7512450
Simultaneous background and foreground modeling for tracking in surveillance video
We present a stochastic tracking algorithm for surveillance video where targets are dim and at low resolution. The algorithm builds motion models for both background and foreground by integrating motion and intensity information. Some other merits of the algorithm include adaptive selection of feature points for scene description and defining proper cost functions for displacement estimation. The experimental results show tracking robustness and precision in a challenging video sequences.
1717304
Shape Regression Machine
We present a machine learning approach called shape regression machine (SRM) to segmenting in real time an anatomic structure that manifests a deformable shape in a medical image. Traditional shape segmentation methods rely on various assumptions. For instance, the deformable model assumes that edge defines the shape; the Mumford-Shah variational method assumes that the regions inside/outside the (closed) contour are homogenous in intensity; and the active appearance model assumes that shape/appearance variations are linear. In addition, they all need a good initialization. In contrast, SRM poses no such restrictions. It is a two-stage approach that leverages (a) the underlying medical context that defines the anatomic structure and (b) an annotated database that exemplifies the shape and appearance variations of the anatomy. In the first stage, it solves the initialization problem as object detection and derives a regression solution that needs just one scan in principle. In the second stage, it learns a nonlinear regressor that predicts the nonrigid shape from image appearance. We also propose a boosting regression approach that supports real time segmentation. We demonstrate the effectiveness of SRM using experiments on segmenting the left ventricle endocardium from an echocardiogram of an apical four chamber view.
8965145
A robust algorithm for probabilistic human recognition from video
Human recognition from video requires solving the two tasks, recognition and tracking, simultaneously. This leads to a parameterized time series state space model, representing both motion and identity of the human. Sequential Monte Carlo (SMC) algorithms, like Condensation, can be developed to offer numerical solutions to this model. However in outdoor environments, the solution is more likely to diverge from the foreground, causing failures in both recognition and tracking. In this paper we propose an approach for tackling this problem by incorporating the constraint of temporal continuity in the observations. Experimental results demonstrate improvements over its Condensation counterpart.
15470334
Probabilistic recognition of human faces from video
Recognition of human faces using a gallery of still or video images and a probe set of videos is systematically investigated using a probabilistic framework. In still-to-video recognition, where the gallery consists of still images, a time series state space model is proposed to fuse temporal information in a probe video, which simultaneously characterizes the kinematics and identity using a motion vector and an identity variable, respectively. The joint posterior distribution of the motion vector and the identity variable is estimated at each time instant and then propagated to the next time instant, Marginalization over the motion vector yields a robust estimate of the posterior distribution of the identity variable. A computationally efficient sequential importance sampling (SIS) algorithm is developed to estimate the posterior distribution. Empirical results demonstrate that, due to the propagation of the identity variable over time, a degeneracy in posterior probability of the identity variable is achieved to give improved recognition. The gallery is generalized to videos in order to realize video-to-video recognition. An exemplar-based learning strategy is adopted to automatically select video representatives from the gallery, serving as mixture centers in an updated likelihood measure. The SIS algorithm is applied to approximate the posterior distribution of the motion vector, the identity variable, and the exemplar index, whose marginal distribution of the identity variable produces the recognition result. The model formulation is very general and it allows a variety of image representations and transformations. Experimental results using images/videos collected at UMD, NIST/USF, and CMU with pose/illumination variations illustrate the effectiveness of this approach for both still-to-video and video-to-video scenarios with appropriate model choices.
41942332
Exemplar-Based Face Recognition from Video
A new exemplar-based probabilistic approach for face recognition in video sequences is presented. The approach has two stages: First, Exemplars, which are selected representatives from the raw video, are automatically extracted from gallery videos. The exemplars are used to summarize the gallery video information. In the second part, exemplars are then used as centers for probabilistic mixture distributions for the tracking and recognition process. A particle method is used to compute the posteriori probabilities. Probabilistic methods are attractive in this context as they allow a systematic handling of uncertainty and an elegant way for fusing temporal information.Contrary to some previous video-based approaches, our approach is not limited to a certain image representation. It rather enhances known ones, such as the PCA, with temporal fusion and uncertainty handling. Experiments demonstrate the effectiveness of each of the two stages. We tested this approach on more than 100 training and testing sequences, with 25 different individuals.
