5,896 research outputs found

    Certifying the Existence of Epipolar Matrices

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    Given a set of point correspondences in two images, the existence of a fundamental matrix is a necessary condition for the points to be the images of a 3-dimensional scene imaged with two pinhole cameras. If the camera calibration is known then one requires the existence of an essential matrix. We present an efficient algorithm, using exact linear algebra, for testing the existence of a fundamental matrix. The input is any number of point correspondences. For essential matrices, we characterize the solvability of the Demazure polynomials. In both scenarios, we determine which linear subspaces intersect a fixed set defined by non-linear polynomials. The conditions we derive are polynomials stated purely in terms of image coordinates. They represent a new class of two-view invariants, free of fundamental (resp.~essential)~matrices

    Deep Convolutional Ranking for Multilabel Image Annotation

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    Multilabel image annotation is one of the most important challenges in computer vision with many real-world applications. While existing work usually use conventional visual features for multilabel annotation, features based on Deep Neural Networks have shown potential to significantly boost performance. In this work, we propose to leverage the advantage of such features and analyze key components that lead to better performances. Specifically, we show that a significant performance gain could be obtained by combining convolutional architectures with approximate top-kk ranking objectives, as thye naturally fit the multilabel tagging problem. Our experiments on the NUS-WIDE dataset outperforms the conventional visual features by about 10%, obtaining the best reported performance in the literature

    Elucidation of the Origins of Stratospheric Sulfate Aerosols by Isotopic Methods

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    Stratospheric sulfate aerosols (SSA) play an important part in regulating the climate of the earth and in the maintenance of important stratospheric species, including stratospheric ozone. The sources of stratospheric sulfate aerosol sulfur remain an unresolved issue because of uncertainties in the global sulfur budget and model limitations. The origins of SSA particles should be reflected in their isotopic composition. In this thesis project, the sulfur isotopic fractionation factors of processes that produce stratospheric sulfate aerosols (SSA) were quantified using a variety of theoretical and experimental techniques. RRKM (unimolecular dissociation) theory was applied to compute the isotopic fractionation of the homogeneous oxidation of SO2 via OH radicals. The overall isotopic enrichment associated with the total OCS loss pathways in the stratosphere was determined by analyzing high resolution FT-IR data from balloon flights. The isotopic fractionation of the photolytic decomposition of OCS was estimated by measuring the absorption spectra of OCS sulfur isotopologues. We also measured the isotopic composition of stratospheric aerosols sampled during the period 1973-1974, in the course of the Department of Energy?s AIRSTREAM campaign. Combining our results with literature values of the sulfur isotopic composition of SSA precursors, we modeled the steady-state isotopic composition of sulfur compounds in the atmosphere using the JPL/Caltech 1-D chemical transport model. Our data supports the view that OCS and SO2 are both important in the maintenance of the background stratospheric sulfate aerosol layer

    Lateral transport of thermal capillary waves

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    We demonstrate that collective motion of interfacial fluctuations can occur at the interface between two coexisting thermodynamic phases. Based on computer simulation results for driven diffusive Ising and Blume-Capel models, we conjecture that the thermal capillary waves at a planar interface travel along the interface if the lateral order parameter current j_op(y) is an odd function of the distance y from the interface and hence possesses opposite directions in the two phases. Such motion does not occur if j_op(y) is an even function of y. A discrete Gaussian interface model with effective dynamics exhibits similiar transport phenomena but with a simpler dispersion relation. These findings open up avenues for controlled interfacial transport on the nanoscale.Comment: 4 pages, 6 figure

    Pose Embeddings: A Deep Architecture for Learning to Match Human Poses

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    We present a method for learning an embedding that places images of humans in similar poses nearby. This embedding can be used as a direct method of comparing images based on human pose, avoiding potential challenges of estimating body joint positions. Pose embedding learning is formulated under a triplet-based distance criterion. A deep architecture is used to allow learning of a representation capable of making distinctions between different poses. Experiments on human pose matching and retrieval from video data demonstrate the potential of the method
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