EG 2018 - Posters

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https://diglib.eg.org/handle/10.2312/2632161

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Browsing EG 2018 - Posters by Subject "Applied computing"

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    Growing Circles: A Region Growing Algorithm for Unstructured Grids and Non-aligned Boundaries
    (The Eurographics Association, 2018) Dabbaghchian, Saeed; Jain, Eakta and Kosinka, Jirí
    Detecting the boundaries of an enclosed region is a problem which arises in some applications such as the human upper airway modeling. Using of standard algorithms fails because of the inevitable errors, i.e. gaps and overlaps between the surrounding boundaries. Growing circles is an automatic approach to address this problem. A circle is centered inside the region and starts to grow by increasing its radius. Its growth is limited either by the surrounding boundaries or by reaching its maximum radius. To deal with complex shapes, many circles are used in which each circle partially reconstructs the region, and the whole region is determined by the union of these partial regions. The center of the circles and their maximum radius are calculated adaptively. It is similar to the region growing algorithm which is widely used in image processing applications. However, it works for unstructured grids as well as Cartesian ones. As an application of the method, it is applied to detect the boundaries of the upper airway cross-sections.
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    A Smart Palette for Helping Novice Painters to Mix Physical Watercolor Pigments
    (The Eurographics Association, 2018) Chen, Mei-Yun; Yang, Ci-Syuan; Ouhyoung, Ming; Jain, Eakta and Kosinka, Jirí
    For novice painters, color mixing is a necessary skill which takes many years to learn. To get the skill easily, we design a system, a smart palette, to help them learn quickly. Our system is based on physical watercolor pigments, and we use a spectrometer to measure the transmittance and reflectance of watercolor pigments and collect a color mixing dataset. Moreover, we use deep neural network (DNN) to train a color mixing model. After that, using the model to predict a large amount of color mixing data creates a lookup table for color matching. In the smart palette, users can select a target color from an input image; then, the smart palette will find the nearest color, which is a matched color, and show a recipe where two pigments and their respective quantities can be mixed to get that color.