人脸合成中模型的平滑调整和逼真的纹理映射

将薄平面样条函数用于人头模型的调整,使模型调整变得更加平滑、自然,并在纹理映射时,提出了基于模型分块的视角相关纹理映射的方法,该方法将模型分块并用每块对应的最清晰、非冗余的纹理对模型块做映射,使块与块之间形成的规则过渡带非常易于纹理融合,整个模型逐块地一次绘制完成,然后在模型中嵌入了抽象的人脸肌肉,合成出非常逼真的人脸表情动画．实验表明文中算法快速、简捷,合成的人脸图像细节清晰、逼真．     

Energy Efficient Run-Time Incremental Mapping for 3-D Networks-on-Chip

3-D Networks-on-Chip(NoC) emerge as a potent solution to address both the interconnection and design complexity problems facing future Multiprocessor System-on-Chips(MPSoCs).Effective run-time mapping on such 3-D NoC-based MPSoCs can be quite challenging,as the arrival order and task graphs of the target applications are typically not known a priori,which can be further complicated by stringent energy requirements for NoC systems.This paper thus presents an energy-aware run-time incremental mapping algorithm(ERIM) for 3-D NoC which can minimize the energy consumption due to the data communications among processor cores,while reducing the fragmentation effect on the incoming applications to be mapped,and simultaneously satisfying the thermal constraints imposed on each incoming application.Specifically,incoming applications are mapped to cuboid tile regions for lower energy consumption of communication and the minimal routing.Fragment tiles due to system fragmentation can be gleaned for better resource utilization.Extensive experiments have been conducted to evaluate the performance of the proposed algorithm ERIM,and the results are compared against the optimal mapping algorithm(branch-and-bound) and two heuristic algorithms(TB and TL).The experiments show that ERIM outperforms TB and TL methods with significant energy saving(more than 10%),much reduced average response time,and improved system utilization.     

IMORL: Incremental Multiple-Object Recognition and Localization

This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an image. Unlike the conventional multiple-object learning algorithms, the proposed method can automatically and adaptively learn from continuous video streams over the entire learning life. This kind of incremental learning capability enables the proposed approach to accumulate experience and use such knowledge to benefit future learning and the decision making process. Furthermore, IMORL can effectively handle variations in the number of instances in each data chunk over the learning life. Another important aspect analyzed in this paper is the concept drifting issue. In multiple-object learning scenarios, it is a common phenomenon that new interesting objects may be introduced during the learning life. To handle this situation, IMORL uses an adaptive learning principle to autonomously adjust to such new information. The proposed approach is independent of the base learning models, such as decision tree, neural networks, support vector machines, and others, which provide the flexibility of using this method as a general learning methodology in multiple-object learning scenarios. In this paper, we use a neural network with a multilayer perceptron (MLP) structure as the base learning model and test the performance of this method in various video stream data sets. Simulation results show the effectiveness of this method.     

增量与演化流形学习综述

流形学习的目标是发现观测数据嵌入在高维数据空间中的低维光滑流形.近年来,在线或增量地发现内在低维流形结构成为流形学习的研究热点.从增量学习和演化学习2个方面入手,对该领域已有研究进展进行综述.增量流形学习较之传统的批量流形学习方法具有动态增量的能力,而演化流形学习能够在线地发现海量动态数据的内在规律,有利于进行维数约简和数据分析.文中对主要的增量与演化流形学习算法的基本原理、特点进行了阐述,分析了各自的优点与不足,指出了该领域的开放问题,并对进一步的研究方向进行了展望.     

Online Metric Tracking and Smoothing

We consider the online smoothing problem, in which a tracker is required to maintain distance no more than Δ≥0 from a time-varying signal f while minimizing its own movement. The problem is determined by a metric space (X,d) with an associated cost function c:?→?. Given a signal f ₁,f ₂,…∈X the tracker is responsible for producing a sequence a ₁,a ₂,… of elements of X that meet the proximity constraint: d(f _i ,a _i )≤Δ. To complicate matters, the tracker is on-line—the value a _i may only depend on f ₁,…,f _i —and wishes to minimize the cost of his travels, ∑c(d(a _i ,a _i+1)). We evaluate such tracking algorithms competitively, comparing this with the cost achieved by an optimal adversary apprised of the entire signal in advance. The problem was originally proposed by Yi and Zhang (In: Proceedings of the 20th annual ACM-SIAM symposium on discrete algorithms (SODA), pp. 1098–1107. ACM Press, New York, 2009), who considered the natural circumstance where the metric spaces are taken to be ? ^k with the ? ₂ metric and the cost function is equal to 1 unless the distance is zero (thus the tracker pays a fixed cost for any nonzero motion).

