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Community detection is important for understanding networks. Previous studies observed that communities are not necessarily disjoint and might overlap. It is also agreed that some outlier vertices participate in no community, and some hubs in a community might take more important roles than others. Each of these facts has been independently addressed in previous work. But there is no algorithm, to our knowledge, that can identify these three structures altogether. To overcome this limitation, we propose a novel model where vertices are measured by their centrality in communities, and define the identification of overlapping communities, hubs, and outliers as an optimization problem, calculated by nonnegative matrix factorization. We test this method on various real networks, and compare it with several competing algorithms. The experimental results not only demonstrate its ability of identifying overlapping communities, hubs, and outliers, but also validate its superior performance in terms of clustering quality. [ABSTRACT FROM AUTHOR]

This paper presents an embedded omni-vision navigation system which involves landmark recognition, multi-object tracking, and vehicle localization. A new tracking algorithm, the feature matching embedded particle filter, is proposed. Landmark recognition is used to provide the front-end targets. A global localization method for omni-vision based on coordinate transformation is also proposed. The digital signal processor (DSP) provides a hardware platform for on-board tracker. Dynamic navigator employs DSP tracker to follow the landmarks in real time during the arbitrary movement of the vehicle and computes the position for localization based on time sequence images analysis. Experimental results demonstrated that the navigator can efficiently offer the vehicle guidance. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we describe how geometrically correct and visually realistic shadows may be computed for objects composited into a single view of a target scene. Compared to traditional single view compositing methods, which either do not deal with the shadow effects or manually create the shadows for the composited objects, our approach efficiently utilizes the geometric and photometric constraints extracted from a single target image to synthesize the shadows consistent with the overall target scene for the inserted objects. In particular, we explore (i) the constraints provided by imaged scene structure, e.g. vanishing points of orthogonal directions, for camera calibration and thus explicit determination of the locations of the camera and the light source; (ii) the relatively weaker geometric constraint, the planar homology, that models the imaged shadow relations when explicit camera calibration is not possible; and (iii) the photometric constraints that are required to match the color characteristics of the synthesized shadows with those of the original scene. For each constraint, we demonstrate the working examples followed by our observations. To show the accuracy and the applications of the proposed method, we present the results for a variety of target scenes, including footage from commercial Hollywood movies and 3D video games. [ABSTRACT FROM AUTHOR]

A correction to the article "Identifying overlapping communities as well as hubs and outliers via nonnegative matrix factorization" that was published in the October 21, 2013 issue is presented.