Abstract: The Hough transform is a popular technique used in the field of image processing and computer vision. With a Hough transform technique, not only the normal angle and distance of a line but also the line-segment’s length and midpoint (centroid) can be extracted by analysing the voting distribution around a peak in the Hough space. In this paper, a method based on minimum-entropy analysis is proposed to extract the set of parameters of a line segment. In each column around a peak in Hough space, the voting values specify probabilistic distributions. The corresponding entropies and statistical means are computed. The line-segment’s normal angle and length are simultaneously computed by fitting a quadratic polynomial curve to the voting entropies. The line-segment’s midpoint and normal distance are computed by fitting and interpolating a linear curve to the voting means. The proposed method is tested on simulated images for detection accuracy by providing comparative results. Experimental results on real-world images verify the method as well. The proposed method for line-segment detection is both accurate and robust in the presence of quantization error, background noise, or pixel disturbances.

Abstract: Shape and geometry features are encoded from contour of the hand only.Robust preprocessing is introduced to cope with the noise and disjoint fingers.Hand orientation and finger registration is applied to provide more flexibility.Two level score fusion is adopted to enhance the verification performance.Promising results are obtained over contact and contactless (IITD) datasets. A multimodal biometric system for personal identity verification is proposed using hand shape and hand geometry in this paper. Shape and geometry features are derived with the help of only contour of the hand image for which only one image acquisition device is sufficient. All the processing is done with respect to a stable reference point at wrist line which is more stable as compared to the centroid against the finger rotation and peaks and valleys determination. Two shape based features are extracted by using the distance and orientation of each point of hand contour with respect to the reference point followed by wavelet decomposition to reduce the dimension. Seven distances are used to encode the geometrical information of the hand. Shape and geometry based features are fused at score levels and their performances are evaluated using standard ROC curves between false acceptance rate, true acceptance rate, equal error rate and decidability index. Different similarity measures are used to examine the accuracy of the introduced method. Performance of system is analyzed for shape based (distance and orientation) and geometrical features individually as well as for all possible combinations of feature and score level fusion. The proposed features and fusion methods are studied over two hand image datasets, (1) JUET contact database of 50 subjects having 10 templates each and (2) IITD contactless dataset of 240 subjects with 5 templates each. The proposed method outperforms other approaches with the best 0.31% of EER.

Abstract: A novel interferometric method - SLIVER (Super Localization by Image inVERsion interferometry) - is proposed for estimating the separation of two incoherent point sources with a mean squared error that does not deteriorate as the sources are brought closer. The essential component of the interferometer is an image inversion device that inverts the field in the transverse plane about the optical axis, assumed to pass through the centroid of the sources. The performance of the device is analyzed using the Cram\'er-Rao bound applied to the statistics of spatially-unresolved photon counting using photon number-resolving and on-off detectors. The analysis is supported by Monte-Carlo simulations of the maximum likelihood estimator for the source separation, demonstrating the superlocalization effect for separations well below that set by the Rayleigh criterion. Simulations indicating the robustness of SLIVER to mismatch between the optical axis and the centroid are also presented. The results are valid for any imaging system with a circularly symmetric point-spread function.

Abstract: A big challenge in text classification is to perform classification on a large-scale and high-dimensional text corpus in the presence of imbalanced class distributions and a large number of irrelevant or noisy term features. A number of techniques have been proposed to handle this challenge with varying degrees of success. In this paper, by combining the strengths of two widely used text classification techniques, K-Nearest-Neighbor (KNN) and centroid based (Centroid) classifiers, we propose a scalable and effective flat classifier, called CenKNN, to cope with this challenge. CenKNN projects high-dimensional (often hundreds of thousands) documents into a low-dimensional (normally a few dozen) space spanned by class centroids, and then uses the $$k$$ k -d tree structure to find $$K$$ K nearest neighbors efficiently. Due to the strong representation power of class centroids, CenKNN overcomes two issues related to existing KNN text classifiers, i.e., sensitivity to imbalanced class distributions and irrelevant or noisy term features. By working on projected low-dimensional data, CenKNN substantially reduces the expensive computation time in KNN. CenKNN also works better than Centroid since it uses all the class centroids to define similarity and works well on complex data, i.e., non-linearly separable data and data with local patterns within each class. A series of experiments on both English and Chinese, benchmark and synthetic corpora demonstrates that although CenKNN works on a significantly lower-dimensional space, it performs substantially better than KNN and its five variants, and existing scalable classifiers, including Centroid and Rocchio. CenKNN is also empirically preferable to another well-known classifier, support vector machines, on highly imbalanced corpora with a small number of classes.

