Abstract: This paper presents a data clustering approach using modified K-Means algorithm based on the improvement of the sensitivity of initial center (seed point) of clusters. This algorithm partitions the whole space into different segments and calculates the frequency of data point in each segment. The segment which shows maximum frequency of data point will have the maximum probability to contain the centroid of cluster. The number of cluster's centroid (k) will be provided by the user in the same manner like the traditional K-mean algorithm and the number of division will be k∗k (‘k’ vertically as well as ‘k’ horizontally). If the highest frequency of data point is same in different segments and the upper bound of segment crosses the threshold ‘k’ then merging of different segments become mandatory and then take the highest k segment for calculating the initial centroid (seed point) of clusters. In this paper we also define a threshold distance for each cluster's centroid to compare the distance between data point and cluster's centroid with this threshold distance through which we can minimize the computational effort during calculation of distance between data point and cluster's centroid. It is shown that how the modified k-mean algorithm will decrease the complexity & the effort of numerical calculation, maintaining the easiness of implementing the k-mean algorithm. It assigns the data point to their appropriate class or cluster more effectively.

Abstract: Previous studies have shown that the centroid of a general type-2 fuzzy set (T2 FS) A can be obtained by taking the union of the centroids of all the α-planes (each raised to level α) of A. Karnik-Mendel (KM) or the enhanced KM (EKM) algorithms are used to compute the centroid of each α-plane. The iterative features in KM/EKM algorithms can be time-consuming, especially when the algorithms have to be repeated for many α-planes. This paper proposes a new method named centroid-flow (CF) algorithm to compute the centroid of A without having to apply KM/EKM algorithms for every α-plane. Extensive simulations have shown that the CF algorithm can reduce the computation time by 75%-80 % and 50% -75%, compared with KM and EKM algorithms, respectively, and still maintains satisfactory computation accuracy for various T2 FSs when the primary variable x and α -plane are discretized finely enough.

Abstract: Conventional centroid estimation fits a template point spread function (PSF) to image data. Because the PSF is typically not known to high accuracy, systematic errors exist. Here, we present an accurate centroid displacement estimation algorithm by reconstructing the PSF from Nyquist-sampled images. In absence of inter-pixel response variations, this method can estimate centroid displacement between two 32×32 images to sub-micropixel accuracy. Inter-pixel response variations can be calibrated in Fourier space by using laser metrology. The inter-pixel variations of Fourier transforms of the pixel response functions can be conveniently expressed in terms of powers of spatial wavenumbers. Calibrating up to the third-order terms in the expansion, the displacement estimation is accurate to a few micro-pixels. This algorithm is applicable to a new mission concept of performing mirco-arcsecond level relative astrometry using a 1 m telescope for detecting terrestrial exoplanets and high-precision photometry missions.

Abstract: This paper evaluates experimentally approaches to tracking dynamic objects using 3D LIDAR data such as that obtained from a Velodyne HDL-64. Whilst many algorithms exist for tracking objects in 2D laser data, only recently have algorithms which model the 3D appearance of tracked objects been proposed. The benets of these algorithms with respect to model free centroid based methods have not been fully examined, and therefore the key contribution of this paper is to analyse the behaviour of dierent implementations of these two broad approaches. Through a quantied comparison of simple tracking methods for 3D LIDAR data validated against manually labelled tracks it was found that centroid tracking outperformed 3D appearance model based techniques. In the experiments, which focused upon pedestrian tracking in particular, centroid tracking achieved up to 95% correct data associations compared to 60% for the appearance model based methods.

Abstract: A decentralized controller-observer scheme for centroid tracking with a multi-robot system is presented. The key idea is to develop, for each robot, an observer of the collective system's state; each local observer is updated by only using information of the state of the robot and of its neighbors. The local observers' estimations are then used by the individual robots to cooperatively track an assigned time-varying reference for the weighted centroid. Convergence of the scheme is proven for both fixed and switching communication topologies, as well as for directed and undirected communication graphs. Numerical simulations relative to different case studies are illustrated to validate the approach.

