Multi-Frame Correspondence Estimation Using Subspace Constraints

When a rigid scene is imaged by a moving camera, the set of all displacements of all points across multiple frames often resides in a low-dimensional linear subspace. Linear subspace constraints have been used successfully in the past for recovering 3D structure and 3D motion information from multiple frames (e.g., by using the factorization method of Tomasi and Kanade (1992, International Journal of Computer Vision, 9:137–154)). These methods assume that the 2D correspondences have been precomputed. However, correspondence estimation is a fundamental problem in motion analysis. In this paper we show how the multi-frame subspace constraints can be used for constraining the 2D correspondence estimation process itself.We show that the multi-frame subspace constraints are valid not only for affine cameras, but also for a variety of imaging models, scene models, and motion models. The multi-frame subspace constraints are first translated from constraints on correspondences to constraints directly on image measurements (e.g., image brightness quantities). These brightness-based subspace constraints are then used for estimating the correspondences, by requiring that all corresponding points across all video frames reside in the appropriate low-dimensional linear subspace.The multi-frame subspace constraints are geometrically meaningful, and are {not} violated at depth discontinuities, nor when the camera-motion changes abruptly. These constraints can therefore replace {heuristic} constraints commonly used in optical-flow estimation, such as spatial or temporal smoothness.     

2D vs. 3D Deformable Face Models: Representational Power,Construction, and Real-Time Fitting

Model-based face analysis is a general paradigm with applications that include face recognition, expression recognition, lip-reading, head pose estimation, and gaze estimation. A face model is first constructed from a collection of training data, either 2D images or 3D range scans. The face model is then fit to the input image(s) and the model parameters used in whatever the application is. Most existing face models can be classified as either 2D (e.g. Active Appearance Models) or 3D (e.g. Morphable Models). In this paper we compare 2D and 3D face models along three axes: (1) representational power, (2) construction, and (3) real-time fitting. For each axis in turn, we outline the differences that result from using a 2D or a 3D face model.     

实根隔离算法及其在电机中的应用

电机系统中的大多数问题都可以转化为非线性方程组全部解的问题,而现有的数值方法由于各自的弱点,使得人们对带有误差的数值方法不能放心.符号计算的特点是数值方法不具有的精确计算.本文研究了基于符号计算的实根隔离算法,运用Maple10.0软件开发了求出所有实根的计算程序.该方法有效地应用近似因式分解,编程简单、分析直观、结果可靠.实例计算表明该方法具有实用性、有效性,为电机参数多解问题提供了新的方法.     

Analysis of a sparse hypermatrix Cholesky with fixed-sized blocking

We present the way in which we have constructed an implementation of a sparse Cholesky factorization based on a hypermatrix data structure. This data structure is a storage scheme which produces a recursive 2D partitioning of a sparse matrix. It can be useful on some large sparse matrices. Subblocks are stored as dense matrices. Thus, efficient BLAS3 routines can be used. However, since we are dealing with sparse matrices some zeros may be stored in those dense blocks. The overhead introduced by the operations on zeros can become large and considerably degrade performance. We present the ways in which we deal with this overhead. Using matrices from different areas (Interior Point Methods of linear programming and Finite Element Methods), we evaluate our sequential in-core hypermatrix sparse Cholesky implementation. We compare its performance with several other codes and analyze the results. In spite of using a simple fixed-size partitioning of the matrix our code obtains competitive performance. This work was supported by the Ministerio de Educación y Ciencia of Spain (TIN2004-07739-C02-01).     

构建在联接探索和分解分布上分布估计算法的扩展算法

遗传算法（GA）在解决变量间存在较大相互作用优化问题时缺乏有效性，一种解决问题的途径是分布估计算法（EDA）。分解分布算法是一种近似高阶相互作用的EDA，它用分解Boltzmann分布来产生新的解。运用联接探测及分解分布给出一个以高概率找到最优解的新算法。该算法能解决一些分布估计算法难于处理的问题。实验证明了算法的可行性和有效性。     

一种信任关系强度敏感的社会化推荐算法

为了进一步提高推荐算法的准确率,更好地对用户间的信任关系进行建模,首先提出了一种信任关系强度敏感的社会化推荐算法(StrengthMF).与以往的算法相比,该算法假设建立信任关系的两个用户之间并不一定存在着相似的兴趣爱好.在推荐过程中,StrengthMF算法通过共享的潜在用户特征空间来对信任关系强度和用户兴趣进行建模,通过进一步识别出那些与目标用户有着共同爱好的朋友来对求解的过程进行优化.为了验证算法所估计出的信任关系强度的准确性,接着又在SocialMF算法的基础上,提出了一种使用所估计的信任关系对其重新训练和学习的InfluenceMF算法.实验结果表明,与目前较为流行的方法相比,新方法能在RMSE和MAE上取得更好的推荐结果,其所推导出的信任关系强度能进一步提高已有推荐算法的性能.     

利用三次剩余构造的基于身份环签名方案

传统的基于身份环签名方案大多采用双线性配对实现,但配对方法的运算复杂度较高,会大幅降低签名方案的效率。为此,提出一种非配对的环签名方案。给出用于有效计算三次剩余3′次根的算法,在该算法的基础上生成签名密钥,并结合三次剩余理论构造基于身份的环签名方案。分析结果表明,在大整数分解困难问题的假设前提下,该方案在随机预言模型下被证明是选择消息和身份安全的。同时,该方案也满足签名者无条件匿名性。     

Filtering,smoothing and prediction using a control‐loop spectral factorization method for coloured noise

A method for the linear least‐squares estimation of random signals contaminated with random noise that uses a new method of spectral factorization is shown. It is shown that the optimal filter can be written entirely in terms of the two spectral factors of signal plus noise and noise‐alone, and can be applied to the general case of coloured and white additive noise. The method of spectral factorization used is novel and uses control‐system methodology. Copyright © 2012 John Wiley & Sons, Ltd.     

Computing Longest Previous Factor in linear time and applications

We give two optimal linear-time algorithms for computing the Longest Previous Factor (LPF) array corresponding to a string w. For any position i in w, LPF[i] gives the length of the longest factor of w starting at position i that occurs previously in w. Several properties and applications of LPF are investigated. They include computing the Lempel-Ziv factorization of a string and detecting all repetitions (runs) in a string in linear time independently of the integer alphabet size.     

Identification of certain noisy MA models: new results

In this paper, we address the identification problem of p-inputs q-outputs MA models, corrupted by a white noise with unknown covariance matrix, in the case where p<q. Under certain additional conditions, we show that the generating function of the MA model is identifiable (up to a p×p constant orthogonal matrix) from the autocovariance function of the observation. Our results extend those already obtained in Desbouvries et al. [5] and Desbouvries and Loubaton [6].