Microscopic Origin of Casimir-Polder Forces

We establish a general relation between dispersion forces. First, based on QED in causal media, leading-order perturbation theory is used to express both the single-atom Casimir-Polder and the two-atom van der Waals potentials in terms of the atomic polarizabilities and the Green tensor for the body-assisted electromagnetic field. Endowed with this geometry-independent framework, we then employ the Born expansion of the Green tensor together with the Clausius-Mosotti relation to prove that the macroscopic Casimir-Polder potential of an atom in the presence of dielectric bodies is due to an infinite sum of its microscopic many-atom van der Waals interactions with the atoms comprising the bodies. This theorem holds for inhomogeneous, dispersing, and absorbing bodies of arbitrary shapes and arbitrary atomic composition on an arbitrary background of additional magnetodielectric bodies.     

Nonparametric Bayesian Image Segmentation

Image segmentation algorithms partition the set of pixels of an image into a specific number of different, spatially homogeneous groups. We propose a nonparametric Bayesian model for histogram clustering which automatically determines the number of segments when spatial smoothness constraints on the class assignments are enforced by a Markov Random Field. A Dirichlet process prior controls the level of resolution which corresponds to the number of clusters in data with a unique cluster structure. The resulting posterior is efficiently sampled by a variant of a conjugate-case sampling algorithm for Dirichlet process mixture models. Experimental results are provided for real-world gray value images, synthetic aperture radar images and magnetic resonance imaging data.     

Path-based clustering for grouping of smooth curves and texture segmentation

Perceptual grouping organizes image parts in clusters based on psychophysically plausible similarity measures. We propose a novel grouping method in this paper, which stresses connectedness of image elements via mediating elements rather than favoring high mutual similarity. This grouping principle yields superior clustering results when objects are distributed on low-dimensional extended manifolds in a feature space, and not as local point clouds. In addition to extracting connected structures, objects are singled out as outliers when they are too far away from any cluster structure. The objective function for this perceptual organization principle is optimized by a fast agglomerative algorithm. We report on perceptual organization experiments where small edge elements are grouped to smooth curves. The generality of the method is emphasized by results from grouping textured images with texture gradients in an unsupervised fashion.     

Stability-based validation of clustering solutions

Data clustering describes a set of frequently employed techniques in exploratory data analysis to extract "natural" group structure in data. Such groupings need to be validated to separate the signal in the data from spurious structure. In this context, finding an appropriate number of clusters is a particularly important model selection question. We introduce a measure of cluster stability to assess the validity of a cluster model. This stability measure quantifies the reproducibility of clustering solutions on a second sample, and it can be interpreted as a classification risk with regard to class labels produced by a clustering algorithm. The preferred number of clusters is determined by minimizing this classification risk as a function of the number of clusters. Convincing results are achieved on simulated as well as gene expression data sets. Comparisons to other methods demonstrate the competitive performance of our method and its suitability as a general validation tool for clustering solutions in real-world problems.     

Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera

Category-level object recognition, segmentation, and tracking in videos becomes highly challenging when applied to sequences from a hand-held camera that features extensive motion and zooming. An additional challenge is then to develop a fully automatic video analysis system that works without manual initialization of a tracker or other human intervention, both during training and during recognition, despite background clutter and other distracting objects. Moreover, our working hypothesis states that category-level recognition is possible based only on an erratic, flickering pattern of interest point locations without extracting additional features. Compositions of these points are then tracked individually by estimating a parametric motion model. Groups of compositions segment a video frame into the various objects that are present and into background clutter. Objects can then be recognized and tracked based on the motion of their compositions and on the shape they form. Finally, the combination of this flow-based representation with an appearance-based one is investigated. Besides evaluating the approach on a challenging video categorization database with significant camera motion and clutter, we also demonstrate that it generalizes to action recognition in a natural way. Electronic Supplementary Material  The online version of this article () contains supplementary material, which is available to authorized users. This work was supported in part by the Swiss national science foundation under contract no. 200021-107636.     

On the Implicit Anti-Aliasing Feature of Continuous-Time Cascaded Sigma–Delta Modulators

This paper deals with one of the most outstanding advantages of continuous-time (CT) sigma-delta modulators compared to their discrete-time counterparts: the implicit anti-aliasing feature (AAF). Although inherent in any CT architecture, analysis of anti-aliasing properties has mostly been restricted to single-stage modulators in the past. In this contribution, extensions on analysis methods for the study of the AAF of CT multistage noise-shaping architectures are covered. A theoretical model is introduced and confirmed through simulation results. Contrary to previous belief, the results indicate that usually all stages of a cascaded architecture are involved in the anti-aliasing behaviour and hence that it is not solely determined by the first stage.     

Pairwise data clustering by deterministic annealing

Partitioning a data set and extracting hidden structure from the data arises in different application areas of pattern recognition, speech and image processing. Pairwise data clustering is a combinatorial optimization method for data grouping which extracts hidden structure from proximity data. We describe a deterministic annealing approach to pairwise clustering which shares the robustness properties of maximum entropy inference. The resulting Gibbs probability distributions are estimated by mean-field approximation. A new structure-preserving algorithm to cluster dissimilarity data and to simultaneously embed these data in a Euclidian vector space is discussed which can be used for dimensionality reduction and data visualization. The suggested embedding algorithm which outperforms conventional approaches has been implemented to analyze dissimilarity data from protein analysis and from linguistics. The algorithm for pairwise data clustering is used to segment textured images     

Systematic approach to the synthesis of continuous-time cascaded sigma–delta modulators

This paper deals with a systematic approach to the synthesis of continuous-time cascaded sigma–delta modulators. Based on a system-theoretical model, a detailed derivation of the digital cancelation filters for continuous-time cascaded architectures is presented in order to achieve maximum signal-to-noise ratio together with optimal anti-aliasing performance. By using the same model, an exact equation for the performance loss of any cascaded architecture is derived. The latter is due to the scaling for stability and given relative to an ideal high-pass filter of the overall modulator order. Finally, an analytical calculation of optimal scaling coefficients in between the stages is performed, resulting in a limited search-space for these coefficients. Theoretical results are verified by simulations.