13,231 research outputs found

    Automatic landmark annotation and dense correspondence registration for 3D human facial images

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    Dense surface registration of three-dimensional (3D) human facial images holds great potential for studies of human trait diversity, disease genetics, and forensics. Non-rigid registration is particularly useful for establishing dense anatomical correspondences between faces. Here we describe a novel non-rigid registration method for fully automatic 3D facial image mapping. This method comprises two steps: first, seventeen facial landmarks are automatically annotated, mainly via PCA-based feature recognition following 3D-to-2D data transformation. Second, an efficient thin-plate spline (TPS) protocol is used to establish the dense anatomical correspondence between facial images, under the guidance of the predefined landmarks. We demonstrate that this method is robust and highly accurate, even for different ethnicities. The average face is calculated for individuals of Han Chinese and Uyghur origins. While fully automatic and computationally efficient, this method enables high-throughput analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl

    Neighbourhood detection and indentification of spatio-temporal dynamical systems using a coarse-to-fine approach

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    A novel approach to the determination of the neighbourhood and the identification of spatio-temporal dynamical systems is investigated. It is shown that thresholding to convert the pattern to a binary pattern and then applying cellular automata (CA) neighbourhood detection methods can provide an initial estimate of the neighbourhood. A coupled map lattice model can then be identified using the CA detected neighbourhood as the initial conditions. This provides a coarse-to-fine approach for neighbourhood detection and identification of coupled map lattice models. Three examples are used to demonstrate the application of the new approach

    Properties of Catlin's reduced graphs and supereulerian graphs

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    A graph GG is called collapsible if for every even subset RV(G)R\subseteq V(G), there is a spanning connected subgraph HH of GG such that RR is the set of vertices of odd degree in HH. A graph is the reduction of GG if it is obtained from GG by contracting all the nontrivial collapsible subgraphs. A graph is reduced if it has no nontrivial collapsible subgraphs. In this paper, we first prove a few results on the properties of reduced graphs. As an application, for 3-edge-connected graphs GG of order nn with d(u)+d(v)2(n/p1)d(u)+d(v)\ge 2(n/p-1) for any uvE(G)uv\in E(G) where p>0p>0 are given, we show how such graphs change if they have no spanning Eulerian subgraphs when pp is increased from p=1p=1 to 10 then to 1515

    Lai’s conditions for spanning and dominating closed trails

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    Asymptotic distributions of the signal-to-interference ratios of LMMSE detection in multiuser communications

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    Let sk=1N(v1k,...,vNk)T,{\mathbf{s}}_k=\frac{1}{\sqrt{N}}(v_{1k},...,v_{Nk})^T, k=1,...,Kk=1,...,K, where {vik,i,k\{v_{ik},i,k =1,...}=1,...\} are independent and identically distributed random variables with Ev11=0Ev_{11}=0 and Ev112=1Ev_{11}^2=1. Let Sk=(s1,...,sk1,{\mathbf{S}}_k=({\mathbf{s}}_1,...,{\mathbf{s}}_{k-1}, sk+1,...,sK){\mathbf{s}}_{k+1},...,{\mathbf{s}}_K), Pk=diag(p1,...,{\mathbf{P}}_k=\operatorname {diag}(p_1,..., pk1,pk+1,...,pK)p_{k-1},p_{k+1},...,p_K) and \beta_k=p_k{\mathbf{s}}_k^T({\mathb f{S}}_k{\mathbf{P}}_k{\mathbf{S}}_k^T+\sigma^2{\mathbf{I}})^{-1}{\math bf{s}}_k, where pk0p_k\geq 0 and the βk\beta_k is referred to as the signal-to-interference ratio (SIR) of user kk with linear minimum mean-square error (LMMSE) detection in wireless communications. The joint distribution of the SIRs for a finite number of users and the empirical distribution of all users' SIRs are both investigated in this paper when KK and NN tend to infinity with the limit of their ratio being positive constant. Moreover, the sum of the SIRs of all users, after subtracting a proper value, is shown to have a Gaussian limit.Comment: Published at http://dx.doi.org/10.1214/105051606000000718 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org
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