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Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Beryl Gnanaraj Swetha Manivasagam Jaya Sreevalsan-Nair Graphics-Visualization-Computing Lab International Institute of Information Technology Bangalore Bangalore Karnataka India Department of Computer Science and Engineering PSG College of Technology Coimbatore Tamil Nadu India

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CV4FEE: Flood Extent Estimation Using Consensus Voting in Ensemble of Methods for Change Detection in Sentinel-1 GRD SAR Images

CV4FEE: Flood Extent Estimation Using Consensus Voting in En...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Ragesh Thangavel Jaya Sreevalsan-Nair Center for Information Technology and Public Policy IIIT Bangalore Bangalore India Graphics-Visualization-Computing Lab IIIT Bangalore Bangalore India

ISBN: (纸本)9781728173344

There are several methods for finding flooded areas using change detection from C-band Sentinel-l temporal GRDH Synthetic Aperture Radar (SAR) images. Using these methods, an automatic flood extent mapping is obtained through differences between multiple time instances of SAR images, which itself is processed to remove noise and isolated pixels. While a few selected methods give comparable results using similar approaches, it is difficult to determine the best result amongst them. Hence, we propose CV4FEE, which is a consensus voting (CV) of outcomes from an ensemble of flood extent estimation (FEE) methods. We select three different state-of-the-art FEE methods, based on a specific criterion. We then use different consensus voting functions to compute deterministic as well as probabilistic flood extent maps, thus accounting for uncertainty in computation in selected methods. We demonstrate our results of flood area estimation in two different flooding events in India, namely the Hyderabad flood and the Nivar cyclone, in 2020. We show that the probabilistic maps give flood extent estimation that confirms the GEE4FLOOD results, qualitatively. Our method successfully demonstrates the uncertainty in the estimation process.

关键词： Uncertainty Systematics Estimation Lakes Probabilistic logic Reservoirs Radar polarimetry

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Comparing Community Detection Methods in Brain Functional Connectivity Networks 4th

Comparing Community Detection Methods in Brain Functional Co...

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4th International Conference on Computational Intelligence, Cyber Security, and Computational Models, ICC3 2019

作者： Vangimalla, Reddy Rani Sreevalsan-Nair, Jaya Graphics-Visualization-Computing Lab E-Health Research Center International Institute of Information Technology - Bangalore 26/C Electronics City Bangalore560100 India

ISBN: (纸本)9789811596995

Brain functional networks are essential for understanding the functional connectome. computing the temporal dependencies between the regions of brain activities from the functional magnetic resonance imaging (fMRI) gives us the functional connectivity between the regions. The pairwise connectivities in matrix form correspond to the functional network (fNet), also referred to as a functional connectivity network (FCN). We start with analyzing a correlation matrix, which is an adjacency matrix of the FCN. In this work, we perform a case study of comparison of different analytical approaches in finding node-communities of the brain network. We use five different methods of community detection, out of which two methods are implemented on the network after filtering out the edges with weight below a predetermined threshold. We additionally compute and observe the following characteristics of the outcomes: (i) modularity of the communities, (ii) symmetrical node-partition between the left and right hemispheres of the brain, i.e., hemispheric symmetry, and (iii) hierarchical modular organization. Our contribution is in identifying an appropriate test bed for comparison of outcomes of approaches using different semantics, such as network science, information theory, multivariate analysis, and data mining. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Influence of Aleatoric Uncertainty on Semantic Classification of Airborne Lidar Point Clouds: A Case Study with Random Forest Classifier Using Multiscale Features

Influence of Aleatoric Uncertainty on Semantic Classificatio...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jaya Sreevalsan-Nair Pragyan Mohapatra Graphics-Visualization-Computing Lab International Institute of Information Technology Bangalore Karnataka India

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

For semantic classification of LiDAR point clouds, the features derived from the local geometric descriptors are routinely used as features in (supervised) learning algorithms. In this study, our goal is to determine if the aleatoric uncertainty in the input to a supervised semantic classifier influences the outcomes. We consider two sources of such uncertainty - one from the computation of multiscale local geometric descriptors and the other, from class ambiguities at object boundaries. We perform ensembles of experiments to measure the significance of these uncertainties in the semantic classification of airborne LiDAR point clouds, when using random forest classifier. Our case study shows that the presence of aleatoric uncertainty improves the classification outcomes.

