Pub Date : 2024-09-23DOI: 10.1109/TVCG.2024.3456329
Alexander Wyss, Gabriela Morgenshtern, Amanda Hirsch-Husler, Jurgen Bernard
In medical diagnostics of both early disease detection and routine patient care, particle-based contamination of in-vitro diagnostics consumables poses a significant threat to patients. Objective data-driven decision-making on the severity of contamination is key for reducing patient risk, while saving time and cost in quality assessment. Our collaborators introduced us to their quality control process, including particle data acquisition through image recognition, feature extraction, and attributes reflecting the production context of particles. Shortcomings in the current process are limitations in exploring thousands of images, data-driven decision making, and ineffective knowledge externalization. Following the design study methodology, our contributions are a characterization of the problem space and requirements, the development and validation of DaedalusData, a comprehensive discussion of our study's learnings, and a generalizable framework for knowledge externalization. DaedalusData is a visual analytics system that enables domain experts to explore particle contamination patterns, label particles in label alphabets, and externalize knowledge through semi-supervised label-informed data projections. The results of our case study and user study show high usability of DaedalusData and its efficient support of experts in generating comprehensive overviews of thousands of particles, labeling of large quantities of particles, and externalizing knowledge to augment the dataset further. Reflecting on our approach, we discuss insights on dataset augmentation via human knowledge externalization, and on the scalability and trade-offs that come with the adoption of this approach in practice.
{"title":"DaedalusData: Exploration, Knowledge Externalization and Labeling of Particles in Medical Manufacturing - A Design Study.","authors":"Alexander Wyss, Gabriela Morgenshtern, Amanda Hirsch-Husler, Jurgen Bernard","doi":"10.1109/TVCG.2024.3456329","DOIUrl":"https://doi.org/10.1109/TVCG.2024.3456329","url":null,"abstract":"<p><p>In medical diagnostics of both early disease detection and routine patient care, particle-based contamination of in-vitro diagnostics consumables poses a significant threat to patients. Objective data-driven decision-making on the severity of contamination is key for reducing patient risk, while saving time and cost in quality assessment. Our collaborators introduced us to their quality control process, including particle data acquisition through image recognition, feature extraction, and attributes reflecting the production context of particles. Shortcomings in the current process are limitations in exploring thousands of images, data-driven decision making, and ineffective knowledge externalization. Following the design study methodology, our contributions are a characterization of the problem space and requirements, the development and validation of DaedalusData, a comprehensive discussion of our study's learnings, and a generalizable framework for knowledge externalization. DaedalusData is a visual analytics system that enables domain experts to explore particle contamination patterns, label particles in label alphabets, and externalize knowledge through semi-supervised label-informed data projections. The results of our case study and user study show high usability of DaedalusData and its efficient support of experts in generating comprehensive overviews of thousands of particles, labeling of large quantities of particles, and externalizing knowledge to augment the dataset further. Reflecting on our approach, we discuss insights on dataset augmentation via human knowledge externalization, and on the scalability and trade-offs that come with the adoption of this approach in practice.</p>","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142309486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The importance of data charts is self-evident, given their ability to express complex data in a simple format that facilitates quick and easy comparisons, analysis, and consumption. However, the inherent visual nature of the charts creates barriers for people with visual impairments to reap the associated benefts to the same extent as their sighted peers. While extant research has predominantly focused on understanding and addressing these barriers for blind screen reader users, the needs of low-vision screen magnifer users have been largely overlooked. In an interview study, almost all low-vision participants stated that it was challenging to interact with data charts on small screen devices such as smartphones and tablets, even though they could technically "see" the chart content. They ascribed these challenges mainly to the magnifcation-induced loss of visual context that connected data points with each other and also with chart annotations, e.g., axis values. In this paper, we present a method that addresses this problem by automatically transforming charts that are typically non-interactive images into personalizable interactive charts which allow selective viewing of desired data points and preserve visual context as much as possible under screen enlargement. We evaluated our method in a usability study with 26 low-vision participants, who all performed a set of representative chart-related tasks under different study conditions. In the study, we observed that our method signifcantly improved the usability of charts over both the status quo screen magnifer and a state-of-the-art space compaction-based solution.