60706602
Multiple-exemplar discriminant analysis for face recognition
Face recognition is characteristically different from regular pattern recognition and, therefore, requires a different discriminant analysis other than linear discriminant analysis(LDA). LDA is a single-exemplar method in the sense that each class during classification is represented by a single exemplar, i.e., the sample mean of the class. We present a multiple-exemplar discriminant analysis (MEDA) where each class is represented using several exemplars or even the whole available sample set. The proposed approach produces improved classification results when tested on a subset of FERET database where LDA is ineffective.
32007375
Interpolation theorems for graphs, hypergraphs and matroids
Abstract Let P and Q be, respectively, hereditary and cohereditary properties defined on the subsets of a finite set S . We first prove that several functions related to P and Q interpolate over some families of subsets of S . By using this we then derive a number of interpolation results for graphs, hypergraphs and matroids.
25346055
Almost Covers Of 2-Arc Transitive Graphs
Let Γ be a G-symmetric graph whose vertex set admits a nontrivial G-invariant partition ℬ with block size v. Let Γℬ be the quotient graph of Γ relative to ℬ and Γ[B,C] the bipartite subgraph of Γ induced by adjacent blocks B,C of ℬ. In this paper we study such graphs for which Γℬ is connected, (G, 2)-arc transitive and is almost covered by Γ in the sense that Γ[B,C] is a matching of v-1 ≥ 2 edges. Such graphs arose as a natural extremal case in a previous study by the author with Li and Praeger. The case Γℬ ≅Kv+1 is covered by results of Gardiner and Praeger. We consider here the general case where Γℬ ≇Kv+1, and prove that, for some even integer n ≥ 4, Γℬ is a near n-gonal graph with respect to a certain G-orbit on n-cycles of Γℬ. Moreover, we prove that every (G, 2)-arc transitive near n-gonal graph with respect to a G-orbit on n-cycles arises as a quotient Γℬ of a graph with these properties. (A near n-gonal graph is a connected graph Σ of girth at least 4 together with a set ℰ of n-cycles of Σ such that each 2-arc of Σ is contained in a unique member of ℰ.)
1466117
Redundancy and Robustness of the AS-level Internet topology and its models
A comparison between the topological properties of the measured Internet topology, at the autonomous system level (AS graph), and the equivalent graphs generated by two different power law topology generators are presented. Only one of the synthetic generators reproduces the tier connectivity of the AS graph.
20645165
Harper-type lower bounds and the bandwidths of the compositions of graphs
Abstract We give a general Harper-type lower bound for the bandwidth of a graph which is a common generalization of several known results. As applications we get a lower bound for the bandwidth of the composition of two graphs. By using this we determine the bandwidths of some composition graphs such as ( P r × P s )[ H ], ( P r × C s )[ H ] (2 r ≠ s ), ( C r × C s )[ H ] (6 ⩽ 2 r ⩽ s ), etc., for any graph H . Interestingly, the bandwidths of these graphs have nothing to do with the structure of H in general.
13349007
Greedy Algorithms for Client Assignment in Large-Scale Distributed Virtual Environments
Distributed Virtual Environments (DVEs), such as online games, military simulations, collaborative design, etc., are very popular nowadays. To support large-scale DVEs, a multi-server architecture is usually employed, and the virtual world is partitioned into multiple zones for load distribution. The client assignment problem arises when assigning the participating clients to the servers. Current approaches usually assign clients to servers according to the locations of clients in the virtual world, i.e., clients interacting in the same zone of the virtual world will be assigned to the same server. This approach may degrade the interactivity of DVEs if the network delay from a client to its assigned server is large. In this paper, we formulate the client assignment problem, and propose two algorithms to assign clients to servers in a more efficient way. The proposed algorithms are based on the heuristics developed for the well-known Terminal Assignment problem. Simulation results with the BRITE Internet Topology Generator show that our algorithms are effective in enhancing the interactivity of DVEs.