Showing papers on "Centroid published in 2006"

Comparison of centroid computation algorithms in a Shack–Hartmann sensor

Abstract: Analytical theory is combined with extensive numerical simulations to compare different flavours of centroiding algorithms: thresholding, weighted centroid, correlation, quad cell (QC). For each method, optimal parameters are defined in function of photon flux, readout noise and turbulence level. We find that at very low flux the noise of QC and weighted centroid leads the best result, but the latter method can provide linear and optimal response if the weight follows spot displacements. Both methods can work with average flux as low as 10 photons per subaperture under a readout noise of three electrons. At high-flux levels, the dominant errors come from non-linearity of response, from spot truncations and distortions and from detector pixel sampling. It is shown that at high flux, centre of gravity approaches and correlation methods are equivalent (and provide better results than QC estimator) as soon as their parameters are optimized. Finally, examples of applications are given to illustrate the results obtained in the paper.

A Framework for Automatic Clustering of Parametric MIMO Channel Data Including Path Powers

Abstract: We present a solution to the problem of identifying clusters from MIMO measurement data in a data window, with a minimum of user interaction. Conventionally, visual inspection has been used for the cluster identification. However this approach is impractical for a large amount of measurement data. Moreover, visual methods lack an accurate definition of a "cluster" itself. We introduce a framework that is able to cluster multi-path components (MPCs), decide on the number of clusters, and discard outliers. For clustering we use the K-means algorithm, which iteratively moves a number of cluster centroids through the data space to minimize the total difference between MPCs and their closest centroid. We significantly improve this algorithm by following changes: (i) as the distance metric we use the multi- path component distance (MCD), (ii) the distances are weighted by the powers of the MPCs. The implications of these changes result in a definition of a "cluster" itself that appeals to intuition. We assess the performance of the new algorithm by clustering real-world measurement data from an indoor big hall environment.

Type-2 Fuzzistics for Symmetric Interval Type-2 Fuzzy Sets: Part 1, Forward Problems

Abstract: Interval type-2 fuzzy sets (T2 FS) play a central role in fuzzy sets as models for words and in engineering applications of T2 FSs. These fuzzy sets are characterized by their footprints of uncertainty (FOU), which in turn are characterized by their boundaries-upper and lower membership functions (MF). In this two-part paper, we focus on symmetric interval T2 FSs for which the centroid (which is an interval type-1 FS) provides a measure of its uncertainty. Intuitively, we anticipate that geometric properties about the FOU, such as its area and the center of gravities (centroids) of its upper and lower MFs, will be associated with the amount of uncertainty in such a T2 FS. The main purpose of this paper (Part 1) is to demonstrate that our intuition is correct and to quantify the centroid of a symmetric interval T2 FS, and consequently its uncertainty, with respect to such geometric properties. It is then possible, for the first time, to formulate and solve forward problems, i.e., to go from parametric interval T2 FS models to data with associated uncertainty bounds. We provide some solutions to such problems. These solutions are used in Part 2 to solve some inverse problems, i.e., to go from uncertain data to parametric interval T2 FS models (T2 fuzzistics)

Optical navigation apparatus using fixed beacons and a centroid sensing device

Abstract: The present invention relates to an optical navigation system for determining at least one parameter of a pose, which includes the position and orientation of an object in an environment. The optical navigation system uses a number of beacons affixed at known locations in the environment to provide electromagnetic radiation in a sequenced pattern. An on-board optic images the radiation from the beacons onto an on-board centroid sensing device to obtain an imaged distribution of the radiation on the on-board centroid sensing device. The centroid sensing device- determines the centroid of the imaged distribution and provides centroid information to a navigation unit for determining at least one parameter of the pose of the object from the centroid. The navigation system is particularly well-suited for navigating hand-held objects.

Does timbral brightness scale with frequency and spectral centroid

Abstract: Two models that predict perceived timbral brightness in terms of the centroid of the frequency spectrum were investigated. One model simply uses the centroid of the frequency spectrum, the other divides this same value by the fundamental frequency: the latter scales the centroid of the frequency spectrum with the fundamental frequency. Different single tone and pitch combinations, presented sequentially, were compared. Participants were not-asked to ignore piich differences and intervals of greater than an octave were compared. The results indicate that brightness is much better correlated with frequency spectrum centroid (r = 0.513, p < 0.01) than with the ratio of the centroid of the frequency spectrum to the fundamental frequency (r = 0.030, p = 0.441).

Controlled collective motion for trajectory tracking

Abstract: This paper extends previous work on oscillator models to meet the needs of multiagent applications in which the motion of the collective centroid of the group must be dynamic. Individual agents are modeled as unit speed planar kinematic unicycles. A steering control law is derived for each individual so that the velocity of the collective centroid matches a reference velocity, provided the reference speed is less than one. A framework for steering controls is presented such that the unicycles stay near the collective centroid even though the centroid is non-static. Finally, an outer loop controller is proposed to allow tracking of a target vehicle. Simulation results are shown to support analysis.