{"title":"Towards Enhancing Low Vision Usability of Data Charts on Smartphones.","authors":"Yash Prakash, Pathan Aseef Khan, Akshay Kolgar Nayak, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok","doi":"10.1109/TVCG.2024.3456348","DOIUrl":"https://doi.org/10.1109/TVCG.2024.3456348","url":null,"abstract":"<p><p>The importance of data charts is self-evident, given their ability to express complex data in a simple format that facilitates quick and easy comparisons, analysis, and consumption. However, the inherent visual nature of the charts creates barriers for people with visual impairments to reap the associated benefts to the same extent as their sighted peers. While extant research has predominantly focused on understanding and addressing these barriers for blind screen reader users, the needs of low-vision screen magnifer users have been largely overlooked. In an interview study, almost all low-vision participants stated that it was challenging to interact with data charts on small screen devices such as smartphones and tablets, even though they could technically \"see\" the chart content. They ascribed these challenges mainly to the magnifcation-induced loss of visual context that connected data points with each other and also with chart annotations, e.g., axis values. In this paper, we present a method that addresses this problem by automatically transforming charts that are typically non-interactive images into personalizable interactive charts which allow selective viewing of desired data points and preserve visual context as much as possible under screen enlargement. We evaluated our method in a usability study with 26 low-vision participants, who all performed a set of representative chart-related tasks under different study conditions. In the study, we observed that our method signifcantly improved the usability of charts over both the status quo screen magnifer and a state-of-the-art space compaction-based solution.</p>","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142304794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-20DOI: 10.1109/TVCG.2024.3456311
Jinrui Wang, Xinhuan Shu, Benjamin Bach, Uta Hinrichs
This paper defines, analyzes, and discusses the emerging genre of visualization atlases. We currently witness an increase in web-based, data-driven initiatives that call themselves "atlases" while explaining complex, contemporary issues through data and visualizations: climate change, sustainability, AI, or cultural discoveries. To understand this emerging genre and inform their design, study, and authoring support, we conducted a systematic analysis of 33 visualization atlases and semi-structured interviews with eight visualization atlas creators. Based on our results, we contribute (1) a definition of a visualization atlas as a compendium of (web) pages aimed at explaining and supporting exploration of data about a dedicated topic through data, visualizations and narration. (2) a set of design patterns of 8 design dimensions, (3) insights into the atlas creation from interviews and (4) the definition of 5 visualization atlas genres. We found that visualization atlases are unique in the way they combine i) exploratory visualization, ii) narrative elements from data-driven storytelling and iii) structured navigation mechanisms. They target a wide range of audiences with different levels of domain knowledge, acting as tools for study, communication, and discovery. We conclude with a discussion of current design practices and emerging questions around the ethics and potential real-world impact of visualization atlases, aimed to inform the design and study of visualization atlases.
