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2019 IEEE Scientific visualization conference, SciVis 2019

作者： Schatz, Karsten Diehl, Patrick Marcello, Dominic Frank, Juhan Muller, Thomas Frey, Steffen Reina, Guido Weiskopf, Daniel Ertl, Thomas Muller, Christoph Gralka, Patrick Heinemann, Moritz Straub, Alexander Schulz, Christoph Braun, Matthias Rau, Tobias Becher, Michael University of Stuttgart Germany Louisiana State University Baton Rouge United States Max Planck Institute for Astronomy Heidelberg Germany

ISBN: (纸本)9781728135120

The IEEE SciVis 2019 Contest targets the visual analysis of structure formation in the cosmic evolution of the universe from when the universe was five million years old up to now. In our submission, we analyze high-dimensional data to get an overview, then investigate the impact of Active Galactic Nuclei (AGNs) using various visualization techniques, for instance, an adapted filament filtering method for detailed analysis and particle flow in the vicinity of filaments. Based on feedback from domain scientists on these initial visualizations, we also analyzed X-ray emissions and star formation areas. The conversion of star-forming gas to stars and the resulting increasing molecular weight of the particles could be observed. © 2019 IEEE.

关键词： visualization

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Integrated prediction of deep-water gas channels using seismic coloured inversion and spectral decomposition attribute, West offshore, Nile Delta, Egypt

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NRIAG Journal of Astronomy and Geophysics 2020年第1期9卷 459-470页

作者： Amir Ismail[a][b] Hatem Farouk Ewida[c] Mohammad Galal Al-Ibiary[a] Aldo Zollo[b] [a]Department of Geology Faculty of Science Helwan University Cairo Egypt [b]Department of Physics “E. Pancini” University of Naples ‘Federico II’ Italy [c]Exploration General Manager PERENCO North Sinai Petroleum Company Cairo Egypt

ABSTRACTFormulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to *** the West Delta Deep Marine (WDDM) concession many important gas reservoirs have been recently discovered. To improve the possibility of discovering new natural gas resources, an integrated prediction method approach is used in this study to identify the deep-water gas reservoirs. Post-stack seismic coloured inversion and the spectral decomposition attribute, combined with composite logs of five wells, are used to indicate the lateral and vertical variations of the gas channels in El Wastani Formation. data conditioning and petrophysical analysis are performed. Both techniques help and improve visual evidence to detect gas channels. Coloured inverted seismic profile show relatively low impedance zones represent the gas channels. Spectral decomposition attribute solves the problem related to thin gas-sand layers through three different frequency magnitude values. The spatial distribution of the gas-channels properties indicates a good calibration at the well location with the effective porosity in the range of 15–35%. Comparing the results of the coloured seismic inversion with the spectral decomposition attribute, the low-frequency range through the spectral decomposition analysis is consistent with the low impedance values represents the gas channels. Furthermore, the methodology followed in the current study is advantageous to identify the gas reservoirs in various basins with similar geological settings.

关键词： Seismic inversion offshore Nile Delta gas channels gas-bearing sand zones spectral decomposition attribute well logging

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A visual Interaction Framework for Dimensionality Reduction Based data exploration

A Visual Interaction Framework for Dimensionality Reduction ...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Cavallo, Marco Demiralp, Cagatay IBM Res Yorktown Hts NY 10598 USA

ISBN: (纸本)9781450356206

Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data. However, reasoning dynamically about the results of a dimensionality reduction is difficult Dimensionality-reduction algorithms use complex optimizations to reduce the number of dimensions of a dataset, but these new dimensions often lack a clear relation to the initial data dimensions, thus making them difficult to interpret. Here we propose a visual interaction framework to improve dimensionality-reduction based exploratory data analysis. We introduce two interaction techniques, forward projection and backward projection, for dynamically reasoning about dimensionally reduced data, We also contribute two visualization techniques, prolines and feasibility maps, to facilitate the effective use of the proposed interactions. We apply our framework to PCA and autoencoder-based dimensionality reductions. Through data-exploration examples, we demonstrate how our visual interactions can improve the use of dimensionality reduction in exploratory data analysis.

关键词： Dimensionality reduction interaction bidirectional binding visual embedding forward projection backward projection PCA autoencoder deep learning proline feasibility map exploratory data analysis what-if analysis Praxis

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A Statistical Modeling Method for Road Recognition in Traffic Video Analytics

A Statistical Modeling Method for Road Recognition in Traffi...

