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arXiv 2025年

作者： Arora, Vidhu Gupta, Shreyan Kudupu, Ananthakrishna Priyadarshi, Aditya Mundayatt, Aswathi Sreevalsan-Nair, Jaya Graphics-Visualization-Computing Lab International Institute of Information Technology Karnataka Bangalore560100 India

In this article, we improve the deep learning solution for coastline extraction from Synthetic Aperture Radar (SAR) images by proposing a two-stage model involving image classification followed by segmentation. We hypothesize that a single segmentation model usually used for coastline detection is insufficient to characterize different coastline types. We demonstrate that the need for a two-stage workflow prevails through different compression levels of these images. Our results from experiments using a combination of CNN and U-Net models on Sentinel-1 images show that the two-stage workflow, coastline classification-extraction from SAR images (CCESAR) outperforms a single U-Net segmentation model. © 2025, CC BY-SA.

关键词： Image segmentation

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EVOLUTION OF DATA-DRIVEN SINGLE- AND MULTI-HAZARD SUSCEPTIBILITY MAPPING AND EMERGENCE OF DEEP LEARNING METHODS

arXiv

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arXiv 2025年

作者： Sreevalsan-Nair, Jaya Mundayatt, Aswathi Graphics-Visualization-Computing Lab International Institute of Information Technology Karnataka Bangalore560100 India

Data-driven susceptibility mapping of natural hazards has harnessed the advances in classification methods used on heterogeneous sources represented as raster images. Susceptibility mapping is an important step towards risk assessment for any natural hazard. Increasingly, multiple hazards co-occur spatially, temporally, or both, which calls for an in-depth study on multi-hazard susceptibility mapping. In recent years, single-hazard susceptibility mapping algorithms have become well-established and have been extended to multi-hazard susceptibility mapping. Deep learning is also emerging as a promising method for single-hazard susceptibility mapping. Here, we discuss the evolution of methods for a single hazard, their extensions to multi-hazard maps as a late fusion of decisions, and the use of deep learning methods in susceptibility mapping. We finally propose a vision for adapting data fusion strategies in multimodal deep learning to multi-hazard susceptibility mapping. From the background study of susceptibility methods, we demonstrate that deep learning models are promising, untapped methods for multi-hazard susceptibility mapping. Data fusion strategies provide a larger space of deep learning models applicable to multi-hazard susceptibility mapping. © 2025, CC BY.

关键词： Mapping

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To the Point: From Dynamic Heatmap Video to Gaze Points 25

To the Point: From Dynamic Heatmap Video to Gaze Points

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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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On-Tube Attribute visualization for Multivariate Trajectory Data

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IEEE Transactions on visualization and Computer graphics 2023年第1期29卷 1288-1298页

作者： Russig, Benjamin Grab, David Dachselt, Raimund Gumhold, Stefan Department of Computer Graphics and Visualization TU Dresden Germany TU Dresden Interactive Media Lab Germany

Stylized tubes are an established visualization primitive for line data as encountered in many scientific fields, ranging from characteristic lines in flow fields, fiber tracks reconstructed from diffusion tensor imaging, to trajectories of moving objects as they arise from cyber-physical systems in many engineering disciplines. Typical challenges include large data set sizes demanding for efficient rendering techniques as well as a large number of attributes that cannot be mapped simultaneously to the basic visual attributes provided by a tube-based visualization. In this work, we tackle both challenges with a new on-tube visualization approach. We improve recent work on high-quality GPU ray casting of Hermite spline tubes supporting ambient occlusion and extend it by a new layered procedural texturing technique. In the proposed framework, a large number of data set attributes can be mapped simultaneously to a variety of glyphs and plots that are embedded in texture space and organized in layers. Efficient rendering with minimal data transfer is achieved by generating the glyphs procedurally and drawing them in a deferred shading pass. We integrated these techniques in a prototype visualization tool that facilitates flexible mapping of data set attributes to visual tube and glyph attributes. We studied our approach on a variety of example data from different fields and found it to provide a highly adaptable and extensible toolbox to quickly craft tailor-made tube-based trajectory visualizations. © 2022 IEEE.

关键词： Trajectories

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IMGD: Image-based Multiscale Global Descriptors of Airborne LiDAR Point Clouds Used for Comparative Analysis 8

IMGD: Image-based Multiscale Global Descriptors of Airborne ...

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8th Smart Tools and Applications in graphics Conference, STAG 2021

作者： Sreevalsan-Nair, J. Mohapatra, P. Singh, S. Graphics-Visualization-Computing Lab International Institute of Information Technology Bangalore India

ISBN: (纸本)9783038681656

Both geometric and semantic information are required for a complete understanding of regions acquired as three-dimensional (3D) point clouds using the Light Detection and Ranging (LiDAR) technology. However, the global descriptors of such datasets that integrate both the information types are rare. With a focus on airborne LiDAR point clouds, we propose a novel global descriptor that transforms the point cloud from Cartesian to barycentric coordinate spaces. We use both the probabilistic geometric classification, aggregated from multiple scales, and the semantic classification to construct our descriptor using point rendering. Thus, we get an image-based multiscale global descriptor, IMGD. To demonstrate its usability, we propose the use of distribution distance measures between the descriptors for comparing the point clouds. Our experimental results demonstrate the effectiveness of our descriptor, when constructed of publicly available datasets, and on applying our selected distance measures. © 2021 The Author(s)

关键词： Uncertainty analysis

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Tensor Fields for Data Extraction from Chart Images: Bar Charts and Scatter Plots 8th

Tensor Fields for Data Extraction from Chart Images: Bar Cha...