{"title":"Visualization Atlases: Explaining and Exploring Complex Topics through Data, Visualization, and Narration.","authors":"Jinrui Wang, Xinhuan Shu, Benjamin Bach, Uta Hinrichs","doi":"10.1109/TVCG.2024.3456311","DOIUrl":"https://doi.org/10.1109/TVCG.2024.3456311","url":null,"abstract":"<p><p>This paper defines, analyzes, and discusses the emerging genre of visualization atlases. We currently witness an increase in web-based, data-driven initiatives that call themselves \"atlases\" while explaining complex, contemporary issues through data and visualizations: climate change, sustainability, AI, or cultural discoveries. To understand this emerging genre and inform their design, study, and authoring support, we conducted a systematic analysis of 33 visualization atlases and semi-structured interviews with eight visualization atlas creators. Based on our results, we contribute (1) a definition of a visualization atlas as a compendium of (web) pages aimed at explaining and supporting exploration of data about a dedicated topic through data, visualizations and narration. (2) a set of design patterns of 8 design dimensions, (3) insights into the atlas creation from interviews and (4) the definition of 5 visualization atlas genres. We found that visualization atlases are unique in the way they combine i) exploratory visualization, ii) narrative elements from data-driven storytelling and iii) structured navigation mechanisms. They target a wide range of audiences with different levels of domain knowledge, acting as tools for study, communication, and discovery. We conclude with a discussion of current design practices and emerging questions around the ethics and potential real-world impact of visualization atlases, aimed to inform the design and study of visualization atlases.</p>","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142304763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1109/TVCG.2024.3463800
Zhenxing Cui, Lu Chen, Yunhai Wang, Daniel Haehn, Yong Wang, Hanspeter Pfister
This paper presents a systematic study of the generalization of convolutional neural networks (CNNs) and humans on relational reasoning tasks with bar charts. We first revisit previous experiments on graphical perception and update the benchmark performance of CNNs. We then test the generalization performance of CNNs on a classic relational reasoning task: estimating bar length ratios in a bar chart, by progressively perturbing the standard visualizations. We further conduct a user study to compare the performance of CNNs and humans. Our results show that CNNs outperform humans only when the training and test data have the same visual encodings. Otherwise, they may perform worse. We also find that CNNs are sensitive to perturbations in various visual encodings, regardless of their relevance to the target bars. Yet, humans are mainly influenced by bar lengths. Our study suggests that robust relational reasoning with visualizations is challenging for CNNs. Improving CNNs' generalization performance may require training them to better recognize task-related visual properties.
{"title":"Generalization of CNNs on Relational Reasoning With Bar Charts.","authors":"Zhenxing Cui, Lu Chen, Yunhai Wang, Daniel Haehn, Yong Wang, Hanspeter Pfister","doi":"10.1109/TVCG.2024.3463800","DOIUrl":"https://doi.org/10.1109/TVCG.2024.3463800","url":null,"abstract":"<p><p>This paper presents a systematic study of the generalization of convolutional neural networks (CNNs) and humans on relational reasoning tasks with bar charts. We first revisit previous experiments on graphical perception and update the benchmark performance of CNNs. We then test the generalization performance of CNNs on a classic relational reasoning task: estimating bar length ratios in a bar chart, by progressively perturbing the standard visualizations. We further conduct a user study to compare the performance of CNNs and humans. Our results show that CNNs outperform humans only when the training and test data have the same visual encodings. Otherwise, they may perform worse. We also find that CNNs are sensitive to perturbations in various visual encodings, regardless of their relevance to the target bars. Yet, humans are mainly influenced by bar lengths. Our study suggests that robust relational reasoning with visualizations is challenging for CNNs. Improving CNNs' generalization performance may require training them to better recognize task-related visual properties.</p>","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142304740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1109/TVCG.2024.3464738
Riccardo Monica, Dario Lodi Rizzini, Jacopo Aleotti
Head-mounted displays (HMDs) in room-scale virtual reality are usually tracked using inside-out visual SLAM algorithms. Alternatively, to track the motion of the HMD with respect to a fixed real-world reference frame, an outside-in instrumentation like a motion capture system can be adopted. However, outside-in tracking systems may temporarily lose tracking as they suffer by occlusion and blind spots. A possible solution is to adopt a hybrid approach where the inside-out tracker of the HMD is augmented with an outside-in sensing system. On the other hand, when the tracking signal of the outside-in system is recovered after a loss of tracking the transition from inside-out tracking to hybrid tracking may generate a discontinuity, i.e a sudden change of the virtual viewpoint, that can be uncomfortable for the user. Therefore, hybrid tracking solutions for HMDs require advanced sensor fusion algorithms to obtain a smooth transition. This work proposes a method for hybrid tracking of a HMD with smooth transitions based on an adaptive complementary filter. The proposed approach can be configured with several parameters that determine a trade-off between user experience and tracking error. A user study was carried out in a room-scale virtual reality environment, where users carried out two different tasks while multiple signal tracking losses of the outside-in sensor system occurred. The results show that the proposed approach improves user experience compared to a standard Extended Kalman Filter, and that tracking error is lower compared to a state-of-the-art complementary filter when configured for the same quality of user experience.
