Motion segmentation of multiple translating objects using line correspondences
We present an algebraic approach to multibody motion segmentation from line correspondences. Given three perspective views containing multiple linearly moving objects, we demonstrate that after applying a polynomial embedding to the line correspondences, they became related by the so-called multibody line constraint of translational motions. We show how to linearly estimate the multibody trifocal epipole from line-line-line correspondences. The individual trifocal epipoles are then obtained from the derivatives of the multibody line constraint (up to an unknown factor). Given normalized trifocal epipoles, we can use any special clustering technique to obtain the clustering of the motions and the correspondences. The limitations of the proposed algorithm are also discussed. Experimental results on synthetic and real dynamic scenes are presented.

title: Camera Calibration from a Single Frame of Planar Pattern
abstract: A method to calibrate camera from a single frame of planar pattern is presented in this paper. For a camera model with four intrinsic parameters and visible lens distortion, the principal point and distortion coefficients are firstly determined through analysis of the distortion in an image. The distortion is then removed. Finally, the other intrinsic and extrinsic parameters of the camera are obtained through direct linear transform followed by bundle adjustment. Theoretically, the method makes it possible to analyze the calibration result at the level of a single frame. Practically, such a method provides a easy way to calibrate a camera used in industrial vision system on line and used in desktop vision system. Experimental results of both simulated data and real images validate the method.

title: Optimized routing for fault management in optical burst-switched WDM networks
abstract: Optical burst switching (OBS) is a promising technique for supporting high-capacity, bursty data traffic over optical wavelength-division-multiplexed (WDM) networks. An optical link failure may result in a huge amount of data (and revenue) loss, and it has been an important survivability concern in optical networks. In this paper, we study the fault- management issues related with a link failure in an OBS network. We propose to use pre-planned global rerouting to balance network load and to reroute bursts after a link fails. We apply optimization techniques to pre-plan explicit backup routes for failure scenarios. Our objective is to achieve optimal load balancing both before and after a failure such that the network state can still remain stable with minimum burst-loss probability when a failure occurs. We apply the pre-planned normal and backup routing tables to an OBS network, and study the network performance after a failure occur using illustrative numerical examples. The results show that the average burst-loss probability can be significantly reduced by 60% - from an average of 0.10 to 0.04 (when the normalized link load is less than 0.5) using globally-rerouted backup routes, when compared with the scheme without global rerouting. We also observe that the burst-loss probability is reduced by 43% - from an average of 0.07 to 0.04 (when the link load is less than 0.5) if the rerouting is done using optimization techniques, when compared with shortest-path routing.for Fault Management

title: Name Disambiguation Using Atomic Clusters
abstract: Name ambiguity is a critical problem in many applications, in particular in the online bibliography systems, such as DBLP and CiteSeer. Previously, several clustering based methods have been proposed although, the problem still presents to be a big challenge for both research and industry communities. In this paper, we present a complementary study to the problem from another point of view. We propose an approach of finding atomic clusters to improve the performance of existing clustering-based methods. We conducted experiments on a dataset from a real-world system: Arnetminer.org. Experiments results show that significant improvements can be obtained by using the proposed atomic clusters finding approach (about +8% and +27% improvements depending on different clustering methods).

title: ArnetMiner: extraction and mining of academic social networks
abstract: This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.

title: Simplifying the Reconstruction of 3D Models using Parameter Elimination
abstract: Reconstructing large models from images is a significant challenge for computer vision, computer graphics, and related fields. In this paper, we present an approach for simplifying the reconstruction process by mathematically eliminating external camera parameters. This results in less parameters to estimate and in an overall significantly more robust and accurate reconstruction. We reformulate the problem in such a manner as to be able to identify invariants, eliminate superfluous parameters, and measure the performance of our formulation under various conditions. We compare a two-step camera orientation-free method, where the majority of the points are reconstructed using a linear equation set, and a camera position-and- orientation free method, using a degree-two equation set. Both approaches use a full perspective camera and are applied to synthetic and real-world datasets.

title: Clustering in Dynamic Spatial Databases
abstract: Efficient clustering in dynamic spatial databases is currently an open problem with many potential applications. Most traditional spatial clustering algorithms are inadequate because they do not have an efficient support for incremental clustering.In this paper, we propose DClust, a novel clustering technique for dynamic spatial databases. DClust is able to provide multi-resolution view of the clusters, generate arbitrary shapes clusters in the presence of noise, generate clusters that are insensitive to ordering of input data and support incremental clustering efficiently. DClust utilizes the density criterion that captures arbitrary cluster shapes and sizes to select a number of representative points, and builds the Minimum Spanning Tree (MST) of these representative points, called R-MST. After the initial clustering, a summary of the cluster structure is built. This summary enables quick localization of the effect of data updates on the current set of clusters. Our experimental results show that DClust outperforms existing spatial clustering methods such as DBSCAN, C2P, DENCLUE, Incremental DBSCAN and BIRCH in terms of clustering time and accuracy of clusters found.