedings contain 100 papers. The special focus in this conference is on visual Computing. The topics include: Adaptive Attention Model for Lidar Instance Segmentation;view Dependent Surface Material Recognition;3D visual Object Detection from Monocular Images;skin Identification Using Deep Convolutional Neural Network;resolution-Independent Meshes of Superpixels;automatic Video Colorization Using 3D Conditional Generative Adversarial Networks;improving visual Reasoning with Attention Alignment;multi-camera Temporal Grouping for Play/Break Event Detection in Soccer Games;Trajectory Prediction by Coupling Scene-LSTM with Human Movement LSTM;DeepGRU: Deep Gesture Recognition Utility;augmented Curiosity: Depth and Optical Flow Prediction for Efficient exploration;information visualization for Highlighting Conflicts in Educational Timetabling Problems;contourNet: Salient Local Contour Identification for Blob Detection in Plasma Fusion Simulation data;mutual Information-Based Texture Spectral Similarity Criterion;accurate Computation of Interval Volume Measures for Improving Histograms;Ant-SNE: Tracking Community Evolution via Animated t-SNE;Automated Segmentation of the Pectoral Muscle in Axial Breast MR Images;Angio-AI: Cerebral Perfusion Angiography with Machine Learning;conformal Welding for Brain-Intelligence analysis;Learning Graph Cut Class Prototypes for Thigh CT Tissue Identification;delineation of Road Networks Using Deep Residual Neural Networks and Iterative Hough Transform;automatic Estimation of Arterial Input Function in Digital Subtraction Angiography;one-Shot-Learning for visual Lip-Based Biometric Authentication;age Group and Gender Classification of Unconstrained Faces;efficient 3D Face Recognition in Uncontrolled Environment;Pupil Center Localization Using SOMA and CNN;Real-Time Face Features Localization with Recurrent Refined Dense CNN Architectures;estimation of the Distance Between Fingertips Using Silhouette and Texture Information of Dorsa

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Towards SDI services for geological maps data 1

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International conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2018

作者： Chafiq, Tarik Ouadoud, Mohammed Oulidi, Hassane Jarar Fekri, Ahmed Elaloui, Abdenbi Faculty of Sciences Ben M’Sik University Hassan II Casablanca Morocco Faculty of Sciences Abdelmalek Essaadi University Tetouan Morocco Hassania School of Public Works Casablanca Morocco

ISBN: (数字)9783030118815

ISBN: (纸本)9783030118808

The aim of this work is to facilitate access to geological data coming from distributed data sources and the information (metadata) related to these data. For that purpose, a spatial data infrastructure (SDI) prototype has been established which comprises a geoportal that provides access to a geological Catalogue through Catalogue Service for the Web (CSW) in order to get the metadata that describes the data as well as the available services as the WMS (Web Map Service) view and Web Feature Service (WFS) download services, which aims to be compliant with the Open Geospatial Consortium (OGC) standards rules. Indeed, the establishment of spatial data infrastructure that has been elaborated in accordance with internationally recognized standards allows the exploration and sharing of geological data information, and will also allow researchers and professionals to spend more time in the analysis and the discovery of these data. This work can be used as a model for other fields in geoscience such as geophysics and hydrology or other close fields. © Springer Nature Switzerland AG 2019.

关键词： Interoperability

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Emotion-Aware Human Attention Prediction 32

Emotion-Aware Human Attention Prediction

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Cordel, Macario O., ii Fan, Shaojing Shen, Zhiqi Kankanhalli, Mohan S. De La Salle Univ Manila Philippines Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781728132938

Despite the recent success in face recognition and object classification, in the field of human gaze prediction, computer models are still struggling to accurately mimic human attention. One main reason is that visual attention is a complex human behavior influenced by multiple factors, ranging from low-level features (e.g., color, contrast) to high-level human perception (e.g., objects interactions, object sentiment), making it difficult to model computationally. In this work, we investigate the relation between object sentiment and human attention. We first introduce an improved evaluation metric (AttI) for measuring human attention that focuses on human fixation consensus. A series of empirical data analyses with AttI indicate that emotione-voking objects receive attention favor, especially when they co-occur with emotionally-neutral objects, and this favor varies with different image complexity. Based on the empirical analyses, we design a deep neural network for human attention prediction which allows the attention bias on emotion-evoking objects to be encoded in its feature space. Experiments on two benchmark datasets demonstrate its superior performance, especially on metrics that evaluate relative importance of salient regions. This research provides the clearest picture to date on how object sentiments influence human attention, and it makes one of the first attempts to model this phenomenon computationally.

关键词： Deep Learning Others Representation Learning Scene analysis and Understanding Vision Applications and Systems

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Deep Learning of Movement Intent and Reaction Time for EEG-informed Adaptation of Rehabilitation Robots

Deep Learning of Movement Intent and Reaction Time for EEG-i...

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IEEE/RAS-EMBS International conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Neelesh Kumar Konstantinos P. Michmizos Computational Brain Lab Rutgers University New Jersey USA

ISBN: (数字)9781728159072

ISBN: (纸本)9781728159089

Mounting evidence suggests that adaptation is a crucial mechanism for rehabilitation robots in promoting motor learning. Yet, it is commonly based on robot-derived movement kinematics, which is a rather subjective measurement of performance, especially in the presence of a sensorimotor impairment. Here, we propose a deep convolutional neural network (CNN) that uses electroencephalography (EEG) as an objective measurement of two kinematics components that are typically used to assess motor learning and thereby adaptation: i) the intent to initiate a goal-directed movement, and ii) the reaction time (RT) for that movement. We evaluated our CNN on data acquired from an in-house experiment where 12 healthy subjects moved a rehabilitation robotic arm in four directions on a plane, in response to visual stimuli. Our CNN achieved average test accuracies of 80.08% and 79.82% in a binary classification of the intent (intent vs. no intent) and RT (slow vs. fast), respectively. Our results demonstrate how individual movement components implicated in distinct types of motor learning can be predicted from synchronized EEG data acquired before the start of the movement. Our approach can, therefore, inform robotic adaptation in real-time and has the potential to further improve one's ability to perform the rehabilitation task.

关键词： Electroencephalography Training Task analysis Rehabilitation robotics Robot sensing systems Manipulators Kinematics

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