室内虚拟现实中的头戴式显示器(HMD)通常使用内向外视觉 SLAM 算法进行跟踪。另外,为了跟踪头戴式显示器相对于固定现实世界参考帧的运动,也可以采用运动捕捉系统等外入式仪器。然而,外入式跟踪系统可能会暂时失去跟踪能力,因为它们会受到遮挡和盲点的影响。一种可行的解决方案是采用混合方法,即在 HMD 的由内向外跟踪器上增加一个由外向内的传感系统。另一方面,当外入式系统的跟踪信号在失去跟踪后恢复时,从内向外跟踪到混合跟踪的过渡可能会产生不连续性,即虚拟视点的突然变化,这会让用户感到不舒服。因此,用于 HMD 的混合跟踪解决方案需要先进的传感器融合算法来实现平稳过渡。本作品提出了一种基于自适应互补滤波器的平滑过渡 HMD 混合跟踪方法。所提出的方法可配置多个参数,这些参数决定了用户体验与跟踪误差之间的权衡。在房间规模的虚拟现实环境中进行了一项用户研究,用户在执行两项不同任务的同时,外入式传感器系统出现了多个信号跟踪损失。结果表明,与标准的扩展卡尔曼滤波器相比,所提出的方法改善了用户体验,而且在配置相同的用户体验质量时,与最先进的互补滤波器相比,跟踪误差更小。
{"title":"Adaptive Complementary Filter for Hybrid Inside-Out Outside-In HMD Tracking With Smooth Transitions.","authors":"Riccardo Monica, Dario Lodi Rizzini, Jacopo Aleotti","doi":"10.1109/TVCG.2024.3464738","DOIUrl":"10.1109/TVCG.2024.3464738","url":null,"abstract":"<p><p>Head-mounted displays (HMDs) in room-scale virtual reality are usually tracked using inside-out visual SLAM algorithms. Alternatively, to track the motion of the HMD with respect to a fixed real-world reference frame, an outside-in instrumentation like a motion capture system can be adopted. However, outside-in tracking systems may temporarily lose tracking as they suffer by occlusion and blind spots. A possible solution is to adopt a hybrid approach where the inside-out tracker of the HMD is augmented with an outside-in sensing system. On the other hand, when the tracking signal of the outside-in system is recovered after a loss of tracking the transition from inside-out tracking to hybrid tracking may generate a discontinuity, i.e a sudden change of the virtual viewpoint, that can be uncomfortable for the user. Therefore, hybrid tracking solutions for HMDs require advanced sensor fusion algorithms to obtain a smooth transition. This work proposes a method for hybrid tracking of a HMD with smooth transitions based on an adaptive complementary filter. The proposed approach can be configured with several parameters that determine a trade-off between user experience and tracking error. A user study was carried out in a room-scale virtual reality environment, where users carried out two different tasks while multiple signal tracking losses of the outside-in sensor system occurred. The results show that the proposed approach improves user experience compared to a standard Extended Kalman Filter, and that tracking error is lower compared to a state-of-the-art complementary filter when configured for the same quality of user experience.</p>","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142304718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1109/TVCG.2024.3456395
Yu Qin, Brittany Terese Fasy, Carola Wenk, Brian Summa
Merge trees are a valuable tool in the scientific visualization of scalar fields; however, current methods for merge tree comparisons are computationally expensive, primarily due to the exhaustive matching between tree nodes. To address this challenge, we introduce the Merge Tree Neural Network (MTNN), a learned neural network model designed for merge tree comparison. The MTNN enables rapid and high-quality similarity computation. We first demonstrate how to train graph neural networks, which emerged as effective encoders for graphs, in order to produce embeddings of merge trees in vector spaces for efficient similarity comparison. Next, we formulate the novel MTNN model that further improves the similarity comparisons by integrating the tree and node embeddings with a new topological attention mechanism. We demonstrate the effectiveness of our model on real-world data in different domains and examine our model's generalizability across various datasets. Our experimental analysis demonstrates our approach's superiority in accuracy and efficiency. In particular, we speed up the prior state-of-the-art by more than 100× on the benchmark datasets while maintaining an error rate below 0.1%.