Abstract: The present invention provides a gesture recognition device which can accurately recognize a user's gesture in a free space with a simple configuration, and which is mounted on a processing unit and which causes the processing unit to execute an operation corresponding to the recognized gesture. The gesture recognition device ( 1000 ) comprises a palm centroid determining unit ( 30 ) for determining centroid of a palm, a palm area determining unit ( 40 ) for determining the area of a palm, a finger length/angle determining unit ( 50 ) for calculating length of finger by obtaining the length between the centroid and the fingertips and for calculating the angles formed by lines connecting the centroid and the fingertips, and a gesture identifying unit ( 60 ) for identifying the gesture by the combination of the variation of the centroid of the palm, the change of the palm area, the change of length between the centroid and the fingertips and the change of angles formed by the lines connecting the centroid and the fingertips.

Abstract: Visual sensing is an attractive approach to detect the weld position in an arc-welding process, which provides information for seam tracking However, it is difficult to accurately detect the weld position adjacent to a molten pool because of strong arc disturbances A novel algorithm based on the weld-pool image centroid is presented to improve the seam-tracking ability The molten pool images are taken by a camera arranged ahead of the welding torch and the centroid is extracted as a parameter to detect the weld position It is worth noting that the centroid corresponds to the thermal distribution of the molten pool affected by the offset between the arc and the seam centreline Therefore the offset between the arc and the seam centreline can be estimated by this centroid The least square linear regression method is employed to correlate the relationship between the centroid and the offset under different welding conditions For further analysis of the centroid characteristics, a non-linear neural network is designed with three input variables which are the position, displacement and moving velocity of the centroid, respectively This neural network model shows higher accuracy of weld detection In comparison with directly detecting the weld position, the centroid can be measured and computed easily This algorithm is expected to provide a promising vision model to improve the accuracy of seam tracking in real time, and subsequently to ensure good welding quality

Abstract: To simplify the algorithm for determining the surface plasmon resonance (SPR) angle for special applications and development trends, a fast method for determining an SPR angle, called the fixed-boundary centroid algorithm, has been proposed. Two experiments were conducted to compare three centroid algorithms from the aspects of the operation time, sensitivity to shot noise, signal-to-noise ratio (SNR), resolution, and measurement range. Although the measurement range of this method was narrower, the other performance indices were all better than the other two centroid methods. This method has outstanding performance, high speed, good conformity, low error and a high SNR and resolution. It thus has the potential to be widely adopted.

Abstract: The invention discloses a centroid location algorithm based on RSSI (Received Signal Strength Indication) correction for a wireless sensor network, and the centroid location algorithm comprises: (1) anchor nodes periodically broadcast information around, wherein the information contains respective node ID (identity) and coordinates; and ordinary nodes average the RSSI of the same anchor node after receiving the information; (2) the ordinary nodes do not receive new information any more after collecting the information of n anchor nodes, wherein n is greater than 3 and less than or equal to 100; the ordinary nodes sort the anchor nodes according to the RSSI from strong to weak and establish mapping of the RSSI value and the distance from the nodes to the anchor nodes; (3) the anchor nodes with the RSSI value greater than or equal to minus 93dbm and less than or equal to minus 113dbm is selected for self-location calculation; and (4) the calculated unknown node coordinate set is aggregated and averaged to obtain the unknown node coordinates. In the invention, the point-to-point distance between the nodes is measured by correcting an RSSI ranging technology, and a triangle centroid location algorithm is adopted for location, thus the measurement error of the RSSI is reduced. Compared with the triangle centroid location algorithm based on the RSSI, the centroid location algorithm provided by the invention has the advantage that the location accuracy is greatly improved.

Abstract: The nearest shrunken centroid (NSC) methodology, originally developed for high-dimensional genomics problems, was recently applied in a stylometric study. Although NSC has many advantages, stylometric problems usually differ from genomics problems in several important ways: texts are of a wide range of sizes, a large series of texts are often the subjects for classification, and most importantly the set of candidate authors cannot usually be assumed to be closed. Consequently, naı̈ve application of NSC methodology can produce misleading results. We extend the NSC methodology for more general application to stylometry. Reanalysis of the Book of Mormon using the open-set NSC method produced dramatically different results from a closed-set NSC analysis. .................................................................................................................................................................................

