Browsing by Subject "perception"

Now showing 1 - 5 of 5
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    How Art Can Educate the Radiologist's Eye: Duchamp's “Nude Descending a Staircase”
    (Elsevier, 2018-01) Gunderman, Richard B.; Idahosa, Aimebenomon O.; Radiology and Imaging Sciences, School of Medicine
    Duchamp's “Nude Descending a Staircase, No. 2” was dubbed one of the most famous and controversial paintings of its day (1). Along with the cubist school of which it was a part, it helped to change the way artists and the public perceived art, and its influence persists down to the present day (2). Less known but no less notable is the fact that “Nude Descending” also offers important educational insights to radiologists, particularly regarding the daily work of radiologic interpretation.
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    Perception's Crucial Role in Radiology Education
    (Elsevier, 2019-01) Gunderman, Richard B.; Patel, Parth; Radiology and Imaging Sciences, School of Medicine
    Perception is at the core of what radiologists do every day. Almost by definition, fully qualified radiologists are very good at perceiving, at least when it comes to the detection and interpretation of radiological images of the human body. But could a more thorough understanding of perception—and in particular, how we learn to perceive—enable radiologists to find more joy in their work, further enhance their powers of perception, or teach radiology more effectively?
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    Rainbows Revisited: Modeling Effective Colormap Design for Graphical Inference
    (IEEE, 2020-10) Reda, Khairi; Albers Szafir, Danielle; Computer and Information Science, School of Science
    Color mapping is a foundational technique for visualizing scalar data. Prior literature offers guidelines for effective colormap design, such as emphasizing luminance variation while limiting changes in hue. However, empirical studies of color are largely focused on perceptual tasks. This narrow focus inhibits our understanding of how generalizable these guidelines are, particularly to tasks like visual inference that require synthesis and judgement across multiple percepts. Furthermore, the emphasis on traditional ramp designs (e.g., sequential or diverging) may sideline other key metrics or design strategies. We study how a cognitive metric-color name variation-impacts people's ability to make model-based judgments. In two graphical inference experiments, participants saw a series of color-coded scalar fields sampled from different models and assessed the relationships between these models. Contrary to conventional guidelines, participants were more accurate when viewing colormaps that cross a variety of uniquely nameable colors. We modeled participants' performance using this metric and found that it provides a better fit to the experimental data than do existing design principles. Our findings indicate cognitive advantages for colorful maps like rainbow, which exhibit high color categorization, despite their traditionally undesirable perceptual properties. We also found no evidence that color categorization would lead observers to infer false data features. Our results provide empirically grounded metrics for predicting a colormap's performance and suggest alternative guidelines for designing new quantitative colormaps to support inference. The data and materials for this paper are available at: https://osf.io/tck2r/.
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    Real-time Illumination and Visual Coherence for Photorealistic Augmented/Mixed Reality
    (ACM, 2021-05) Alhakamy, A'aeshah; Tuceryan, Mihran; Computer and Information Science, School of Science
    A realistically inserted virtual object in the real-time physical environment is a desirable feature in augmented reality (AR) applications and mixed reality (MR) in general. This problem is considered a vital research area in computer graphics, a field that is experiencing ongoing discovery. The algorithms and methods used to obtain dynamic and real-time illumination measurement, estimating, and rendering of augmented reality scenes are utilized in many applications to achieve a realistic perception by humans. We cannot deny the powerful impact of the continuous development of computer vision and machine learning techniques accompanied by the original computer graphics and image processing methods to provide a significant range of novel AR/MR techniques. These techniques include methods for light source acquisition through image-based lighting or sampling, registering and estimating the lighting conditions, and composition of global illumination. In this review, we discussed the pipeline stages with the details elaborated about the methods and techniques that contributed to the development of providing a photo-realistic rendering, visual coherence, and interactive real-time illumination results in AR/MR.
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    What Americans Think About Philanthropy and Nonprofits
    (2023-04) School of Philanthropy, Lilly Family