{"title":"Rapid and Precise Topological Comparison with Merge Tree Neural Networks.","authors":"Yu Qin, Brittany Terese Fasy, Carola Wenk, Brian Summa","doi":"10.1109/TVCG.2024.3456395","DOIUrl":"https://doi.org/10.1109/TVCG.2024.3456395","url":null,"abstract":"<p><p>Merge trees are a valuable tool in the scientific visualization of scalar fields; however, current methods for merge tree comparisons are computationally expensive, primarily due to the exhaustive matching between tree nodes. To address this challenge, we introduce the Merge Tree Neural Network (MTNN), a learned neural network model designed for merge tree comparison. The MTNN enables rapid and high-quality similarity computation. We first demonstrate how to train graph neural networks, which emerged as effective encoders for graphs, in order to produce embeddings of merge trees in vector spaces for efficient similarity comparison. Next, we formulate the novel MTNN model that further improves the similarity comparisons by integrating the tree and node embeddings with a new topological attention mechanism. We demonstrate the effectiveness of our model on real-world data in different domains and examine our model's generalizability across various datasets. Our experimental analysis demonstrates our approach's superiority in accuracy and efficiency. In particular, we speed up the prior state-of-the-art by more than 100× on the benchmark datasets while maintaining an error rate below 0.1%.</p>","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142304778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Text entry with word-gesture keyboards (WGK) is emerging as a popular method and becoming a key interaction for Extended Reality (XR). However, the diversity of interaction modes, keyboard sizes, and visual feedback in these environments introduces divergent word-gesture trajectory data patterns, thus leading to complexity in decoding trajectories into text. Template-matching decoding methods, such as SHARK2 [32], are commonly used for these WGK systems because they are easy to implement and configure. However, these methods are susceptible to decoding inaccuracies for noisy trajectories. While conventional neural-network-based decoders (neural decoders) trained on word-gesture trajectory data have been proposed to improve accuracy, they have their own limitations: they require extensive data for training and deep-learning expertise for implementation. To address these challenges, we propose a novel solution that combines ease of implementation with high decoding accuracy: a generalizable neural decoder enabled by pre-training on large-scale coarsely discretized word-gesture trajectories. This approach produces a ready-to-use WGK decoder that is generalizable across mid-air and on-surface WGK systems in augmented reality (AR) and virtual reality (VR), which is evident by a robust average Top-4 accuracy of 90.4% on four diverse datasets. It significantly outperforms SHARK2 with a 37.2% enhancement and surpasses the conventional neural decoder by 7.4%. Moreover, the Pre-trained Neural Decoder's size is only 4 MB after quantization, without sacrificing accuracy, and it can operate in real-time, executing in just 97 milliseconds on Quest 3.
{"title":"Gesture2Text: A Generalizable Decoder for Word-Gesture Keyboards in XR Through Trajectory Coarse Discretization and Pre-Training","authors":"Junxiao Shen;Khadija Khaldi;Enmin Zhou;Hemant Bhaskar Surale;Amy Karlson","doi":"10.1109/TVCG.2024.3456198","DOIUrl":"10.1109/TVCG.2024.3456198","url":null,"abstract":"Text entry with word-gesture keyboards (WGK) is emerging as a popular method and becoming a key interaction for Extended Reality (XR). However, the diversity of interaction modes, keyboard sizes, and visual feedback in these environments introduces divergent word-gesture trajectory data patterns, thus leading to complexity in decoding trajectories into text. Template-matching decoding methods, such as SHARK2 [32], are commonly used for these WGK systems because they are easy to implement and configure. However, these methods are susceptible to decoding inaccuracies for noisy trajectories. While conventional neural-network-based decoders (neural decoders) trained on word-gesture trajectory data have been proposed to improve accuracy, they have their own limitations: they require extensive data for training and deep-learning expertise for implementation. To address these challenges, we propose a novel solution that combines ease of implementation with high decoding accuracy: a generalizable neural decoder enabled by pre-training on large-scale coarsely discretized word-gesture trajectories. This approach produces a ready-to-use WGK decoder that is generalizable across mid-air and on-surface WGK systems in augmented reality (AR) and virtual reality (VR), which is evident by a robust average Top-4 accuracy of 90.4% on four diverse datasets. It significantly outperforms SHARK2 with a 37.2% enhancement and surpasses the conventional neural decoder by 7.4%. Moreover, the Pre-trained Neural Decoder's size is only 4 MB after quantization, without sacrificing accuracy, and it can operate in real-time, executing in just 97 milliseconds on Quest 3.