2,999 research outputs found

    Texture to the Rescue : Practical Paper Fingerprinting based on Texture Patterns

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    In this article, we propose a novel paper fingerprinting technique based on analyzing the translucent patterns revealed when a light source shines through the paper. These patterns represent the inherent texture of paper, formed by the random interleaving of wooden particles during the manufacturing process. We show that these patterns can be easily captured by a commodity camera and condensed into a compact 2,048-bit fingerprint code. Prominent works in this area (Nature 2005, IEEE S&P 2009, CCS 2011) have all focused on fingerprinting paper based on the paper "surface." We are motivated by the observation that capturing the surface alone misses important distinctive features such as the noneven thickness, random distribution of impurities, and different materials in the paper with varying opacities. Through experiments, we demonstrate that the embedded paper texture provides a more reliable source for fingerprinting than features on the surface. Based on the collected datasets, we achieve 0% false rejection and 0% false acceptance rates. We further report that our extracted fingerprints contain 807 degrees of freedom (DoF), which is much higher than the 249 DoF with iris codes (that have the same size of 2,048 bits). The high amount of DoF for texturebased fingerprints makes our method extremely scalable for recognition among very large databases; it also allows secure usage of the extracted fingerprint in privacy-preserving authentication schemes based on error correction techniques

    Evaluation of Multimedia Fingerprinting Image

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    Recognizing the fingerprints of the Galactic bar: a quantitative approach to comparing model (l,v) distributions to observation

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    We present a new method for fitting simple hydrodynamical models to the (l,v) distribution of atomic and molecular gas observed in the Milky Way. The method works by matching features found in models and observations. It is based on the assumption that the large-scale features seen in (l,v) plots, such as ridgelines and the terminal velocity curve, are influenced primarily by the underlying large-scale Galactic potential and are only weakly dependent on local ISM heating and cooling processes. In our scheme one first identifies by hand the features in the observations: this only has to be done once. We describe a procedure for automatically extracting similar features from simple hydrodynamical models and quantifying the "distance" between each model's features and the observations. Application to models of the Galactic Bar region (|l|<30deg) shows that our feature-fitting method performs better than \chi^2 or envelope distances at identifying the correct underlying galaxy model.Comment: Accepted for publication in MNRA

    Optimizing MRFâ ASL scan design for precise quantification of brain hemodynamics using neural network regression

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    Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154517/1/mrm28051.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154517/2/mrm28051_am.pd
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