Abstract: A randomized algorithm is substantiated for the strongly NP-hard problem of partitioning a finite set of vectors of Euclidean space into two clusters of given sizes according to the minimum-of-the sum-of-squared-distances criterion. It is assumed that the centroid of one of the clusters is to be optimized and is determined as the mean value over all vectors in this cluster. The centroid of the other cluster is fixed at the origin. For an established parameter value, the algorithm finds an approximate solution of the problem in time that is linear in the space dimension and the input size of the problem for given values of the relative error and failure probability. The conditions are established under which the algorithm is asymptotically exact and runs in time that is linear in the space dimension and quadratic in the input size of the problem.

Abstract: The paper is focused on the forecasting method for time series groups with the use of algorithms for cluster analysis. K-means algorithm is suggested to be a basic one for clustering. The coordinates of the centers of clusters have been put in cor- respondence with summarizing time series data - the centroids of the clusters. A description of time series, the centroids of the clusters, is implemented with the use of forecasting models. They are based on strict binary trees and a modified clonal selection algorithm. With the help of such forecasting models, the possibil- ity of forming analytic dependences is shown. It is suggested to use a common forecasting model, which is constructed for time series - the centroid of the cluster, in

Abstract: Estimation of frequency-dependent ultrasonic attenuation is an important aspect of tissue characterization Along with other acoustic parameters studied in quantitative ultrasound, the attenuation coefficient can be used to differentiate normal and pathological tissue The spectral centroid downshift (CDS) method is one the most common frequencydomain approaches applied to this problem In this study, a statistical analysis of this method’s performance was carried out based on a parametric model of the signal power spectrum in the presence of electronic noise The parametric model used for the power spectrum of received RF data assumes a Gaussian spectral profile for the transmit pulse, and incorporates effects of attenuation, windowing, and electronic noise Spectral moments were calculated and used to estimate second-order centroid statistics A theoretical expression for the variance of a maximum likelihood estimator of attenuation coefficient was derived in terms of the centroid statistics and other model parameters, such as transmit pulse center frequency and bandwidth, RF data window length, SNR, and number of regression points Theoretically predicted estimation variances were compared with experimentally estimated variances on RF data sets from both computer-simulated and physical tissue-mimicking phantoms Scan parameter ranges for this study were electronic SNR from 10 to 70 dB, transmit pulse standard deviation from 05 to 41 MHz, transmit pulse center frequency from 2 to 8 MHz, and data window length from 3 to 17 mm Acceptable agreement was observed between theoretical predictions and experimentally estimated values with differences smaller than 005 dB/cm/MHz across the parameter ranges investigated This model helps predict the best attenuation estimation variance achievable with the CDS method, in terms of said scan parameters

Abstract: In this article, we define the general Lp -centroid bodies, which extend the notion of Lp -centroid bodies by Lutwak and Zhang. Further, we generalize the two monotone inequalities by Wang, Lu and Leng, and establish the Brunn-Minkowski type inequalities of dual quermassintegrals for this new notion. In particular, the extremal values of dual quermassintegrals of the polars of general Lp -centroid bodies are also provided. Mathematics subject classification (2010): 52A20, 52A40.