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142304741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shape is commonly used to distinguish between categories in multi-class scatterplots. However, existing guidelines for choosing effective shape palettes rely largely on intuition and do not consider how these needs may change as the number of categories increases. Unlike color, shapes can not be represented by a numerical space, making it difficult to propose general guidelines or design heuristics for using shape effectively. This paper presents a series of four experiments evaluating the efficiency of 39 shapes across three tasks: relative mean judgment tasks, expert preference, and correlation estimation. Our results show that conventional means for reasoning about shapes, such as filled versus unfilled, are insufficient to inform effective palette design. Further, even expert palettes vary significantly in their use of shape and corresponding effectiveness. To support effective shape palette design, we developed a model based on pairwise relations between shapes in our experiments and the number of shapes required for a given design. We embed this model in a palette design tool to give designers agency over shape selection while incorporating empirical elements of perceptual performance captured in our study. Our model advances understanding of shape perception in visualization contexts and provides practical design guidelines that can help improve categorical data encodings.
{"title":"Shape It Up: An Empirically Grounded Approach for Designing Shape Palettes.","authors":"Chin Tseng, Arran Zeyu Wang, Ghulam Jilani Quadri, Danielle Albers Szafir","doi":"10.1109/TVCG.2024.3456385","DOIUrl":"https://doi.org/10.1109/TVCG.2024.3456385","url":null,"abstract":"<p><p>Shape is commonly used to distinguish between categories in multi-class scatterplots. However, existing guidelines for choosing effective shape palettes rely largely on intuition and do not consider how these needs may change as the number of categories increases. Unlike color, shapes can not be represented by a numerical space, making it difficult to propose general guidelines or design heuristics for using shape effectively. This paper presents a series of four experiments evaluating the efficiency of 39 shapes across three tasks: relative mean judgment tasks, expert preference, and correlation estimation. Our results show that conventional means for reasoning about shapes, such as filled versus unfilled, are insufficient to inform effective palette design. Further, even expert palettes vary significantly in their use of shape and corresponding effectiveness. To support effective shape palette design, we developed a model based on pairwise relations between shapes in our experiments and the number of shapes required for a given design. We embed this model in a palette design tool to give designers agency over shape selection while incorporating empirical elements of perceptual performance captured in our study. Our model advances understanding of shape perception in visualization contexts and provides practical design guidelines that can help improve categorical data encodings.</p>","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142304782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose HaptoFloater, a low-latency mid-air visuo-haptic augmented reality (VHAR) system that utilizes imperceptible color vibrations. When adding tactile stimuli to the visual information of a mid-air image, the user should not perceive the latency between the tactile and visual information. However, conventional tactile presentation methods for mid-air images, based on camera-detected fingertip positioning, introduce latency due to image processing and communication. To mitigate this latency, we use a color vibration technique; humans cannot perceive the vibration when the display alternates between two different color stimuli at a frequency of 25 Hz or higher. In our system, we embed this imperceptible color vibration into the mid-air image formed by a micromirror array plate, and a photodiode on the fingertip device directly detects this color vibration to provide tactile stimulation. Thus, our system allows for the tactile perception of multiple patterns on a mid-air image in 59.5 ms. In addition, we evaluate the visual-haptic delay tolerance on a mid-air display using our VHAR system and a tactile actuator with a single pattern and faster response time. The results of our user study indicate a visual-haptic delay tolerance of 110.6 ms, which is considerably larger than the latency associated with systems using multiple tactile patterns.