Centroid bodies and comparison of volumes

Abstract: For −1 < p < 1 we introduce the concept of a polar pcentroid body ΓpK of a star body K. We consider the question of whether ΓpK ⊂ ΓpL implies vol(L) ≤ vol(K). Our results extend the studies by Lutwak in the case p = 1 and Grinberg, Zhang in the case p > 1.

Interstellar cloud structure: the statistics of centroid velocities

Abstract: Context. The statistical properties of maps of line centroids have been used for almost 50 years, but there is still no general agreement on their interpretation. Aims. We have tried to quantify which properties of underlying turbulent velocity fields can be derived from centroid velocity maps, and we tested conditions under which the scaling behaviour of the centroid velocities matches the scaling of the three-dimensional velocity field. Methods. Using fractal cloud models we systematically studied the relation between three-dimensional density and velocity fields and the statistical properties of the resulting line centroid maps. We paid special attention to cases with large density fluctuations resembling supersonic interstellar turbulence. Starting from the ∆-variance analysis, we derived a new tool to compute the scaling behaviour of the three-dimensional velocity field from observed intensity and centroid velocity maps. Results. We provide two criteria to decide whether the information from the centroid velocities directly reflects the properties of the underlying velocity field. Applying these criteria allows us to understand the different results found so far in the literature for interpreting the statistics of velocity centroids. The new iteration scheme can be used to derive the three-dimensional velocity scaling from centroid velocity maps for arbitrary density and velocity fields, but it requires accurate knowledge of the average density of the interstellar cloud under consideration.

Fast point-based 3-D alignment of live cells

Abstract: Typical time intervals between acquisitions of three-dimensional (3-D) images of the same cell in live cell imaging are in the orders of minutes. In the meantime, the live cell can move in a water basin on the stage. This movement can hamper the studies of intranuclear processes. We propose a fast point-based image registration method for the suppression of the movement of a cell as a whole in the image data. First, centroids of certain intracellular objects are computed for each image in a time-lapse series. Then, a matching between the centroids, which have the maximal number of pairs, is sought between consecutive point sets by a 3-D extension of a two-dimensional fast point pattern matching method, which is invariant to rotation, translation, local distortion, and extra/missing points. The proposed 3-D extension assumes rotations only around the z axis to retain the complexity of the original method. The final step involves computing the optimal fully 3-D transformation between images from corresponding points in the least-squares manner. The robustness of the method was evaluated on generated data. The results of the simulations show that the method is very precise and its correctness can be estimated. This article also presents two practical application examples, namely the registration of images of HP1 domains and the registration of images of telomeres. More than 97% of time-consecutive images were successfully registered. The results show that the method is very well suited to live cell imaging.

Grassmann clustering

Abstract: An important tool in high-dimensional, explorative data mining is given by clustering methods. They aim at identifying samples or regions of similar characteristics, and often code them by a single codebook vector or centroid. One of the most commonly used partitional clustering techniques is the k-means algorithm, which in its batch form partitions the data set into k disjoint clusters by simply iterating between cluster assignments and cluster updates. The latter step implies calculating a new centroid within each cluster. We generalize the concept of k-means by applying it not to the standard Euclidean space but to the manifold of subvectorspaces of a fixed dimension, also known as the Grassmann manifold. Important examples include projective space i.e. the manifold of lines and the space of all hyperplanes. Detecting clusters in multiple samples drawn from a Grassmannian is a problem arising in various applications. In this manuscript, we provide corresponding metrics for a Grassmann k-means algorithm, and solve the centroid calculation problem explicitly in closed form. An application to nonnegative matrix factorization illustrates the feasibility of the proposed algorithm.

Concurrent Segmentation and Recognition with Shape-Driven Fast Marching Methods

Abstract: We present a variational framework that integrates the statistical boundary shape models into a Level Set system that is capable of both segmenting and recognizing objects. Since we aim to recognize objects, we trace the active contour and stop it near real object boundaries while inspecting the shape of the contour instead of enforcing the contour to get a priori shape. We get the location of character boundaries and character labels at the system output. We developed a promising local front stopping scheme based on both image and shape information for fast marching systems. A new object boundary shape signature model, based on directional Gauss gradient filter responses, is also proposed. The character recognition system that employs the new boundary shape descriptor outperforms the other systems, based on well-known boundary signatures such as centroid distance, curvature etc.