Abstract: We present an approach for correcting the bias in 3D reconstruction of points imaged by a calibrated stereo rig. Our analysis is based on the observation that, due to quantization error, a 3D point reconstructed by triangulation essentially represents an entire region in space. The true location of the world point that generated the triangulated point could be anywhere in this region. We argue that the reconstructed point, if it is to represent this region in space without bias, should be located at the centroid of this region, which is not what has been done in the literature. We derive the exact geometry of these regions in space, which we call 3D cells, and we show how they can be viewed as uniform distributions of possible pre-images of the pair of corresponding pixels. By assuming a uniform distribution of points in 3D, as opposed to a uniform distribution of the projections of these 3D points on the images, we arrive at a fast and exact computation of the triangulation bias in each cell. In addition, we derive the exact covariance matrices of the 3D cells. We validate our approach in a variety of simulations ranging from 3D reconstruction to camera localization and relative motion estimation. In all cases, we are able to demonstrate a marked improvement compared to conventional techniques for small disparity values, for which bias is significant and the required corrections are large.

Abstract: Pedestrian motion type classification is proposed in this work. The model incorporates the pedestrian pose recognition and lateral speed, motion direction and spatial layout of the environment. Pedestrian poses are recognized according to the spatial body language ratio. The center of mass of the body relative to its width and height is used to define the pedestrian pose. Motion trajectory is obtained by using point tracking on the centroid of detected human region. And then estimated velocity is determined. Spatial layout is determined by the distance of the pedestrian to the road lane boundary. These models will be then hierarchically separated according to their action (walking, starting, bending and stopping). In order to classify the pedestrian crossing road, a walking human model is proposed. A walking human is defined by ratio of the centroid location from the ground plane divided by the height of bounding box. It should satisfy a constraint. The proposed algorithms are evaluated using publicly (Caltech and ETH) datasets and our pedestrian dataset. The performance results shown the correct pedestrian crossing road classification is 98.10%.

Abstract: In analog designs, the most widely adopted layout practice to improve matching is the symmetrical common-centroid placement. However, this arrangement cannot be obtained in general. In this paper, it is shown that there are asymmetrical placements with a common centroid which are also immune to process gradients and suitable for designs where a symmetrical layout is not possible. In addition, this paper proposes an automated method, based on a standard simulated annealing framework, to arrange fully-integrated capacitors in a layout to improve their matching.

Abstract: An improved centroid extraction algorithm for autonomous star sensor is proposed in this study, which focuses on the improvements of the location accuracy of stars and the speed of the centroid extraction. First, the coarse positioning of stars is carried out to achieve the dispersive regions of the stars quickly. Then the stars pixels are chosen by the automatic seeded region growing algorithm. Subsequently, in order to restrain the interference of noise, the grey values of the stars pixels are modified according to the characteristics of the star energy distribution. Finally, the fine positioning of the star can be achieved using the proposed centroid calculation formula. Experimental results show that the proposed algorithm has high-positioning accuracy and good noise resistant ability compared with the other two centroid extraction algorithms. Moreover, the computational complexity of the proposed algorithm is lower than that of the other two algorithms.

Abstract: This paper proposes a method for automated extraction of street trees in a typical urban environment from 3D point cloud data acquired by the mobile laser scanning system First, the algorithm utilizes the voxel-based method to remove the ground points from the scene Second, the Euclidean distance clustering is adopted to cluster points into individual objects The eigenvalues of neighborhood covariance matrix and the corresponding normalized centroid distance are computed for each point to obtain the subdivided dimensional features Finally, the statistical component features and horizontal information are calculated for object detection The experiment results show the feasibility of the proposed algorithm

Abstract: A kinematic description of a star spot in the focal plane is presented for star sensors under dynamical conditions, which involves all necessary parameters such as the image motion, velocity, and attitude parameters of the vehicle. Stars at different locations of the focal plane correspond to the slightly different orientation and extent of motion blur, which characterize the space-variant point spread function. Finally, the image motion, the energy distribution, and centroid extraction are numerically investigated using the kinematic model under dynamic conditions. A centroid error of eight successive iterations <0.002 pixel is used as the termination criterion for the Richardson-Lucy deconvolution algorithm. The kinematic model of a star sensor is useful for evaluating the compensation algorithms of motion-blurred images.