关键词： Uncertainty Semantics Three-dimensional displays Laser radar Training Probabilistic logic Measurement

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AUGMENTED SEMANTIC SIGNATURES OF AIRBORNE LIDAR POINT CLOUDS FOR DETERMINING CHANGE IN TIME-VARYING DATA

arXiv

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arXiv 2020年

作者： Sreevalsan-Nair, Jaya Mohapatra, Pragyan Graphics-Visualization-Computing Lab International Institute of Information Technology Bangalore Karnataka560100 India

LiDAR point clouds provide rich geometric information, which is particularly useful for the analysis of complex scenes of urban regions. Finding structural and semantic differences between two different three-dimensional point clouds, say, of the same region but acquired at different time instances is an important problem. Usually, the data capture has inconsistencies when taken at different time instances, e.g., sampling densities, and the orientation of the flight path. Hence, change detection involves computationally expensive registration and segmentation. We are interested in capturing the relative differences in the geometric uncertainty and semantic content of the point cloud without the registration process. Hence, we propose an orientation-invariant geometric signature of the point cloud, which integrates its probabilistic geometric and semantic classifications. We study different properties of the geometric signature, which are image-based encoding of geometric uncertainty and semantic content. We explore different metrics to determine differences between these signatures, which in turn compare point clouds without performing point-to-point registration. We have observed that a point cloud with four semantic classes, namely, buildings, trees, road, and low-vegetation, that the tree class shows a characteristic pattern. Thus, we use a case study of airborne LiDAR point clouds where the visual and the quantitative comparisons of the geometric signatures of point clouds are useful in demonstrating changes during a thematic event, such as progressive deforestation, in the topography of an urban region. Our results show that the differences in the signatures corroborate with the geometric and semantic differences of the point *** Codes 68U05, 68U10, 68U20, 65C50 Copyright © 2020, The Authors. All rights reserved.

关键词： Geometry

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SHREC'19 track: Extended 2D scene sketch-based 3D scene retrieval 12

SHREC'19 track: Extended 2D scene sketch-based 3D scene retr...

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12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual Conference of the European Association for Computer graphics, EG 2019

作者： Yuan, Juefei Abdul-Rashid, Hameed Li, Bo Lu, Yijuan Schreck, Tobias Bui, Ngoc-Minh Do, Trong-Le Nguyen, Khac-Tuan Nguyen, Thanh-An Nguyen, Vinh-Tiep Tran, Minh-Triet Wang, Tianyang School of Computing Sciences and Computer Engineering University of Southern Mississippi United States Department of Computer Science Texas State University United States Institute of Computer Graphics and Knowledge Visualization Graz University of Technology Austria Faculty of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Software Engineering Lab Vietnam National University Ho Chi Minh City Viet Nam University of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Department of Computer Science and Information Technology Austin Peay State University United States

ISBN: (纸本)9783038680772

Sketch-based 3D scene retrieval is to retrieve 3D scene models given a user's hand-drawn 2D scene sketch. It is a brand new but also very challenging research topic in the field of 3D object retrieval due to the semantic gap in their representations: 3D scene models or views differ from non-realistic 2D scene sketches. To boost this interesting research, we organized a 2D Scene Sketch-Based 3D Scene Retrieval track in SHREC'18, resulting a SceneSBR18 benchmark which contains 10 scene classes. In order to make it more comprehensive, we have extended the number of the scene categories from the initial 10 classes in the SceneSBR2018 benchmark to 30 classes, resulting in a new and more challenging benchmark SceneSBR2019 which has 750 2D scene sketches and 3,000 3D scene models. Therefore, the objective of this track is to further evaluate the performance and scalability of different 2D scene sketch-based 3D scene model retrieval algorithms using this extended and more comprehensive new benchmark. In this track, two groups from USA and Vietnam have successfully submitted 4 runs. Based on 7 commonly used retrieval metrics, we evaluate their retrieval performance. We have also conducted a comprehensive analysis and discussion of these methods and proposed several future research directions to deal with this challenging research topic. Deep learning techniques have been proved their great potentials again in dealing with this challenging retrieval task, in terms of both retrieval accuracy and scalability to a larger dataset. We hope this publicly available benchmark, together with its evaluation results and source code, will further enrich and promote 2D scene sketch-based 3D scene retrieval research area and its corresponding applications. © 2019 The Author(s)

