5,012 research outputs found

    Unsupervised feature learning by augmenting single images

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    When deep learning is applied to visual object recognition, data augmentation is often used to generate additional training data without extra labeling cost. It helps to reduce overfitting and increase the performance of the algorithm. In this paper we investigate if it is possible to use data augmentation as the main component of an unsupervised feature learning architecture. To that end we sample a set of random image patches and declare each of them to be a separate single-image surrogate class. We then extend these trivial one-element classes by applying a variety of transformations to the initial 'seed' patches. Finally we train a convolutional neural network to discriminate between these surrogate classes. The feature representation learned by the network can then be used in various vision tasks. We find that this simple feature learning algorithm is surprisingly successful, achieving competitive classification results on several popular vision datasets (STL-10, CIFAR-10, Caltech-101).Comment: ICLR 2014 workshop track submission (7 pages, 4 figures, 1 table

    Power-Based Direction-of-Arrival Estimation Using a Single Multi-Mode Antenna

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    Phased antenna arrays are widely used for direction-of-arrival (DoA) estimation. For low-cost applications, signal power or received signal strength indicator (RSSI) based approaches can be an alternative. However, they usually require multiple antennas, a single antenna that can be rotated, or switchable antenna beams. In this paper we show how a multi-mode antenna (MMA) can be used for power-based DoA estimation. Only a single MMA is needed and neither rotation nor switching of antenna beams is required. We derive an estimation scheme as well as theoretical bounds and validate them through simulations. It is found that power-based DoA estimation with an MMA is feasible and accurate

    Polarimetry of Water Ice Particles Providing Insights on Grain Size and Degree of Sintering on Icy Planetary Surfaces

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    The polarimetry of the light scattered by planetary surfaces is a powerful tool to provide constraints on their microstructure. To improve the interpretation of polarimetric data from icy surfaces, we have developed the POLarimeter for ICE Samples (POLICES) complementing the measurement facilities of the Ice Laboratory at the University of Bern. The new setup uses a high precision Stokes polarimeter to measure the degree of polarization in the visible light scattered by surfaces at moderate phase angles (from 1.5 to 30{\deg}). We present the photometric and polarimetric phase curves measured on various surfaces made of pure water ice particles having well-controlled size and shape (spherical, crushed, frost). The results show how the amplitude and the shape of the negative polarization branch change with the particles sizes and the degree of metamorphism of the ice. We found that fresh frost formed by water condensation on cold surfaces has a phase curve characterized by resonances (Mie oscillations) indicating that frost embryos are transparent micrometer-sized particles with a narrow size distribution and spherical shape. Comparisons of these measurements with polarimetric observations of the icy satellites of the Solar System suggest that Europa is possibly covered by relatively coarser (~40-400 {\mu}m) and more sintered grains than Enceladus and Rhea, more likely covered by frost-like particles of few micrometers in average. The great sensitivity of polarization to grain size and degree of sintering makes it an ideal tool to detect hints of ongoing processes on icy planetary surfaces, such as cryovolcanism.Comment: 36 pages, 1 table, 11 figures, 2 data sets, accepted in Journal of Geophysical Research: Planet
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