Abstract: We consider some different distance-based "central parts" of a tree including sets of vertices that minimize the sum of distances to: all other vertices (the centroid of a tree), all leaves (the leaf-centroid of a tree), all internal vertices (the internal-centroid of a tree). The subgraphs induced by these "central parts" are briefly discussed. Regarding their relative locations in the same tree, it is shown that the centroid is always located in the "middle" of the leaf-centroid and internal-centroid. In a tree T of order n, the distance between the leaf-centroid and the centroid or the internal centroid can be as large as $${\frac{n}{2}}$$ n 2 (asymptotically); the distance between the internal centroid and the centroid, however, can only be as large as $${\frac{n}{4}}$$ n 4 (asymptotically). All extremal cases are obtained by the so called comets. We also point out that this study can be further generalized to trees with additional constraints on the diameter or vertex degrees. The arguments are very similar but with more technical calculations.

Abstract: In geometric calibration of cone-beam computed tomography (CBCT), sphere-like objects such as balls are widely imaged, the positioning information of which is obtained to determine the unknown geometric parameters. In this process, the accuracy of the detector location of CB projection of the center of the ball, which we call the center projection, is very important, since geometric calibration is sensitive to errors in the positioning information. Currently in almost all the geometric calibration using balls, the center projection is invariably estimated by the center of the support of the projection or the centroid of the intensity values inside the support approximately. Clackdoyle's work indicates that the center projection is not always at the center of the support or the centroid of the intensity values inside, and has given a quantitative analysis of the maximum errors in evaluating the center projection by the centroid. In this paper, an exact method is proposed to calculate the center projection, utilizing both the detector location of the ellipse center and the two axis lengths of the ellipse. Numerical simulation results have demonstrated the precision and the robustness of the proposed method. Finally there are some comments on this work with non-uniform density balls, as well as the effect by the error occurred in the evaluation for the location of the orthogonal projection of the cone vertex onto the detector.

Abstract: Centroid computation of Gaussian spot is often conducted to get the exact position of a target or to measure wave-front slopes in the fields of target tracking and wave-front sensing. Center of Gravity (CoG) is the most traditional method of centroid computation, known as its low algorithmic complexity. However both electronic noise from the detector and photonic noise from the environment reduces its accuracy. In order to improve the accuracy, thresholding is unavoidable before centroid computation, and optimum threshold need to be selected. In this paper, the model of Gaussian spot is established to analyze the performance of optimum threshold under different Signal-to-Noise Ratio (SNR) conditions. Besides, two optimum threshold selection methods are introduced: TmCoG (using m % of the maximum intensity of spot as threshold), and TkCoG ( usingμn +κσ n as the threshold), μn and σn are the mean value and deviation of back noise. Firstly, their impact on the detection error under various SNR conditions is simulated respectively to find the way to decide the value of k or m. Then, a comparison between them is made. According to the simulation result, TmCoG is superior over TkCoG for the accuracy of selected threshold, and detection error is also lower.

Abstract: We present a robust algorithm that registers one point set to another for nonrigid case. We formulate the problem as a Gaussian mixture model (GMM) density estimation by considering one of the point sets as the GMM centroids and the other as the data points generated by GMM. We displace the centroids and make them register to the data by maximizing the likelihood. To facilitate the process, we introduce a set of weights which provide the proximity information among pairs of points of both point sets and iteratively update the displacement and weights in alternating steps. We propose a priority based combination to update the proximity weights, which leverages the richness of Shape Context (SC). In the displacement updation step, we propose a graph-Laplacian regularization which helps in preserving the intrinsic geometry of the point set to be displaced. We also introduce a fast algorithm that reduces the computation complexity significantly. We apply our method on publicly available datasets. Our results validate the robustness of our approach by outperforming current state-of-the-art methods.