Surface-based characteristic path generation

Abstract: This document discusses, among other things, systems and methods for efficiently using surface data to calculate a characteristic path of a virtual three-dimensional object. A surface mesh is constructed using segmented volumetric data representing the object. Geodesic distance from a reference point is calculated for each shape element in the surface mesh. The geodesic distance values are used to produce rings. Ring centroids are computed and connected to form the characteristic path, which is optionally pruned and smoothed.

Near-Duplicate Image Recognition and Content-based Image Retrieval using Adaptive Hierarchical Geometric Centroids

Abstract: In this paper, we present a new feature extraction method that simultaneously captures the global and local characteristics of an image by adaptively computing hierarchical geometric centroids of the image. We show that these hierarchical centroids have some very interesting properties such as illumination invariant and insensitive to scaling. We have applied the method for near-duplicate image recognition and for content-based image retrieval. We present experimental results to show that our method works effectively in both applications.

Newton Method for Riemannian Centroid Computation in Naturally Reductive Homogeneous Spaces

Abstract: We address the problem of computing the Riemannian centroid of a constellation of points in a naturally reductive homogeneous manifold. We note that many interesting manifolds used in engineering (such as the special orthogonal group, Grassman, sphere, positive definite matrices) possess this structure. We develop an intrinsic Newton scheme for the centroid computation. This is achieved by exploiting a formula that we introduce for obtaining the Hessian of the squared Riemannian distance on naturally reductive homogeneous spaces. Some results of finding the centroid of a constellation of points in these spaces are presented, which evidence the quadratic convergence of the Newton method derived herein. These computer simulation results show that, as expected, the Newton method has a faster convergence rate than the usual gradient-based approaches.

Performance of regional distance centroid moment tensor inversion applied to the 2004 mid‐Niigata prefecture earthquake, Japan

Abstract: SUMMARY We develop a centroid moment tensor (CMT) inversion method by using a densely distributed regional seismic network in order to calculate the centroid locations and MT solutions of regional earthquakes. Centroid location and time are obtained using a grid search algorithm with the new CMT inversion method. This method is applied to the earthquakes that occurred in the focal area of the 2004 mid-Niigata prefecture earthquake, central Japan. The centroid locations and focal mechanisms of the main shock and aftershocks with moment magnitudes greater than 3.4 are effectively constrained. We assess the reliability of the CMT inversion method by comparing its results with accurate hypocentres calculated with a local temporary seismic network. The accuracy of the centroid locations in the horizontal and vertical directions is approximately less than 4 km. The MT solutions are consistent with the P-wave first-motion polarities. By using the nodal planes of focal mechanisms and the centroid locations of many earthquakes, we successfully imaged the major faults distributed on a vertical cross-section across the main shock fault. Further, we evaluate the validity of the application to ‘offshore earthquakes’ by using the aftershocks that occur in the landward area, wherein the seismic network does not cover the focal area but is located in a one-sided region of the focal area. These results show that the focal mechanism and depth are very stable, although the accuracy of the horizontal distribution is reduced to half.

Generating rooted and free plane trees

Abstract: This article has two main results. First, we develop a simple algorithm to list all nonisomorphic rooted plane trees in lexicographic order using a level sequence representation. Then, by selecting a unique centroid to act as the root of a free plane tree, we apply the rooted plane tree algorithm to develop an algorithm to list all nonisomorphic free plane trees. The latter algorithm also uses a level sequence representation and lists all free plane trees with a unique centroid first followed by all free plane trees with two centroids. Both algorithms are proved to run in constant amortized time using straightforward bounding methods.

Center of mass attracts attention

Abstract: Using the spatial cueing technique, this study demonstrates that the center of mass (centroid) of a visual scene has a special ability to attract attention even when there is no object presented at this location. Four boxes formed an imaginary square and were presented to the left or right hemifield. After the cueing in one box, a target appeared in one of the four boxes and, in addition, at centroid. Fastest reaction times were observed at centroid, irrespective of whether this centroid was also occupied by a box. Reaction times at the uncued locations varied according to their relative positions to centroid and fixation. No inhibition of return effect was observed when the cue was at centroid.

Empirical Evaluation of Centroid-based Models for Single-label Text Categorization

Abstract: Centroid-based models have been used in Text Categorization because, despite their computational simplicity, they show a robust behavior and good performance. In this paper we experimentally evaluate several centroidbased models on single-label text categorization tasks. We also analyze document length normalization and two different term weighting schemes. We show that: (1) Document length normalization is not always the best option in a classification task. (2) The traditional tfidf term weighting approach remains very effective, even when compared to more recent approaches. (3) Despite the fact that several ways to calculate the centroid of a class in a dataset have been proposed, there is one that always outperforms the others. (4) A computationally simple and fast centroid-based model can give results similar to the top-performing SVM model.