Abstract: The star centroid estimation is the most important operation, which directly affects the precision of attitude determination for star sensors. This paper presents a theoretical study of the systematic error introduced by the star centroid estimation algorithm. The systematic error is analyzed through a frequency domain approach and numerical simulations. It is shown that the systematic error consists of the approximation error and truncation error which resulted from the discretization approximation and sampling window limitations, respectively. A criterion for choosing the size of the sampling window to reduce the truncation error is given in this paper. The systematic error can be evaluated as a function of the actual star centroid positions under different Gaussian widths of star intensity distribution. In order to eliminate the systematic error, a novel compensation algorithm based on the least squares support vector regression (LSSVR) with Radial Basis Function (RBF) kernel is proposed. Simulation results show that when the compensation algorithm is applied to the 5-pixel star sampling window, the accuracy of star centroid estimation is improved from 0.06 to 6 × 10−5 pixels.

Abstract: Methods and apparatus for interactively rotating three-dimensional (3D) objects using multitouch gestures. To perform a roll gesture, multiple touch points are detected on a multitouch-enabled device. The touch points are associated with, or select, a 3D object displayed on the device. The centroid of the touch points is computed, and motion of the centroid, resulting from motion of the touch points, is tracked. When motion of the centroid is detected, a displacement is obtained, and the displacement is mapped to a rotation transformation. The 3D object may then be rotated according to the rotation transformation, and a 2D projection of the rotated 3D object is displayed. If the number of touch points changes, rotation may be reset without rotating the object. Alternatively, displacement from the previous centroid to the new centroid is determined and the object is rotated accordingly.

Abstract: In the community of information retrieval, Centroid Classifier has been showed to be a simple and yet effective method for text categorization. However, it is often plagued with model misfit (or inductive bias) incurred by its assumption. Various methods have been proposed to address this issue, such as Weight Adjustment, Voting, Refinement and DragPushing. However, existing methods employ only one criterion, i.e., training-set error. Researches in machine learning indicate that training-set error based method cannot guarantee the generalization capability of base classifiers for unseen examples. To overcome this problem, we propose a novel Model Adjustment algorithm, which makes use of training-set errors as well as training-set margins. Furthermore, we prove that for a linearly separable problem, proposed method converges to the optimal solution after finite updates using any learning parameter @h(@h>0). The empirical assessment conducted on four benchmark collections indicates that proposed method performs slightly better than SVM classifier in prediction accuracy, as well as beats it in running time.

Abstract: Intuitionistic Fuzzy C-means (IFCM) is a robust clustering method which is based upon intuitionistic fuzzy set theory. It uses Euclidean distance as a distance metric, hence can only cluster hyper spherically distributed data-sets in data space or in feature space. FCM and KFCM with a new distance measure (FCM-σ and KFCM-σ) can detect non-hyperspherical clusters in data space and feature space but they are sensitive to noise and produce inefficient results in the presence of noise. This paper present a robust Intuitionistic Fuzzy c-means(IFCM-σ) and a robust kernel Intutitionistic Fuzzy C-Means(KIFCM-σ) with a new distance metric that incorporates the distance variation in a cluster to regularize the distance between data point and the cluster centroid. Propose algorithms are the hybridization of IFCM, kernel function, and new distance metric in the data space and in the feature space which avoid various problems of IFCM and FCM-σ. Experiments are done using two-dimensional synthetic data-sets and noisy digital images, and results are compared with IFCM, KIFCM, FCM-σ and KFCM-σ. The results show that our proposed algorithms, especially KIFCM-σ are more effective.

Abstract: This paper presents a methodology for shape recognition that focuses on dealing with the difficult problem of large deformations. The proposed methodology consists in a novel feature extraction technique, which uses a non-rigid representation adaptable to the shape. This technique employs a deformable grid based on the computation of geometrical centroids that follows a region partitioning algorithm. Then, a feature vector is extracted by computing pixel density measures around these geometrical centroids. The result is a shape descriptor that adapts its representation to the given shape and encodes the pixel density distribution. The validity of the method when dealing with large deformations has been experimentally shown over datasets composed of handwritten shapes. It has been applied to signature verification and shape recognition tasks demonstrating high accuracy and low computational cost.