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8th Workshop on Topological Methods in Data Analysis and visualization, TopoInVis 2019

作者： Sreevalsan-Nair, Jaya Dadhich, Komal Daggubati, Siri Chandana Graphics-Visualization-Computing Lab International Institute of Information Technology Bangalore Karnataka India

ISBN: (纸本)9783030834999

Charts are an essential part of both graphicacy (graphical literacy), and statistical literacy. As chart understanding has become increasingly relevant in data science, automating chart analysis by processing raster images of the charts has become a significant problem. Automated chart reading involves data extraction and contextual understanding of the data from chart images. In this paper, we perform the first step of determining the computational model of chart images for data extraction for selected chart types, namely, bar charts, and scatter plots. We demonstrate the use of positive semidefinite second-order tensor fields as an effective model. We identify an appropriate tensor field as the model and propose a methodology for the use of its degenerate point extraction for data extraction from chart images. Our results show that tensor voting is effective for data extraction from bar charts and scatter plots, and histograms, as a special case of bar charts. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphic methods

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Design as traversal and consequences: An exploration tool for experimental designs 07

Design as traversal and consequences: An exploration tool fo...

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graphics Interface 2007

作者： Jennings, Christopher G. Kirkpatrick, Arthur E. Graphics Visualization and Usability Lab. Department of Computing Science Simon Fraser University Canada

ISBN: (纸本)1568813376

We present a design space explorer for the space of experimental designs. For many design problems, design decisions are determined by the consequences of the design rather than its elemental parts. To support this need, the explorer is constructed to make the designer aware of design-level options, provide a structured context for design, and provide feedback on the consequences of design decisions. We argue that this approach encourages the designer to consider a wider variety of designs, which will lead to more effective designs overall. In a qualitative study, experiment designers using the explorer were found to consider a wider variety of designs and more designs overall than they reported considering in their normal practice.

关键词： Computer graphics

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A versatile hierarchical mesh for subdivision surfaces

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IEEE Potentials 2008年第5期27卷 20-25页

作者： Fünfzig, Christoph Müller, Kerstin Janetzek, Rafael Techmann, Torsten PRISM Lab. Arizona State University United States PRISM Lab. Arizona State University Computer Graphics and Visualization University of Kaiserslautern Germany Darmstadt University of Technology Germany

The construction and processing of surface geometry can be done by connecting surface primitives to vertices, edges, and neighbors. The representation schemes of surface geometry are vertex-based, edge-based, and quad-edge-based. Catmull and Clark rule of subdivision includes three different rules to create new vertices in the refined control mesh, that are vertex, edge, and face subdivision rule. A new vertex can be created in the refined mesh as a convex combination of the vertices in the neighborhood of vertex, edge, and face in the control mesh. The first subdivision step has a quadrangle control mesh and number of vertices with valence unequal to four constant during refinement. The Charles Loop subdivision scheme is based on box spline and during a subdivision a triangle is divided into four triangles through vertex and edge subdivision rule.

关键词： Mesh generation

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Evaluating and Improving RoSELS for Road Surface Extraction from 3D Automotive LiDAR Point Cloud Sequences 1

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3rd International Conference on Deep Learning Theory and Applications, DeLTA 2022

作者： Katkoria, Dhvani Sreevalsan-Nair, Jaya Graphics-Visualization-Computing Lab IIIT Bangalore 26/C Electronics City Karnataka 560100 India

ISBN: (数字)9783031373176

ISBN: (纸本)9783031373169

Navigable space determination is a difficult problem encountered in robotics and intelligent vehicle technology and it requires integrated solutions using advances in computer vision and computational geometry. The conventional data processing workflow uses semantic segmentation to identify road points from three-dimensional (3D) automotive LiDAR point clouds, which have to be extended to determine its boundary points. The boundary points are critical in getting an initial coarse extraction of its surface. Hence, to filter the curb or edge points from the road points, the semantic segmentation results are post-processed. The road surface is determined by the entire trajectory information, which is captured as a sequence of LiDAR point clouds. For the entire road surface extraction, we use an automated system, RoSELS (Road Surface Extraction for LiDAR point cloud Sequence) that works at different scales, i.e., at the scale of a point, a point cloud, and the sequence of point clouds. In this paper, we evaluate the algorithms used in RoSELS, namely, the Gaussian Mixture Model for curb detection and ResNet-50 for transfer learning in frame classification. We evaluate the quality of the mesh extracted using RoSELS, which is intended for straight-road geometry. Here, we also show how the initial extracted surface can be further post-processed to extend RoSELS for curved roads. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicles

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BarChartAnalyzer: Data Extraction and Summarization of Bar Charts from Images

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SN Computer Science 2022年第6期3卷 500页

作者： Daggubati, Siri Chandana Sreevalsan-Nair, Jaya Dadhich, Komal Graphics-Visualization-Computing Lab IIIT Bangalore 26/C Electronics City Hosur Road Karnataka Bangalore 560100 India

Charts or scientific plots are widely used visualizations for efficient knowledge dissemination from datasets. However, these charts are predominantly available in image format. There are various scenarios where these images are interpreted in the absence of their source data table. This leads to a pertinent need for data extraction from an available chart image. We narrow down our scope to bar charts and its subtypes. We propose a semi-automated workflow, BarChartAnalyzer, for data extraction from chart images. Our workflow integrates the following tasks in sequence: chart type classification, image annotation, object detection, text detection and recognition, data table extraction, chart summarization, and, optionally, chart redesign. Our data extraction uses second-order tensor fields from tensor voting used in computer vision. Here, we propose a novel application of design study methodology for the chart summarization component. Our results show that our workflow can effectively and accurately extract data from images of different resolutions and subtypes of bar charts. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Bar charts Chart classification Chart image analysis Chart segmentation Convolutional neural Network Data extraction Design study methodology Grouped bar charts Histograms Optical character recognition Stacked bar charts Text recognition Text summarization

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