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On-Tube Attribute visualization for Multivariate Trajectory Data

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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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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A Methodology for Integrating Population Health Surveys Using Spatial Statistics and visualizations for Cross-Sectional Analysis

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SN Computer Science 2023年第3期4卷 224页

作者： Ravindra, Harshitha Sreevalsan-Nair, Jaya Graphics-Visualization-Computing Lab E-Health Research Center IIIT Bangalore 26/C Electronics City Hosur Road Karnataka Bangalore 560100 India

Large-scale population surveys are beneficial in gathering information on the performance indicators of public well-being, including health and socio-economic standing. However, conducting national population surveys for low and middle-income countries (LMIC) with high population density comes at a high economic cost. To conduct surveys at low-cost and efficiently, multiple surveys with different, but focused, goals are implemented through various organizations in a decentralized manner. Some of the surveys tend to overlap in outcomes with spatial, temporal or both scopes. Mining data jointly from surveys with significant overlap gives new insights while preserving their autonomy. We propose a three-step workflow for integrating surveys using spatial analytic workflow supported by visualizations. We implement the workflow on a case study using two recent population health surveys in India to study malnutrition in children under-five. Our case study focuses on finding hotspots and coldspots for malnutrition, specifically undernutrition, by integrating the outcomes of both surveys. Malnutrition in children under-five is a pertinent global public health problem that is widely prevalent in India. Our work shows that such an integrated analysis is beneficial alongside independent analyses of such existing national surveys to find new insights into national health indicators. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Anemia Hellinger distance Large-scale surveys Micronutrient deficiency Multi-source data fusion Spatial analysis Spatial autocorrelation Stunting Underweight visualization Wasting

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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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CCESAR: COASTLINE CLASSIFICATION-EXTRACTION FROM SAR IMAGES USING CNN-U-NET COMBINATION

arXiv

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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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An Immersive Method for Extracting Structural Information from Unorganized Point Clouds

An Immersive Method for Extracting Structural Information fr...

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Nicograph International (NicoInt)

作者： Zhongyuan Yu Tianfang Lin Stefan Gumhold Matthew McGinity Immersive Experience Lab TU Dresden Chair of Computer Graphics and Visualization TU Dresden

ISBN: (数字)9798350386882

ISBN: (纸本)9798350386899

Point clouds find extensive applications across diverse domains such as construction, architecture, and archaeology due to their capacity to encapsulate rich datasets. Given the density and detail of these datasets, extracting accurate structural information (geometrical and connectivity information) demands sophisticated algorithms and tools. The recent advancements in virtual reality (VR) technology and rendering techniques have significantly elevated the ability to visualize these point cloud datasets, affording users an immersive experience within the data. This immersive capability holds the potential to streamline the process of extracting valuable information from these datasets. In this work, we explore the feasibility of extracting and visualizing structural information from point clouds with immersive interfaces. We propose strategies to enhance human perception of the underlying structure and interactive methods to extract face, edge, vertex, and connectivity data from the raw, unorganized point clouds. Our method is evaluated based on public datasets. We believe our work represents a promising attempt to integrate human insights into the data processing pipeline.

关键词： Point cloud compression Geometry Pipelines Data visualization Prototypes Immersive experience Rendering (computer graphics)

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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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Scaling Up Study Area Size in Flood Susceptibility Mapping

Scaling Up Study Area Size in Flood Susceptibility Mapping

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

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

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Flood susceptibility mapping (FSM) is crucial for preparedness and risk mitigation of floods, and machine learning (ML)-based FSM methods have been well-studied. However, due to computational constraints, geographical variations, and differential data availability, FSM is often confined to smaller regions, much less than 1,000 km 2 . To address the computational constraints of FSM generation in data-rich regions, we propose a data-parallel approach using PySpark with two levels of spatial partitioning, where they address the computational load and geographical variations, respectively. In our case study of the state of Kerala, India, we propose grid lattice partitioning and the Ecologically Sensitive Zone (ESZ) map for the two levels of spatial partitioning, respectively. The latter is used to train random forest classifier models. We investigate the influence of contiguity in training grids on FSM outcomes within different ESZs. Our quantitative and qualitative comparative analysis of observations demonstrate the importance of contiguous training grids, especially in capturing finer details in FSM. Our implementation demonstrates the efficiency of our approach in the study area of Kerala spanning 38,863 km 2 .

关键词： Training Accuracy Computational modeling Biological system modeling Scalability Lattices Computational efficiency

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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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