Abstract: Many video object tracking systems use block matching algorithm (BMA) because of its simple computational structure and robust performance. The BMA, however, exhibits fundamental limitations resulting from non-rigid shapes and similar patterns to the background. The authors propose a combined shape and feature-based non-rigid object tracking algorithm, which is tightly coupled with an adaptive background generation to overcome the limit of block matching. The proposed algorithm is robust to the object's sudden movement or the change of features. This becomes possible by tracking both feature points and their neighbouring regions. Combination of background and shape boundary information significantly improves the tracking performance because the target object and the corresponding feature points on the boundary can be easily found. The shape control points (SCPs) are regularly distributed on the contour of the object, and the authors compare and update the centroid during the tracking process, where straying SCPs are removed, and the tracking continues with only qualified SCPs. As a result, the proposed method becomes free from potential failing factors such as spatio-temporal similarity between object and background, object deformation and occlusion, to name a few. Experiments have been performed using several in-house video sequences including various objects such as a moving robot, swimming fish and walking people. In order to demonstrate the performance of the proposed tracking algorithm, a number of experiments have been performed under noisy and low-contrast environment. For more objective comparison, performance evaluation of tracking surveillance 2002 data sets were also used.

Abstract: The spectral centroid of a signal is the curve whose value at any given time is the centroid of the corresponding constant-time cross section of the signal's spectrogram. A spectral centroid provides a noise-robust estimate of how the dominant frequency of a signal changes over time. As such, spectral centroids are an increasingly popular tool in several signal processing applications, such as speech processing. We provide a new, fast and accurate algorithm for the real-time computation of the spectral centroid of a discrete-time signal. In particular, by exploiting discrete Fourier transforms, we show how one can compute the spectral centroid of a signal without ever needing to explicitly compute the signal's spectrogram. We then apply spectral centroids to an emerging biometrics problem: to determine a person's heart and breath rates by measuring the Doppler shifts their body movements induce in a continuous wave radar signal. We apply our algorithm to real-world radar data, obtaining heart- and breath-rate estimates that compare well against ground truth.

Abstract: In centroid-based classification, each class is represented by a prototype or centroid document vector that is formed by averaging all member vectors during the training phase. In the prediction phase, the label of a test document vector is assigned to that of its nearest class prototype. Recently there has been revived interest in reformulating the prototype/centroid to improve classification performance. In this paper, we study the theoretical properties of the recently proposed Class Feature Centroid (CFC) classifier by considering the rate of change of each prototype vector with respect to individual dimensions (terms). The implication of our theoretical finding is that CFC is inherently biased towards large (dominant majority) classes, which means it is destined to perform poorly for highly class-imbalanced data. Another practical concern about CFC lies in its overly-aggressive design in weeding out terms that appear in all classes. To overcome these CFC limitations while retaining its intrinsic and worthy design goals, we propose an improved and robust centroid-based classifier that uses precise term-class distribution properties instead of simple presence or absence of terms in classes. Specifically, terms are weighted based on the Kullback-Leibler divergence measure between pairs of class-conditional term probabilities, we call this the CFC-KL centroid classifier. We then generalized CFC-KL to handle multi-class data by summing pair wise class-conditioned word probability ratios. Our proposed approach has been evaluated on 5 datasets, on which it consistently outperforms CFC and the baseline Support Vector Machine classifier. We also devise a word cloud visualization approach to highlight the important class-specific words picked out by our CFC-KL, and visually compare it with other popular term weigthing approaches. Our encouraging results show that the centroid based generalized CFC-KL classifier is both robust and efficient to deal with real-world text classification.

Abstract: The present invention provides a Satellite-bone camera space image distortion correction method based on the stellar map matching, the stellar point on the image is used as referecen point, achieving the distortion correction, including following steps: (1)image segmentation, extracting the stellar facula in the image; (2) calculating areal coordinates of each facula by the centroid method, as a real coordinate; (3) processing pre-correction according to the deformation parameter obtained by the ground standardization of the camera; (4) processing stellar map matching by single frame or multiple frames Hausdorff distance stellar map matching method, calculating academic coordinate corresponding to the practical coordinate; (5) calculating deformation parameter of the image by the corresponding relation between the practical coordinate and the academic coordinate; (6) obtaining corrected image by the three steps fitting of a polynomial and interpolation method. In addition, the invention aiming at correctoon impact of the camera plain shaft point system error, provides a adaptive compensation method. The invention has small operation, evident image correcting effect, and effectively preventing the impact of the correction caused by the image noise and the input system error.