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International conference on Cognitive Infocommunications (CogInfoCom)

作者： Hang Shi Hadi Ghahremannezhadand Chengjun Liu New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781728182131

ISBN: (纸本)9781728182148

A novel statistical modeling method is presented to solve the automated road recognition problem for the region of interest (RoI) detection in traffic video cognition. First, a temporal feature guided statistical modeling method is proposed for road modeling. Specifically, a foreground detection method is applied to extract the temporal features from the video and then to estimate a background image. Furthermore, the temporal features guide the statistical modeling method to select sample data. Additionally, a model pruning strategy is applied to estimate the road model. Second, a new road region detection method is presented to detect the road regions in the video. The method applies discrimination functions to classify each pixel in the estimated background image into a road class or a non-road class, respectively. The proposed method provides an intra-cognitive communication mode between the ROI selection and video analysis systems. Experimental results using real traffic videos from the New Jersey Department of Transportation (NJDOT) show that the proposed method is able to (i) detect the road region accurately and robustly and (ii) improve upon the state-of-the-art road recognition methods.

关键词： Analytical models Roads visual analytics Video sequences Transportation Feature extraction data models

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A Semi-supervised Deep Generative Model for Human Body analysis 1

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15th European conference on Computer Vision (ECCV)

作者： de Bem, Rodrigo Ghosh, Arnab Ajanthan, Thalaiyasingam Miksik, Ondrej Siddharth, N. Torr, Philip Univ Oxford Dept Engn Sci Oxford England Fed Univ Rio Grande Ctr Computat Sci Rio Grande Brazil

ISBN: (数字)9783030110123

ISBN: (纸本)9783030110123;9783030110116

Deep generative modelling for human body analysis is an emerging problem with many interesting applications. However, the latent space learned by such models is typically not interpretable, resulting in less flexible models. In this work, we adopt a structured semi-supervised approach and present a deep generative model for human body analysis where the body pose and the visual appearance are disentangled in the latent space. Such a disentanglement allows independent manipulation of pose and appearance, and hence enables applications such as pose-transfer without being explicitly trained for such a task. In addition, our setting allows for semi-supervised pose estimation, relaxing the need for labelled data. We demonstrate the capabilities of our generative model on the Human3.6M and on the DeepFashion datasets.

关键词： Deep generative models Variational autoencoders Semi-supervised learning Human pose estimation analysis-by-synthesis

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A Closed-Loop System Processing High-Density Electrical Recordings and visual Stimuli to Study Retinal Circuits Properties 9

A Closed-Loop System Processing High-Density Electrical Reco...

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9th IEEE/EMBS International conference on Neural Engineering (NER)

作者： Zaher, Sara Lonardoni, Davide Boi, Fabio Seu, Giovanni Pietro Angotzi, Gian Nicola Meloni, Paolo Berdondini, Luca Fdn Ist Italiano Tecnol IIT NetS3 Lab I-16163 Genoa Italy Politecn Torino Dept Mech & Aerosp Engn DIMEAS I-10129 Turin Italy Univ Genoa Dept Informat Bioengn Robot & Syst Engn I-16145 Genoa Italy Univ Cagliari Dept Elect & Elect Engn I-09123 Cagliari Italy

ISBN: (纸本)9781538679210

Active high-density electrode arrays can record the spiking activity of a large number of single-neurons in brain circuits. This offers the opportunity to develop closedloop neural interfaces for studying complex brain circuits contingent on their cellular activity. However, this requires adapted solutions to process large-volumes of data acquired by thousands of electrodes and, in turn, generate feedbacks. Here, we present a closed-loop system that exploits System on Chip resources of a Xilinx ZedBoard Zynq-7000 to processes the instantaneous mice retina activity recorded by 4096 closely spaced microelectrodes. This is used to infer in real-time the functional properties of retinal ganglion cells (RGCs) all over the retina. Results show the performances of two interactive algorithms designed for (i) data reduction by clustering single-unit sub-millisecond correlated spike-trains from closely spaced neighboring electrodes (closed-loop latency of 22.9 +/- 1.5 ms per second of recording, n=5 retina);(ii) the classification of major types of RGCs consisting in ON and OFF types of functional responses (closed-loop latency of 117.3 +/- 30.9 ms, n=5 retina).

关键词： Retina visualization Microelectrodes Classification algorithms Band-pass filters Image color analysis

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Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand Their Physical Limitations 1

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15th European conference on Computer Vision (ECCV)

作者： Devanne, Maxime Sao Mai Nguyen UBL IMT Atlantique Lab STICC Brest France

ISBN: (数字)9783030110123

ISBN: (纸本)9783030110123;9783030110116

Assistive robotics and particularly robot coaches may be very helpful for rehabilitation healthcare. In this context, we propose a method based on Gaussian Process Latent Variable Model (GP-LVM) to transfer knowledge between a physiotherapist, a robot coach and a patient. Our model is able to map visual human body features to robot data in order to facilitate the robot learning and imitation. In addition, we propose to extend the model to adapt the robots' understanding to patients' physical limitations during assessment of rehabilitation exercises. Experimental evaluation demonstrates promising results for both robot imitation and model adaptation according to patients' limitations.