We begin by studying arbitrary metric spaces with the “pay if you move” metric of Yi and Zhang (In: Proceedings of the 20th annual ACM-SIAM symposium on discrete algorithms (SODA), pp. 1098–1107. ACM Press, New York, [2009]) described above and describe a natural randomized algorithm that achieves a O(logb _Δ)-competitive ratio, where b _Δ=max _x∈X |B _Δ(x)| is the maximum number of points appearing in any ball of radius Δ. We show that this bound is tight.

We then focus on the metric space ? with natural families of monotone cost functions c(x)=x ^p for some p≥0. We consider both the expansive case (p≥1) and the contractive case (p<1), and show that the natural lazy algorithm performs well in the expansive case. In the contractive case, we introduce and analyze a novel deterministic algorithm that achieves a constant competitive ratio depending only on p. Finally, we observe that by slightly relaxing the guarantee provided by the tracker, one can obtain natural analogues of these algorithms that work in continuous metric spaces.

     

Incremental Dynamics

A new system of dynamic logic is introduced and motivated, witha novel approach to variable binding for incremental interpretation. Thesystem is shown to be equivalent to first order logic and complete.The new logic combines the dynamic binding idea from DynamicPredicate Logic with De Bruijn style variable free indexing. Quantifiersbind the next available variable register; the indexing mechanismguarantees that active registers are never overwritten by newquantifiers actions. Apart from its interest in its own right, theresulting system has certain advantages over Dynamic Predicate Logic orDiscourse Representation Theory. It comes with a more well behaved(i.e., transitive) consequence relation, it gives a more explicitaccount of how anaphoric context grows as text gets processed, and ityields new insight into the dynamics of anaphoric linking in reasoning.Incremental dynamics also points to a new way of handling contextdynamically in Montague grammar.     

Smoothing sample extremes: The mixed model approach

Nonparametric regression for sample extremes can be performed using a variety of techniques. The penalized spline approach for the Poisson point process model is considered. The generalized linear mixed model representation for the spline model, with its Bayesian approach to inference, turns out to be a very flexible framework. Monte Carlo Markov chain algorithms are employed for exploration of the posterior distribution. The overall performance of the method is tested on simulated data. Two real data applications are also discussed for modeling trend of intensity of earthquakes in Italy and for assessing seasonality and short term trend of summer extreme temperatures in Milan, Italy.     

AgentCubes: Incremental 3D end-user development

3D game development can be an enticing way to attract K-12 students to computer science, but designing and programming 3D games is far from trivial. Students need to achieve a certain level of 3D fluency in modeling, animation, and programming to be able to create compelling 3D content. The combination of innovative end-user development tools and standards-based curriculum that promotes IT fluency by shifting the pedagogical focus from programming to design, can address motivational aspects without sacrificing principled educational goals. The AgentCubes 3D game-authoring environment raises the ceiling of end-user development without raising the threshold. Our formal user study shows that with Incremental 3D, the gradual approach to transition from 2D to 3D authoring, middle school students can build sophisticated 3D games including 3D models, animations, and programming.     

Experiments with Incremental Concept Formation: UNIMEM

Learning by observation involves automatic creation of categories that summarize experience. In this paper we present UNIMEM, an artificial intelligence system that learns by observation. UNIMEM is a robust program that can be run on many domains with real-world problem characteristics such as uncertainty, incompleteness, and large numbers of examples. We give an overview of the program that illustrates several key elements, including the automatic creation of non-disjoint concept hierarchies that are evaluated over time. We then describe several experiments that we have carried out with UNIMEM, including tests on different domains (universities, Congressional voting records, and terrorist events) and an examination of the effect of varying UNIMEM's parameters on the resulting concept hierarchies. Finally we discuss future directions for our work with the program.     

分形变换与图象噪声平滑

利用分形变换进行图象噪声平滑，是他形理论的又一应用领域。本文对这一问题进行了理论和实验验证，从而指出了分形变换应用于图象噪声平滑的潜力所在。     

On Variational Curve Smoothing and Reconstruction

In this paper we discuss and experimentally compare variational methods for curve denoising, curve smoothing and curve reconstruction problems. The methods are based on defining suitable cost functionals to be minimized, the cost being the combination of a fidelity term measuring the “distance” of a curve from the data and a smoothness term measuring the curve’s L ₁-norm or length.     

