Note: An object detection method for active camera

To solve the problems caused by a changing background during object detection in active camera, this paper proposes a new method based on SURF (speeded up robust features) and data clustering. The SURF feature points of each image are extracted, and each cluster center is calculated by processing the data clustering of k adjacent frames. Templates for each class are obtained by calculating the histograms within the regions around the center points of the clustering classes. The window of the moving object can be located by finding the region that satisfies the histogram matching result between adjacent frames. Experimental results demonstrate that the proposed method can improve the effectiveness of object detection.Yong Chen, Ronghua Zhang, Lei Shang, and Eric H

Evidence for Factorization in Three-body Bˉ→D(∗)K−K0\bar B\to D^{(*)} K^- K^0 Decays

Motivated by experimental results on $\bar B\to D^{(*)}K^-K^{0}$, we use a factorization approach to study these decays. Two mechanisms concerning kaon pair production arise: current-produced (from vacuum) and transition (from the $B$ meson). The kaon pair in the $\bar B {}^0\to D^{(*)+}K^-K^0$ decays can be produced only by the vector current (current-produced), whose matrix element can be extracted from $e^+e^-\to K\bar K$ processes via isospin relations. The decay rates obtained this way are in good agreement with experiment. The $B^-\to D^{(*)0}K^-K^0$ decays involve both current-produced and transition processes. By using QCD counting rules and the measured $B^-\to D^{(*)0} K^- K^0$ decay rates, the measured decay spectra can be understood.Comment: 3 pages, 6 figures. Talk presented at EPS2003 Conference, Aachen, Germany, July 200

An Upper Bound on the Convergence Time for Distributed Binary Consensus

The problem addressed in this paper is the analysis of a distributed consensus algorithm for arbitrary networks, proposed by B\'en\'ezit et al.. In the initial setting, each node in the network has one of two possible states ("yes" or "no"). Nodes can update their states by communicating with their neighbors via a 2-bit message in an asynchronous clock setting. Eventually, all nodes reach consensus on the majority states. We use the theory of electric networks, random walks, and couplings of Markov chains to derive an O(N4 logN) upper bound for the expected convergence time on an arbitrary graph of size N.Comment: 15th International Conference on Information Fusion, July 2012, 7 page