关键词： Scalability

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IDEAS: Immersive dome experiences for accelerating science

arXiv

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arXiv 2019年

作者： Faherty, Jacqueline K. SubbaRao, Mark Wyatt, Ryan Ynnerman, Anders deGrasse Tyson, Neil Geller, Aaron Weber, Maria Rosenfield, Philip Stoeckle, Gabriel Weiskopf, Daniel Magnor, Marcus Williams, Peter K.G. Abbott, Brian Marchetti, Lucia Jarrrett, Thomas Fay, Jonathan Peek, Joshua Graur, Or Durrell, Patrick Homeier, Derek Preston, Heather Müller, Thomas Vos, Johanna M. Brown, David Godfrey, Paige Giorla Rice, Emily Gagliuffi, Daniella Bardalez Bock, Alexander American Museum of Natural History Hayden Planetarium Adler Planetarium International Planetarium Society California Academy of Sciences Linköping University Adler Planetarium University of Chicago AAS WorldWide Telescope Instituto de Astronomiá UNAM Natural History Museum Vienna Austria Visualization Research Center University of Stuttgart Computer Graphics Lab TU Braunschweig Center for Astrophysics | Harvard & Smithsonian American Astronomical Society American Museum of Natural History Hayden Planetarium University of Cape Town Iziko Planetarium & Digital Dome Space Telescope Science Institute Johns Hopkins University Youngstown State University Ward Beecher Planetarium Förderkreis Planetarium Göttingen Germany Calusa Nature Center & Planetarium Haus der Astronomie MPIA Microsoft Research Slooh CUNY Macaulay Honors College Scientific Computing and Imaging Institute University of Utah

Astrophysics lies at the crossroads of big datasets (such as the Large Synoptic Survey Telescope and Gaia), open source software to visualize and interpret high dimensional datasets (such as Glue, WorldWide Telescope, and OpenSpace), and uniquely skilled software engineers who bridge data science and research fields. At the same time, more than 4,000 planetariums across the globe immerse millions of visitors in scientific data. We have identified the potential for critical synergy across data, software, hardware, locations, and content that-if prioritized over the next decade-will drive discovery in astronomical research. Planetariums can and should be used for the advancement of scientific research. Current facilities such as the Hayden Planetarium in New York City, Adler Planetarium in Chicago, Morrison Planetarium in San Francisco, the Iziko Planetarium & Digital Dome Research Consortium in Cape Town, and visualization Center C in Norrköping are already developing software which ingests catalogs of astronomical and multi-disciplinary data critical for exploration research primarily for the purpose of creating scientific storylines for the general public. We propose a transformative model whereby scientists become the audience and explorers in planetariums, utilizing software for their own investigative purposes. In this manner, research benefits from the authentic and unique experience of data immersion contained in an environment bathed in context and equipped for collaboration. Consequently, in this white paper we argue that over the next decade the research astronomy community should partner with planetariums to create visualization-based research opportunities for the field. Realizing this vision will require new investments in software and human capital. Copyright © 2019, The Authors. All rights reserved.

