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Pacemod: parametric contour-based modifications for glyph generation

visual COMPUTER 2024年第5期40卷 3371-3384页

作者： Presnov, Dmitri Berels, Marlena Kolb, Andreas Univ Siegen Comp Graph & Multimedia Syst Grp Siegen Germany

Metaphoric glyphs are intuitively related to the underlying problem domain and enhance the readability and learnability of the resulting visualization. Their construction, however, implies an appropriate modification of the base icon, which is a predominantly manual process. In this paper, we introduce the parametric contour-based modification (PACEMOD) approach that lays the foundations of automated, controllable icon manipulations. Technically, the PACEMOD parametric representation utilizes diffusion curves, enriched with new degrees of freedom in arc-length parameterization, which allows for manipulation of the icon contours' geometry and the related color attributes. Moreover, we propose an implementation of our generic approach for a specific, automated design of metaphoric glyphs, based on periodic, wave-like contour modifications. Finally, the practicality of such periodic contour modifications is demonstrated by two visualization examples, which comprise uncertainty visualization of a rain forecast and gradient glyphs applied to COVID-19 data. In summary, with the PACEMOD approach we introduce an instrument that facilitates a user-centered design of metaphoric glyphs and provides a generic basis for potential further implementations according to specific applications.

关键词： Glyphs Icon-based visualization Contour modification visualization design visual encoding

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A Comparative Study on Deep CNN visual Encoders for Image Captioning 1

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8th International Conference on Computer Vision and Image Processing (CVIP)

作者： Arun, M. Arivazhagan, S. Harinisri, R. Raghavi, P. S. Mepco Schlenk Engn Coll Dept Elect & Commun Engn Sivakasi Tamil Nadu India

ISBN: (数字)9783031585357

ISBN: (纸本)9783031585340;9783031585357

Captioning an image is the process of describing it with syntactically and semantically meaningful terms. An image caption generator is developed by the integration of computer vision and natural language processing technology. Despite the fact that numerous techniques for generating image captions have been developed, the result is inadequate and the need for research in this area is still a demanding topic. The human process of describing any image is by seeing, focusing and captioning, which is equivalent that of feature representation, visual encoding and language generation for the image captioning systems. This study presents the construction of a simple deep learning-based image captioning model and investigates the efficacy of different visual encoding methods employed in the model. We have analyzed and compared the performance of six different pre-trained CNN visual encoding models using Bilingual Evaluation Understudy (BLEU) scores.

关键词： Image Captioning Flickr8K visual encoding BLEU Flickr30K

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visualizing qualitative data: unpacking the complexities and nuances of technology-supported learning processes

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ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT 2024年第5期72卷 2705-2723页

作者： Jiang, Shiyan Huang, Joey Lee, Hollylynne S. North Carolina State Univ Learning Design and Technol Raleigh NC 27606 USA North Carolina State Univ Raleigh NC USA Univ Calif Irvine Creativ Lab Irvine CA USA North Carolina State Univ Dept Teacher Educ & Learning Sci Poe Hall 208 2310 Stinson Dr Raleigh NC 27695 USA

Analyzing qualitative data from learning processes is considered "messy" and time consuming (Chi in J Learn Sci 6(3):271-315, 1997). It is often challenging to summarize and synthesize such data in a manner that conveys the richness and complexity of learning processes in a clear and concise manner. Moreover, qualitative data often contains patterns that are not immediately apparent. Consequently, visualization can be an effective tool for representing and unpacking the complexities and multidimensions of learning processes. Additionally, visualizations provide a time-efficient approach to analyzing data and a high-level view of the learning process over time for researchers to zoom in on intriguing moments and patterns (Huang et al. in Comput Human Behav 87:480-492, 2018). In this conceptual paper, we provide a broad overview of research in the field of visualizing qualitative data and discuss two studies (1) visualizing role-changing patterns in an interdisciplinary learning environment and (2) operationalizing collaborative computational thinking practices via visualization. By leveraging these studies, we aim to demonstrate a visualization processing flow along with qualitative research and methods. Particularly, the processing flow includes three critical elements: research subjectivity, complexity of visual encoding, and purpose of visual encoding. The discussion highlights the iterative and creative nature of the visualization technique. Furthermore, we discuss the benefits, challenges, and limitations of using visualization in the context of qualitative studies.