Abstract: This paper focuses on clustering analysis using a K-means approach for fatigue feature dataset extraction. The aim of this study is to group the dataset as closely as possible (homogeneity) for the scattered dataset. Kurtosis, the wavelet-based energy coefficient and fatigue damage are calculated for all segments after the extraction process using wavelet transform. Kurtosis, the wavelet-based energy coefficient and fatigue damage are used as input data for the K-means clustering approach. K-means clustering calculates the average distance of each group from the centroid and gives the objective function values. Based on the results, maximum values of the objective function can be seen in the two centroid clusters, with a value of 11.58. The minimum objective function value is found at 8.06 for five centroid clusters. It can be seen that the objective function with the lowest value for the number of clusters is equal to five; which is therefore the best cluster for the dataset.

Abstract: This paper present a power lines automatic extraction method from airborne LiDAR point cloud. Firstly, ground points are removed by automatic filtering method based on fluctuant feature of terrain. Vegetation points, building points and part pylon points are also removed by dimensionality feature and direction feature. Secondly, 2D Hough transform and least square fitting are used to fit center line equation of power lines. And then, the laser point of each power lines can be obtained by center line equation. In this step, power lines projection overlap in the horizontal plane are considered as well. Finally, block centroid calculation method is used to calculate 3D nodes of each power lines. These nodes are used to output the power lines vector. The experimental result shows that the proposed method can extract complete power lines from airborne LiDAR point cloud. This method has some practical meaning for power line inspection.

Abstract: Embodiments for categorizing a real-time log event are described. In one example, a Term Frequency-Inverse Document Frequency (TF-IDF) vector for the log event is computed based on pre-calculated TF-IDF matrix of log corpus and number of new words in log event, where log corpus comprises one or more pre-existing log events, and where the log event is indicative of error message. Further, distance between TF-IDF vector and cluster centroid of each cluster in the log corpus is calculated. Thereafter, cluster having closest cluster centroid is identified from amongst the clusters based on distance between TF-IDF vector and cluster centroid of each of the clusters, where closest cluster centroid is cluster centroid closest to TF-IDF vector. Subsequently, log event is categorized into one or more log categories based on comparison of distance between TF-IDF vector and closest cluster centroid pre-determined silhouette threshold corresponding to cluster with closest cluster centroid.

Abstract: This paper discusses collective control of multiple unicycle-type vehicles with nonholonomic constraints and non-identical constant speeds, with focus on the design of a tracking controller for a desired target's position and velocity. The tracking control task is divided into several sub-tasks. The group centroid is controlled to track a desired reference velocity, while the reference velocity is constructed with information from the target's position and velocity so that the group centroid can successfully track both the position and velocity of a target vehicle. In order to keep all the agents close to the centroid within a reasonable spacing, an additive spacing controller is devised. We also discuss in detail the performance limitation and trade-offs of the tracking control due to the constraint of non-identical and constant speeds in such a heterogeneous agent group.

Abstract: The invention provides an automatic human tumble detecting method based on Kinect skeleton tracking The automatic human tumble detecting method includes tracking skeleton of a human body through Kinect, acquiring spatial coordinates of six skeleton joints including head, left shoulder, right shoulder, left hip, right hip and the center between hips (defined as human body centroid point), calculating motion speed of the human body centroid point and the distance between the human body centroid point and the ground surface, excluding non-tumble events, timing the stationary state of the human body centroid point to form six judgment conditions, judging whether human tumble events occur or not, and alarming through cellphone texts once tumble events occur The automatic human tumble detecting method is low in misjudgment rate, and without wearable sensing devices and dependence on visible light, 24-hour continuous real-time detection of the human body can be realized