Motion Smoothing Using Wavelets

Smooth motion generation is an important issue in the computer animation and virtual reality areas. The motion of a rigid body consists of translation and orientation. The former is described by a space curve in 3-dimensional Euclidean space, while the latter is represented by a curve in the unit quaternion space. Although there exist well-known techniques for smoothing the translation data, smoothing the orientation data is yet to be explored due to the nonlinearity of the unit quaternion space. This paper presents a wavelet-based algorithm for smoothing noise-embedded motion data and the experiment shows the effectiveness of the proposed algorithm.     

Incremental passivity and output regulation

In this paper we investigate the use of incremental passivity concepts for the design of nonlinear global regulators. For a specific class of systems and exosystems we show how the problem of designing an internal model-based regulator reduces to the design of an output feedback controller rendering the controlled plant incrementally passive. In this framework we present a result for the design of an output feedback controller achieving incremental passivity for a particular class of nonlinear systems.     

Generalised Nonlocal Image Smoothing

We propose a discrete variational approach for image smoothing consisting of nonlocal data and smoothness constraints that penalise general dissimilarity measures defined on image patches. One of such dissimilarity measures is the weighted L ₂ distance between patches. In such a case we derive an iterative neighbourhood filter that induces a new similarity measure in the photometric domain. It can be regarded as an extended patch similarity measure that evaluates not only the patch similarity of two chosen pixels, but also the similarity of their corresponding neighbours. This leads to a more robust smoothing process since the pixels selected for averaging are more coherent with the local image structure. By slightly modifying the way the similarities are computed we obtain two related filters: The NL-means filter of Buades et al. (SIAM Multiscale Model. Simul. 4(2):490–530, 2005b) and the NDS filter of Mrázek et al. (Geometric Properties for Incomplete Data, Computational Imaging and Vision, vol. 31, pp. 335–352, Springer, Dordrecht, 2006). In fact, the proposed approach can be considered as a generalisation of the latter filter to the space of patches. We also provide novel insights into relations of the NDS filter with diffusion/regularisation methods as well as with some recently proposed graph regularisation techniques. We evaluate our method for the task of denoising greyscale and colour images degraded with Gaussian and salt-and-pepper noise, demonstrating that it compares very well to other more sophisticated approaches.     

数据平滑技术综述

数据平滑是统计语言建模的关键技术,它不仅可以改进语言模型的性能,还可以提高语音识别、文字识别等应用领域的系统识别率,不同的数据平滑方法之间的对比应在各种不同规模的训练集上操作。各种平滑算法中,以Good-Turing估计、线性插值平滑、Katz’s回退式平滑最为典型和常用。该文对各种数据平滑方法进行了经验性对比,并讨论了影响这些数据平滑方法性能的有关因素。     

Smoothing vocal signal spectrum

Proposed was a method of smoothing the spectrum of vocal signal which is based on the histogram analysis of the peaks of the spectral envelope and represents a variety of the frequency analysis that is synchronous with the main tone. Its application was demonstrated by way of examples of the real vocal signals.     

Robust smoothing: Smoothing parameter selection and applications to fluorescence spectroscopy

Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. Various robust smoothing methods for estimating fluorescence emission spectra are compared and data driven methods for the selection of smoothing parameter are suggested. The methods currently implemented in R for smoothing parameter selection proved to be unsatisfactory, and a computationally efficient procedure that approximates robust leave-one-out cross validation is presented.     

Incremental Abductive EBL

In previous work, we described a knowledge-intensive inductive learning algorithm called abductive explanation-based learning (A-EBL) that uses background knowledge to improve the performance of a concept learner. A disadvantage of A-EBL is that it is not incremental. This article describes an alternative learning algorithm called IA-EBL that learns incrementally; IA-EBL replaces the set-cover-based learning algorithm of A-EBL with an extension of a perceptron learning algorithm. IA-EBL is in most other respects comparable to A-EBL, except that the output of the learning system can no longer be easily expressed as a logical theory. In this article, IA-EBL is described, analyzed according to Littlestone's model of mistake-bounded learnability, and finally compared experimentally to A-EBL. IA-EBL is shown to provide order-of-magnitude speedups over A-EBL in two domains when used in an incremental setting.     

Incremental LR parsers

Summary The concept of incremental parsing is briefly introduced. An algorithm which augments an LR parser with the capability of reanalyzing a limited part of a modified program is illustrated. The algorithm operates on a sequence of configurations representing the parse of the old input and finds the smallest part of the sequence which must be recomputed to obtain the parse of the new input.The implementation is discussed: a suitable data structure and a version of the algorithm which operates upon it are introduced; finally the problem of realizing efficient incremental parsers is faced, showing a modification to the basic algorithm which enable the reanalysis to be performed in linear time.Work supported by C.N.R., Italy