关键词： Robot imitation Transfer knowledge Physical rehabilitation Shared Gaussian Process Latent Variable Model Motion analysis

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Demonstrating David Meets Goliath: Combining Smartwatches with a Large Vertical Display for visual data exploration

Demonstrating David Meets Goliath: Combining Smartwatches wi...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Horak, Tom Dachselt, Raimund Badam, Sriram Karthik Elmqvist, Niklas Tech Univ Dresden Interact Media Lab Dresden Germany Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781450356213

We explore the combination of smartwatches and a large interactive display to support visual data analysis. These two extremes of interactive surfaces are increasingly popular, but feature different characteristics-display and input modalities, personal/public use, performance, and portability. With this demonstration, we present our conceptual framework and its implementation, which enables analysts to explore data items using both devices in combination. Building on a analysis scenario for convicted crimes in Baltimore, our demonstration gives an impression of how the device combination can allow users to develop complex insights more fluidly by leveraging the device roles.

关键词： Cross-device interaction visual analysis data exploration multi-display environment large display smartwatch

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City Scale Monitoring of On-Street Parking Violations with StreetHAWK 19

City Scale Monitoring of On-Street Parking Violations with S...

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6th ACM International conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys)

作者： Ranjan, Alok Misra, Prasant Vasan, Arunchandar Krishnakumar, Sunil Sivasubramaniam, Anand Virginia Commonwealth Univ Richmond VA 23284 USA TCS Res & Innovat Bangalore Karnataka India TCS Res & Innovat Chennai Tamil Nadu India Penn State Univ University Pk PA USA TCS Bangalore Karnataka India

ISBN: (纸本)9781450370059

Unauthorized parking on city streets is a major contributor to traffic congestion and road accidents in developing nations. Due to the large scale and density of this problem, citywide (manual) monitoring of parking violations has not been effective with existing practices. To this end, we present StreetHAWK : an edge-centric, automated, real-time, privacy-preserving system;which leverages the rear camera of a dashboard mounted smartphone, and performs visual scene and location analytics to identify potential parking violations. We realize this system by overcoming the challenges of : (i) small object identification in various non-standard setups by extensive training on a deep learning based convolution detection model;(ii) limited violation assessment range of 15 m (a constraint of the phone's single camera unit) by augmenting it with a short-term historian and GPS for meeting the 100 m measurement violation guideline;and (iii) erroneous mobile scene analysis instances by lightweight filtering techniques that piggyback on the mobility of the camera and multi-modal sensing clues. The evaluation results obtained from real-world datasets show that StreetHAWK : (i) has three times higher accuracy in identifying small sized objects than other competing embedded detectors;and (ii) localizes these objects from a moving vehicle with a worst-case error of less than 5 m. On-the-road experiments show that StreetHAWK, running at a speed of 5 frames per second (FPS) on a typical Android smartphone, was able to detect (on an average) 80% of the parking violations.

关键词： Parking violation Smart cities Edge computing visual analytics Localization Mobile system Cyber-physical system

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An innovative approach to safe surgical suturing Part ii: data machine learning predictive analysis 19

An innovative approach to safe surgical suturing Part II: Da...

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9th International conference on Biomedical Engineering and Technology, ICBET 2019

作者： Levy, Alexandre Msellati, Julie De Muer, Andrea ECE Paris Paris France

ISBN: (纸本)9781450361309

Non-invasive robotized surgery is nowadays largely in action for most interventions because of its very beneficial advantages in terms of patient health and material efficiency. However, the still recurrent problem of guaranteeing the quality of suturing action (ie avoiding thread breaking) in all robotized interventions is recurrently impairing the overall results from this approach, mainly due to defective haptic information on threads available to the surgeons from the robot. To improve the efficiency of robot-surgeon collaboration, the problem of communicating relevant and reliable information on threads used by surgeons during suturing is addressed in present paper. From collected data on an experimental setup designed for the study described in Part I, machine learning predictive analysis is built-up in present Part. The approach helps understand the influence of different parameters on the suture ruptures and determine the safety zone in which the surgeon can pull the thread without danger. A display can be added to give the surgeon a visual return during the operations. Results obtained for different types of threads show up to 99% predictive accuracy, especially concerning maximum strength and maximum elongation of a suture before breaking. © 2019 Association for Computing Machinery.

关键词： Predictive analytics

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