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National Research Institute for Earth Science and Disaster Resilience, Tsukuba, Japan
We analyzed strong-motion records at the ground and borehole in and around the Kanto Basin and the seafloor in the Japan Trench area from three nearby offshore earthquakes of similar magnitudes (Mw 5.8–5.9). The seafloor strong-motion records were obtained from S-net, which was established to enhance tsunami and earthquake early warnings after the 2011 great Tohoku-oki earthquake disaster. The borehole records were obtained from MeSO-net, a dense network of seismometers installed at a depth of 20 m in the Tokyo metropolitan area. The ground records were obtained from the K-NET and KiK-net networks, established after the 1995 great Hanshin-Awaji earthquake disaster. The MeSO-net and S-net stations record the shakings continuously, while the K-NET and KiK-net records are based on triggering thresholds. It is crucial to evaluate the properties of strong motions recorded by the S-net for earthquake
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Omnimatte: Associating Objects and Their Effects in Video
We pose a novel problem: automatically associating subjects in videos with ‘effects’ related to them in the scene. Given an input video (top) and rough masks of subjects of interest (middle), our method estimates an
omnimatte – an alpha matte and foreground color that includes the subject itself along with all scene elements associated with it (bottom). The associated elements can be other objects attached to the subject or moving with it, or complex effects such as shadows, reflections, smoke, or ripples the subject creates in water.
Abstract
Computer vision is increasingly effective at segmenting objects in images and videos; however, scene effects related to the objects shadows, reflections, generated smoke, etc are typically overlooked. Identifying such scene effects and associating them with the objects producing them is important for improving our fundamental understanding of visual scenes, and can also assis
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