30,739 research outputs found

    Objectivity and representative practices across artistic and scientific visualization

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    This chapter highlights the story of how artists participated in the practices of observation that Lorraine Daston and Peter Galison compellingly define as collective empiricism. It shows the history of scientific objectivity has constantly crossed paths with the history of artistic visualization, from which it has received some powerful challenges. Historicizing the category of representation also has the advantage of reinforcing its vital connection with visualizational connection that is rarely addressed in current philosophical discussions. Distinctive of twentieth-century image- making, trained judgment was a reaction to the constraints imposed by mechanical reproducibility. In the age of computerization, visualization challenges the boundaries between the artifactual and the natural: The new scientific images fulfill the purpose of manipulating the realand they do so in an aesthetically pleasing way. Callanan's artwork is a physical visualization of real-time raw scientific data

    Data Brushes: Interactive Style Transfer for Data Art

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    Interactive Extraction of High-Frequency Aesthetically-Coherent Colormaps

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    Color transfer functions (i.e. colormaps) exhibiting a high frequency luminosity component have proven to be useful in the visualization of data where feature detection or iso-contours recognition is essential. Having these colormaps also display a wide range of color and an aesthetically pleasing composition holds the potential to further aid image understanding and analysis. However producing such colormaps in an efficient manner with current colormap creation tools is difficult. We hereby demonstrate an interactive technique for extracting colormaps from artwork and pictures. We show how the rich and careful color design and dynamic luminance range of an existing image can be gracefully captured in a colormap and be utilized effectively in the exploration of complex datasets

    Exploring Artistic Learning Through the Creation of Tunnel Books

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    This paper discusses the results of an autoethnographic, arts-based study that explored artistic learning through the creation of tunnel books. The researcher, an art teacher at a private Catholic school, chose the Painted Churches of Schulenburg, TX as the subject of the tunnel books. During the study, the researcher toured four historic churches to obtain information that guided design decisions for the tunnel books. Documentation of the tour was done through photography, video recording, and note taking. Interviews with Katherine Ruffin, book arts professor, and Rand Huebsch, printmaker, book artist, and teacher were conducted to obtain information about construction techniques, materials, and adhesives. Four tunnel books were created. During the process, materials for the tunnel books, media, adhesives, and different assembly approaches were explored. Upon completion, the books were viewed by several individuals who attended the tour of the churches, and Dr. Ann Waltz, Director of the Art School for Children and Young Adults at the University of Houston in Clear Lake, TX. The study was designed to expand artistic learning and gain information that could later be shared with middle school students at St. Clare of Assisi Catholic School. The outcomes revealed new approaches to constructing tunnel books, and applications for new and familiar media. The paper concludes with future plans for the knowledge gained, along with advice for art educators, and the field of art education

    MoSculp: Interactive Visualization of Shape and Time

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    We present a system that allows users to visualize complex human motion via 3D motion sculptures---a representation that conveys the 3D structure swept by a human body as it moves through space. Given an input video, our system computes the motion sculptures and provides a user interface for rendering it in different styles, including the options to insert the sculpture back into the original video, render it in a synthetic scene or physically print it. To provide this end-to-end workflow, we introduce an algorithm that estimates that human's 3D geometry over time from a set of 2D images and develop a 3D-aware image-based rendering approach that embeds the sculpture back into the scene. By automating the process, our system takes motion sculpture creation out of the realm of professional artists, and makes it applicable to a wide range of existing video material. By providing viewers with 3D information, motion sculptures reveal space-time motion information that is difficult to perceive with the naked eye, and allow viewers to interpret how different parts of the object interact over time. We validate the effectiveness of this approach with user studies, finding that our motion sculpture visualizations are significantly more informative about motion than existing stroboscopic and space-time visualization methods.Comment: UIST 2018. Project page: http://mosculp.csail.mit.edu

    SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset

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    Visual complexity identifies the level of intricacy and details in an image or the level of difficulty to describe the image. It is an important concept in a variety of areas such as cognitive psychology, computer vision and visualization, and advertisement. Yet, efforts to create large, downloadable image datasets with diverse content and unbiased groundtruthing are lacking. In this work, we introduce Savoias, a visual complexity dataset that compromises of more than 1,400 images from seven image categories relevant to the above research areas, namely Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism. The images in each category portray diverse characteristics including various low-level and high-level features, objects, backgrounds, textures and patterns, text, and graphics. The ground truth for Savoias is obtained by crowdsourcing more than 37,000 pairwise comparisons of images using the forced-choice methodology and with more than 1,600 contributors. The resulting relative scores are then converted to absolute visual complexity scores using the Bradley-Terry method and matrix completion. When applying five state-of-the-art algorithms to analyze the visual complexity of the images in the Savoias dataset, we found that the scores obtained from these baseline tools only correlate well with crowdsourced labels for abstract patterns in the Suprematism category (Pearson correlation r=0.84). For the other categories, in particular, the objects and advertisement categories, low correlation coefficients were revealed (r=0.3 and 0.56, respectively). These findings suggest that (1) state-of-the-art approaches are mostly insufficient and (2) Savoias enables category-specific method development, which is likely to improve the impact of visual complexity analysis on specific application areas, including computer vision.Comment: 10 pages, 4 figures, 4 table

    3 case studies: a hybrid educational strategy for ART/SCI collaborations

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    In this paper we report on a transdisciplinary university course designed to bring together fine art/visual communication design and computer science students for the creation and implementation of collaborative visual/audio projects that draw upon the specialized knowledge of both these disciplines. While an overview of the syllabus and the teaching methodologies is undertaken in the introduction, the focus of the paper concentrates upon an in-depth discussion and analysis of 3 specific projects that were developed by 3 distinct teams of students comprised of one artist/designer and one engineer each
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