Abstract: The drop in cost and reduction in size of sensor nodes has eased the development of wireless sensor networks (WSNs) applications. However, the noise and disturbing nature of most sensing environments require accurate algorithms that can overcome these difficulties. Nodes' localization is one of the basic activity a WSN can perform to make other network's functionalities, such as routing easy to tackle. Nowadays there exists many localization methods, however many pose computational and/or accuracy issues. Centroid is a localization algorithm by which an unknown node's coordinates are estimated as the centroid of anchors' coordinates. Its implementation is simple but it has a high error rate. In this paper, two methods are proposed to enhance the centroid localization algorithm. The first, Linear Weighting Centroid (LWC) uses the distance between the anchor and the unknown nodes to linearly weight each anchor's coordinates. The second, the Neighbor Weighting Centroid (NWC) uses the number of intersect nodes between an unknown node and its neighbor anchors to estimate the degree of proximity of the anchor nodes. Both methods assign larger weights to closer anchors and lesser weights to remote anchors to improve centroid accuracy while keeping computation almost at the same level. Simulation is used to study the performance of both mechanisms. The results show that for a large WSN, both methods localize unknown nodes with better position accuracy than centroid, with LWC performing better than NWC.

Abstract: Video surveillance in a dynamic environment is one of the current challenging research topics in computer vision. In video surveillance, detection of moving objects from a video is important for object detection, target tracking, and behavior understanding. The present work is about locating a moving object (or multiple objects) over a time using a stationary camera and associating it in consecutive video frames. In this perspective, a video captured by digital camera is used for motion analysis. In the first stage of experiment background subtraction and frame differencing algorithms are used for object detection and its motion is estimated by associating the centroid of the moving object in each differenced frame. Tracking of non-stationary foreground regions is one of the most critical requirements for surveillance systems. In the second stage of experiment same algorithm is chosen for object detection but motion of each track is estimated by Kalman filter. However the best estimate is made by combining the knowledge of prediction and correction mechanisms that were incorporated as part of Kalman filter design. Subsequently kernel based tracking using mean shift theory is implemented for tracking single object under partial occlusion. Histogram based target representations are regularized by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions that are suitable for gradient-based optimization. In this regard a metric derived from the Bhattacharyya Coefficient is used as similarity measure, and subsequently mean shift theory is used to perform the optimization. In order to improve the track efficiency, an object tracking algorithm using Kalman filter (KF) combined with mean shift (MS) is also proposed. Firstly, the system model of KF is constructed, and the center of the object predicted by KF is used in MS algorithm for finding the target in the frame. The result obtained from the mean shift is given to KF as a measurement and is correctly updated using correction technique. The corrected value is taken as a reference position by mean shift for finding the object location in the successive frame. Again the obtained position from the mean shift is sent to KF for correction. The idea of combining Kalman filter theory and mean shift theory has given a direction in bringing out the efficient and reliable tracking results in case of partial occlusion.

Abstract: 3Dscanning devices generally produce a large amount of dense points.This paper presents a feature preserving point cloud simplification method to reduce redundant points while preserving original geometric features well.Firstly,K-mean clustering algorithm was employed to globally gather similar points in a spatial domain.By constructing aK-dtree structure for the point cloud,some nodes of the K-d tree were used as initial clustering centroids.Then,normal vector of point cloud and candidate feature points were estimated with principal component analysis method.Traversing every cluster,if feature points were contained in the cluster,the cluster was subdivided into a series of sub-clusters and the cluster was mapped to a Gaussian sphere.Finally,adaptive mean shift algorithm was employed to classify the data in Gaussian sphere and the clusters in Gaussian sphere were corresponded to the sub-clusters in the spatial domain.The cluster centroids present the simplification data.Several real object models were used to verify the effectiveness of the proposed method.The experiment results demonstrate that the proposed method generates sparse sampling points in flat areas and high density points in high curvature regions.As comparing with the nonuniform grid,hierarchical agglomerative,and K-means methods,the proposed method obtains thesmallest simplification error and preserves original geometric features.