446 research outputs found

    Object Discovery via Cohesion Measurement

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    Color and intensity are two important components in an image. Usually, groups of image pixels, which are similar in color or intensity, are an informative representation for an object. They are therefore particularly suitable for computer vision tasks, such as saliency detection and object proposal generation. However, image pixels, which share a similar real-world color, may be quite different since colors are often distorted by intensity. In this paper, we reinvestigate the affinity matrices originally used in image segmentation methods based on spectral clustering. A new affinity matrix, which is robust to color distortions, is formulated for object discovery. Moreover, a Cohesion Measurement (CM) for object regions is also derived based on the formulated affinity matrix. Based on the new Cohesion Measurement, a novel object discovery method is proposed to discover objects latent in an image by utilizing the eigenvectors of the affinity matrix. Then we apply the proposed method to both saliency detection and object proposal generation. Experimental results on several evaluation benchmarks demonstrate that the proposed CM based method has achieved promising performance for these two tasks.Comment: 14 pages, 14 figure

    THz Nanoscopy of Metal and Gallium Implanted Silicon

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    Drude model successfully quantifies the optical constants for bulk matter, but it is not suitable for subwavelength objects. In this paper, terahertz near-field optical microscopy and finite element simulation are used to study gold patches fabricated by Gallium etching. Electron transport is discovered in determining the optical signal strength. The signal from substrate is more complicated and still not fully understood. As the etching area decreases, near-field interaction is not dominated by doping concentration, and a higher signal is observed near connected metals. With the help of simulation, the abnormal enhancement phenomenon is discussed in detail, which lays the foundation for further experimental verification

    The semi-geometric process and some properties

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    The geometric process has been widely applied in reliability engineering and other areas since its introduction. One of its assumptions is that the times between occurrences of events are independent. This assumption is rather restrictive and can limit its application in the real world. This paper extends the geometric process to a new process, which we call the semi-geometric process, by relaxing this assumption. Some probabilistic properties of the process are derived and parameter estimation is described. A numerical example, based on a real-world dataset, is used to illustrate the model and validate the estimation methodology
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