关键词： Data visualization Qualitative data Learning processes visual encoding

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Enhancing Static Charts With Data-Driven Animations

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2022年第7期28卷 2628-2640页

作者： Lu, Min Fish, Noa Wang, Shuaiqi Lanir, Joel Cohen-Or, Daniel Huang, Hui Shenzhen Univ Shenzhen 518060 Peoples R China Univ Haifa IL-3498838 Haifa Israel Tel Aviv Univ IL-6997801 Tel Aviv Israel

Static visual attributes such as color and shape are used with great success in visual charts designed to be displayed in static, hard-copy form. However, nowadays digital displays become ubiquitous in the visualization of any form of data, lifting the confines of static presentations. In this article, we propose incorporating data-driven animations to bring static charts to life, with the purpose of encoding and emphasizing certain attributes of the data. We lay out a design space for data-driven animated effects and experiment with three versatile effects, marching ants, geometry deformation and gradual appearance. For each, we provide practical details regarding their mode of operation and extent of interaction with existing visual encodings. We examine the impact and effectiveness of our enhancements through an empirical user study to assess preference as well as gauge the influence of animated effects on human perception in terms of speed and accuracy of visual understanding.

关键词： visualization Animation Data visualization encoding Image color analysis visual effects Task analysis visual encoding data-driven animated effects charts

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Reconstructing natural images from human fMRI by alternating encoding and decoding with shared autoencoder regularization

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期73卷 103397-103397页

作者： Qiao, Kai Chen, Jian Wang, Linyuan Zhang, Chi Tong, Li Yan, Bin PLA Strategy Support Force Informat Engn Univ Henan Key Lab Imaging & Intelligence Proc Zhengzhou 450001 Peoples R China

Reconstructing the viewed natural images from the corresponding functional magnetic resonance imaging (fMRI) of human visual cortices is extremely difficult. Utilizing deep learning techniques, the quality of reconstructed images can be significantly improved. This however is subject to availability of sufficient number of pair samples, which is not currently the case. In this study, we propose to perform alternating encoding and decoding to realize the mutual promotion of both based on shared semi-supervised learning and accomplish better reconstruction of natural images from the corresponding fMRI voxels. In our proposed method, the encoder and decoder are used to respectively map the images to the fMRI voxels (visual encoding), and the fMRI voxels to the images (visual reconstruction). We argue that combining the encoder and decoder in different sequences can form two converse and shared autoencoders to regularize the supervised learning of both through the unsupervised learning of fMRI voxels and images. More importantly, we alternatingly train the encoder and decoder with shared autoencoder regularization. Here, the premise is that a better encoder can produce a better decoder, and vice versa. The pixel level identification of the proposed method achieves up to 89.5%, which indicates at least 2% superiority and is the new state-of-the-art compared to the previous works in terms of the image reconstruction performance.

关键词： fMRI visual reconstruction visual encoding Shared autoencoder Alternating optimization

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Context Matters: A Theory of Semantic Discriminability for Perceptual encoding Systems

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2022年第1期28卷 697-706页

作者： Mukherjee, Kushin Yin, Brian Sherman, Brianne E. Lessard, Laurent Schloss, Karen B. Univ Wisconsin Madison Psychol Madison WI 53706 USA Univ Wisconsin Madison Wisconsin Inst Discovery Madison WI 53706 USA Univ Calif Berkeley Cognit Sci Berkeley CA 94720 USA Univ Wisconsin Madison Neurobiol Madison WI USA Northeastern Univ Mech & Ind Engn Boston MA 02115 USA

People's associations between colors and concepts influence their ability to interpret the meanings of colors in information visualizations. Previous work has suggested such effects are limited to concepts that have strong, specific associations with colors. However, although a concept may not be strongly associated with any colors, its mapping can be disambiguated in the context of other concepts in an encoding system. We articulate this view in semantic discriminability theory, a general framework for understanding conditions determining when people can infer meaning from perceptual features. Semantic discriminability is the degree to which observers can infer a unique mapping between visual features and concepts. Semantic discriminability theory posits that the capacity for semantic discriminability for a set of concepts is constrained by the difference between the feature-concept association distributions across the concepts in the set. We define formal properties of this theory and test its implications in two experiments. The results show that the capacity to produce semantically discriminable colors for sets of concepts was indeed constrained by the statistical distance between color-concept association distributions (Experiment 1). Moreover, people could interpret meanings of colors in bar graphs insofar as the colors were semantically discriminable, even for concepts previously considered "non-colorable" (Experiment 2). The results suggest that colors are more robust for visual communication than previously thought.

关键词： visual Reasoning Information visualization visual Communication visual encoding Color Cognition

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The Influence of Active and Passive Motion Experience on Infants' visual Prediction Ability

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PERCEPTUAL AND MOTOR SKILLS 2023年第1期130卷 170-190页

作者： Gehb, Gloria Jovanovic, Bianca Kelch, Amanda Schwarzer, Gudrun Justus Liebig Univ Giessen Dept Dev Psychol Giessen Germany Justus Liebig Univ Giessen Otto Behaghel Str 10F D-35394 Giessen Germany

In the present study we examined whether infants' visual prediction abilities were related to different types of motion experiences. We tested 30 6-month-old infants on a visual-spatial prediction task in which they had to visually anticipate the locational reappearance of temporarily occluded moving objects. We assigned infants to one of three experience groups: active locomotion training, passive motion experience, and a no-training control group. We tested the infants' visual prediction abilities before and after these trainings. We found improved infant predictions at a post-training test only for passively trained infants (p = .015, d = -1.033;Bonferroni corrected). Thus, we conclude that infants' visual-spatial predictions of temporally occluded moving objects was facilitated by mere movement experience, even if passive. visual information gathered during even passive movement seemed sufficient for visual prediction.