{"title":"HaptoFloater: Visuo-Haptic Augmented Reality by Embedding Imperceptible Color Vibration Signals for Tactile Display Control in a Mid-Air Image","authors":"Rina Nagano;Takahiro Kinoshita;Shingo Hattori;Yuichi Hiroi;Yuta Itoh;Takefumi Hiraki","doi":"10.1109/TVCG.2024.3456175","DOIUrl":"10.1109/TVCG.2024.3456175","url":null,"abstract":"We propose HaptoFloater, a low-latency mid-air visuo-haptic augmented reality (VHAR) system that utilizes imperceptible color vibrations. When adding tactile stimuli to the visual information of a mid-air image, the user should not perceive the latency between the tactile and visual information. However, conventional tactile presentation methods for mid-air images, based on camera-detected fingertip positioning, introduce latency due to image processing and communication. To mitigate this latency, we use a color vibration technique; humans cannot perceive the vibration when the display alternates between two different color stimuli at a frequency of 25 Hz or higher. In our system, we embed this imperceptible color vibration into the mid-air image formed by a micromirror array plate, and a photodiode on the fingertip device directly detects this color vibration to provide tactile stimulation. Thus, our system allows for the tactile perception of multiple patterns on a mid-air image in 59.5 ms. In addition, we evaluate the visual-haptic delay tolerance on a mid-air display using our VHAR system and a tactile actuator with a single pattern and faster response time. The results of our user study indicate a visual-haptic delay tolerance of 110.6 ms, which is considerably larger than the latency associated with systems using multiple tactile patterns.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Realistic simulation for miniature control interactions, typically identified by precise and confined motions, commonly found in precision hand-held tools, like calipers, powered engravers, retractable knives, etc., are beneficial for skill training associated with these kinds of tools in virtual reality (VR) environments. However, existing approaches aiming to simulate hand-held tools' miniature control manipulation experiences in VR entail prototyping complexity and require expertise, posing challenges for novice users and individuals with limited resources. Addressing this challenge, we introduce MobiTangibles—proxies for precision hand-held tools' miniature control interactions utilizing smartphone-based magnetic field sensing. MobiTangibles passively replicate fundamental miniature control experiences associated with hand-held tools, such as single-axis translation and rotation, enabling quick and easy use for diverse VR scenarios without requiring extensive technical knowledge. We conducted a comprehensive technical evaluation to validate the functionality of MobiTangibles across diverse settings, including evaluations for electromagnetic interference within indoor environments. In a user-centric evaluation involving 15 participants across bare hands, VR controllers, and MobiTangibles conditions, we further assessed the quality of miniaturized manipulation experiences in VR. Our findings indicate that MobiTangibles outperformed conventional methods in realism and fatigue, receiving positive feedback.
{"title":"MobiTangibles: Enabling Physical Manipulation Experiences of Virtual Precision Hand-Held Tools' Miniature Control in VR","authors":"Abhijeet Mishra;Harshvardhan Singh;Aman Parnami;Jainendra Shukla","doi":"10.1109/TVCG.2024.3456191","DOIUrl":"10.1109/TVCG.2024.3456191","url":null,"abstract":"Realistic simulation for miniature control interactions, typically identified by precise and confined motions, commonly found in precision hand-held tools, like calipers, powered engravers, retractable knives, etc., are beneficial for skill training associated with these kinds of tools in virtual reality (VR) environments. However, existing approaches aiming to simulate hand-held tools' miniature control manipulation experiences in VR entail prototyping complexity and require expertise, posing challenges for novice users and individuals with limited resources. Addressing this challenge, we introduce MobiTangibles—proxies for precision hand-held tools' miniature control interactions utilizing smartphone-based magnetic field sensing. MobiTangibles passively replicate fundamental miniature control experiences associated with hand-held tools, such as single-axis translation and rotation, enabling quick and easy use for diverse VR scenarios without requiring extensive technical knowledge. We conducted a comprehensive technical evaluation to validate the functionality of MobiTangibles across diverse settings, including evaluations for electromagnetic interference within indoor environments. In a user-centric evaluation involving 15 participants across bare hands, VR controllers, and MobiTangibles conditions, we further assessed the quality of miniaturized manipulation experiences in VR. Our findings indicate that MobiTangibles outperformed conventional methods in realism and fatigue, receiving positive feedback.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}