Algorithm for CCD Star Image Rapid Locating

Abstract: Rapid processing of large scale CCD star image is a key technique for new type of star sensor as well as space surveillance sensor. The foreground of CCD star image is differ form the background distinctly. Majority pixels of the image belong of the background. The typical histogram of CCD star image has a two-peak values structure. Base on those analyses, a strategy for star image's pretreatment was presented. The background and noise of the image is removed by a window transform of the pixel gray, all pixel whose gray value below the threshold will be set as a black pixel, the gray of which will be set as zero videlicet. The threshold is determined through the numeric statement of the background pixels by an iterative algorithm. Then a new algorithm for star point locating was presented by a two-step projection checking method. The horizon domains of the star points can be determined through the vertical projection checking of the image at first, then the horizon domain of each star point can be determined through the horizon projection in the horizon domains of the star points. The precise position of the star points is calculated using centroid method at last. The new algorithm is simpler and has smaller consumption of computing and memory than tradition connected domain methods. Experiments on real star images with size of 2048 x 2048 indicated the validity of the algorithm.

Numerical study of centroid detection accuracy for Shack-Hartmann wavefront sensor

Abstract: It is crucial for the wavefront sensor to reduce the influence of noise and enhance the detection accuracy of the centroid, which is also an important step for improving the performances of adaptive optics. In this paper, based on the theoretical analysis and by utilizing the Monte-Carlo simulation (MCS) technology, the results obtained by using the centroid algorithms have been compared in detail for the case, in which centroid of the signal is not at the center of the detection area and it also has a relatively large area. It is shown that the size of the signal spot and its centroid position have considerable influences on the centroid detection accuracy. The numerical results agree well with the theoretical predictions and the optimized parameters for each algorithm have been found. The study will be helpful for further optimizing the optical system and improving the centroid measurement accuracy of the wavefront sensor. (C) 2005 Elsevier Ltd. All rights reserved.

Fingerprint Registration Using Minutia Clusters and Centroid Structure 1

Abstract: In this paper a novel distortion-tolerant fingerprint registration method based on clustering is proposed. In this method, minutiae features of the query fingerprint are divided into various clusters. Several local structure transformations are estimated by local structure sets. Then the global structures (Centroid Structures) are constructed according to the local structure transformation. The global transformation is determined by the score of local structure transformation together with the similarity level of the global structure. Experimental results show that this algorithm is robust for aligning fingerprints with a small number of minutia and heavy distortions. Such situations are often encountered in forensic applications.

Enhanced centroid-based cdlassification technique by filtering outliers

Abstract: Document clustering or unsupervised document classification has been used to enhance information retrieval. Recently this has become an intense area of research due to its practical importance. Outliers are the elements whose similarity to the centroid of the corresponding category is below some threshold value. In this paper, we show that excluding outliers from the noisy training data significantly improves the performance of the centroid-based classifier which is the best known method. The proposed method performs about 10% better than the centroid-based classifier.

Moment based method for feature indentification in digital images

Abstract: A method for identifying features in digital images. The method includes, providing a digital image of a plurality of pixels having one or more features to be identified; providing a feature model having one or more parameters characteristic of a feature to be identified, wherein the feature model has a centroid; and distributing a plurality of test Regions of Interest (ROIs) over the digital image, so that every pixel of the digital image is covered by one or more test ROIs, wherein each test ROI has the same parameter(s) as the feature model, including its centroid. The method then includes for each test ROI, calculating the intensity moment of the image region bounded by the test ROI and if the centroid of the test ROI is offset from the intensity moment, moving the test ROI closer to the intensity moment and reiterating these steps until the centroid and intensity moment have substantially converged, and then processing the next test ROI; determining which ROIs are candidate ROIs; removing duplicate ROIs where two or more candidate ROIs identify the same feature; and outputting the list of candidate ROIs, the positions of which identify the features of interest in the provided image.

Enhanced centroid-based classification technique by filtering outliers

Abstract: Document clustering or unsupervised document classification has been used to enhance information retrieval Recently this has become an intense area of research due to its practical importance Outliers are the elements whose similarity to the centroid of the corresponding category is below some threshold value In this paper, we show that excluding outliers from the noisy training data significantly improves the performance of the centroid-based classifier which is the best known method The proposed method performs about 10% better than the centroid-based classifier

Evaluation of Nonlinear Quantum Time Correlation Functions within the Centroid Dynamics Formulation

Abstract: A method to evaluate nonlinear centroid correlation functions is presented that is amenable to simple numerical computation. It can be implemented with the centroid molecular dynamics method for approximate quantum dynamics with no additional assumptions. Two nonlinear correlation functions are evaluated for a model potential using this scheme and compared with results from exact quantum calculations.