关键词： visual prediction locomotion training visual encoding infancy motor development

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Modeling the Influence of visual Density on Cluster Perception in Scatterplots Using Topology

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2021年第2期27卷 1829-1839页

作者： Quadri, Ghulam Jilani Rosen, Paul Univ S Florida Tampa FL 33620 USA

Scatterplots are used for a variety of visual analytics tasks, including cluster identification, and the visual encodings used on a scatterplot play a deciding role on the level of visual separation of clusters. For visualization designers, optimizing the visual encodings is crucial to maximizing the clarity of data. This requires accurately modeling human perception of cluster separation, which remains challenging. We present a multi-stage user study focusing on four factors-distribution size of clusters, number of points, size of points, and opacity of points-that influence cluster identification in scatterplots. From these parameters, we have constructed two models, a distance-based model, and a density-based model, using the merge tree data structure from Topological Data Analysis. Our analysis demonstrates that these factors play an important role in the number of clusters perceived, and it verifies that the distance-based and density-based models can reasonably estimate the number of clusters a user observes. Finally, we demonstrate how these models can be used to optimize visual encodings on real-world data.

关键词： visualization Task analysis Data models encoding Data visualization Analytical models Computational modeling Scatterplot clustering perception empirical evaluation visual encoding crowdsourcing topological data analysis

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Semantic Discriminability for visual Communication

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2021年第2期27卷 1022-1031页

作者： Schloss, Karen B. Leggon, Zachary Lessard, Laurent Univ Wisconsin Madison Psychol & Wisconsin Inst Discovery Madison WI 53706 USA Univ Wisconsin Madison Biol & Wisconsin Inst Discovery Madison WI 53706 USA Northeastern Univ Mech & Ind Engn Boston MA 02115 USA

To interpret information visualizations, observers must determine how visual features map onto concepts. First and foremost, this ability depends on perceptual discriminability;observers must be able to see the difference between different colors for those colors to communicate different meanings. However, the ability to interpret visualizations also depends on semantic discriminability, the degree to which observers can infer a unique mapping between visual features and concepts, based on the visual features and concepts alone (i.e., without help from verbal cues such as legends or labels). Previous evidence suggested that observers were better at interpreting encoding systems that maximized semantic discriminability (maximizing association strength between assigned colors and concepts while minimizing association strength between unassigned colors and concepts), compared to a system that only maximized color-concept association strength. However, increasing semantic discriminability also resulted in increased perceptual distance, so it is unclear which factor was responsible for improved performance. In the present study, we conducted two experiments that tested for independent effects of semantic distance and perceptual distance on semantic discriminability of bar graph data visualizations. Perceptual distance was large enough to ensure colors were more than just noticeably different. We found that increasing semantic distance improved performance, independent of variation in perceptual distance, and when these two factors were uncorrelated, responses were dominated by semantic distance. These results have implications for navigating trade-offs in color palette design optimization for visual communication.

关键词： visual Reasoning Information visualization visual Communication visual encoding Color Perception Color Cognition

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Eye blinks as a visual processing stage

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2024年第15期121卷 e2310291121-e2310291121页

作者： Yang, Bin Intoy, Janis Rucci, Michele Univ Rochester Dept Brain & Cognit Sci Rochester NY 14627 USA Univ Rochester Ctr Visual Sci Rochester NY 14627 USA

Humans blink their eyes frequently during normal viewing, more often than it seems necessary for keeping the cornea well lubricated. Since the closure of the eyelid disrupts the image on the retina, eye blinks are commonly assumed to be detrimental to visual processing. However, blinks also provide luminance transients rich in spatial information to neural pathways highly sensitive to temporal changes. Here, we report that the luminance modulations from blinks enhance visual sensitivity. By coupling high -resolution eye tracking in human observers with modeling of blink transients and spectral analysis of visual input signals, we show that blinking increases the power of retinal stimulation and that this effect significantly enhances visibility despite the time lost in exposure to the external scene. We further show that, as predicted from the spectral content of input signals, this enhancement is selective for stimuli at low spatial frequencies and occurs irrespective of whether the luminance transients are actively generated or passively experienced. These findings indicate that, like eye movements, blinking acts as a computational component of a visual processing strategy that uses motor behavior to reformat spatial information into the temporal domain.

关键词： spatial vision visual encoding fixational eye movements saccade

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