关键词： Planetariums

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Constructing Multiple Scene Graphs in Distributed Environment

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Computer Aided Drafting,Design and Manufacturing 2015年第1期25卷 16-21页

作者： XIA Rui WANG Guo-ping LI Sheng WANG Heng Shenzhen Key Lab for Cloud Computing Technology and Applications Shenzhen Graduate School of Peking University Graphics & Interaction Lab. School of Electronics Engineering and Computer SciencesPeking University Beijing Engineering Technology Research Center of Virtual Simulation and Visualization Peking University

Scene graph is a infrastructure of the virtual reality system to organize the virtual scene with abstraction, it can provide facility for the rendering engine and should be integrated effectively on demand into a real-time system, where a large quantities of scene objects and resources can be manipulated and managed with high flexibility and reliability. We present a new scheme of multiple scene graphs to accommodate the features of rendering engine and distributed systems. Based upon that, some other functions, e.g. block query, interactive editing, permission management, instance response, ＂redo＂ and ＂undo＂, are implemented to satisfy various requirements. At the same time, our design has compatibility to popular C/S architecture with good concurrent performance. Above all, it is convenient to be used for further development. The results of experiments including responding time demonstrate its good performance.

关键词： multiple scene graphs authority scene editing distributed systems

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Remote interactive visualization of parallel implementation of structural feature extraction of three-dimensional lidar point cloud 3

Remote interactive visualization of parallel implementation ...

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3rd International Conference on Big Data Analytics, BDA 2014

作者： Kumari, Beena Ashe, Avijit Sreevalsan-Nair, Jaya Graphics-Visualization-Computing-Lab International Institute of Information Technology Bangalore 26/C Electronics City Bangalore560100 India

ISBN: (纸本)9783319138190

Lidar (Light detection and ranging) is a popularly used technology to collect dense and precise data of topographic structures of the surface of the earth. In this paper, we have proposed a user assisted remote visualization system for extraction and visualization of structural features obtained from point cloud data obtained from lidar data. The sharp feature lines such as crest lines, edges of buildings, ravines, ridges are known as structural features of point cloud. Our work includes: (a) parallel implementation a topology-based algorithm for extraction of structural features from lidar point cloud using GPGPU (General Purpose graphics Processing Unit) computing, and (b) using a LAN (Local Area Network)-based server-client architecture to achieve remote visualization. © Springer International Publishing Switzerland 2014.

关键词： graphics processing unit

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Structure-aware shape processing 13

Structure-aware shape processing

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SIGGRAPH Asia 2013 Courses, SA 2013

作者： Mitra, Niloy J. Wand, Michael Zhang, Hao Cohen-Or, Daniel Kim, Vladimir Huang, Qi-Xing United Kingdom Max-Planck Institut Informatik Saarland University Germany Graphics Usability and Visualization Lab Simon Fraser University Canada TAU United States School of Computing Science United States Stanford Univ. United States Computer Science Department Princeton University United States Department Computer Science Stanford University United States

ISBN: (纸本)9781450326315

Shape structure is about the arrangement and relations between shape parts. Structure-aware shape processing goes beyond local geometry and low level processing, and analyzes and processes shapes at a high level. It focuses more on the global inter and intra semantic relations among the parts of shape rather than on their local geometry. With recent developments in easy shape acquisition, access to vast repositories of 3D models, and simple-to-use desktop fabrication possibilities, the study of structure in shapes has become a central research topic in shape analysis, editing, and modeling. A whole new line of structure-aware shape processing algorithms has emerged that base their operation on an attempt to understand such structure in shapes. The algorithms broadly consist of two key phases: an analysis phase, which extracts structural information from input data;and a (smart) processing phase, which utilizes the extracted information for exploration, editing, and synthesis of novel shapes. In this survey paper, we organize, summarize, and present the key concepts and methodological approaches towards efficient structure-aware shape processing. We discuss common models of structure, their implementation in terms of mathematical formalism and algorithms, and explain the key principles in the context of a number of state-of-the-art approaches. Further, we attempt to list the key open problems and challenges, both at the technical and at the conceptual level, to make it easier for new researchers to better explore and contribute to this topic. Our goal is to both give the practitioner an overview of available structure-aware shape processing techniques, as well as identify future research questions in this important, emerging, and fascinating research area.

关键词： Semantics

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