Abstract: The equilibrium measure of a compact plane set gives the steady state distribution of charges on the conductor. We show that certain moments of this equilibrium measure, when taken about the electrostatic centroid and depending only on the real coordinate, are extremal for an interval centered at the origin. This has consequences for means of zeros of polynomials, and for means of critical points of Green’s functions. We also study moments depending on the distance from the centroid, such as the electrostatic moment of inertia.

Abstract: Method for reconstructing subsurface Q models (110) from seismic data (10) by performing ray-based (60), centroid frequency shift (50) Q tomography. The seismic source waveform's amplitude spectrum is approximated by a frequency- weighted exponential function of frequency (40), having two parameters to adjust to fit the frequency shift data, thereby providing a better fit to various asymmetric source amplitude spectra. Box constraints may be used in the optimization routine, and a multi-index active-set method used in velocity tomography is a preferred technique for implementing the box constraints (100).

Abstract: JADE is a recent adaptive version of nature inspired algorithm, Differential Evolution (DE). It has shown considerable performance improvement on a set of well studied benchmark functions. This paper studies whether the performance of JADE can be improved further by changing its random population initialization with centroid based population initialization. The new algorithm is known as Centroid-based Initialized JADE (ciJADE). ciJADE not only initialize JADE from centroids of the random parents, it also reflects the worst ten percent of the centroids population. ciJADE is tested on fourteen CEC2005 competition test problems. Experimental results show that ciJADE has significantly improved the performance of JADE on various problems.

Abstract: Currently, kidney stone and tumor removal can be done without surgery. For this purpose, it is required imaging modalities that able to visualize kidney accurately. In order to improve the accuracy of kidney visualization in a short time, an automatic kidney centroid detection is required. This project developed a software to automatically detect the centroid of human kidney. The software was developed using MATLAB with smoothing filter, texture filter and morphological operators. They were used for image segmentation in order to extract important features. Test result shows the software achieve until 96.43% of accuracy in detecting the centroid. The detected centroid can be used as initial point to create ellipse model, which can be used to detect kidney's contour in further research. This software can be implemented in the most US machine that will be used as segmentation tool to reduce human errors and time.

Abstract: The goal of this work is to discriminatively learn contour fragment descriptors for the task of object detection. Unlike previous methods that incorporate learning techniques only for object model generation or for verification after detection, we present a holistic object detection system using solely shape as underlying cue. In the learning phase, we interrelate local shape descriptions (fragments) of the object contour with the corresponding spatial location of the object centroid. We introduce a novel shape fragment descriptor that abstracts spatially connected edge points into a matrix consisting of angular relations between the points. Our proposed descriptor fulfills important properties like distinctiveness, robustness and insensitivity to clutter. During detection, we hypothesize object locations in a generalized Hough voting scheme. The back-projected votes from the fragments allow to approximately delineate the object contour. We evaluate our method e.g. on the well-known ETHZ shape data base, where we achieve an average detection score of 87:5% at 1:0 FPPI only from Hough voting, outperforming the highest scoring Hough voting approaches by almost 8%.

Abstract: One of the fundamental problems in wireless sensor networks (WSNs) is localization that forms the basis for many location aware applications. Localization in WSNs is to determine the physical position of sensor node based on the known positions of several nodes. In this paper, a range free, enhanced weighted centroid localization method using edge weights of adjacent nodes is proposed. In the proposed method, first the adjacent reference (anchor) nodes which are connected to the node to be localized are found, and then the edge weights based on received signal strength indicator information (RSSI) using Mamdani and Sugeno fuzzy inference systems are calculated. After localizing the sensor node by weighted centroid formula using both the Mamdani and Sugeno fuzzy system, a combined approach to localize the node is employed. Finally, the proposed method is simulated to demonstrate the performance by comparing them with the simple centroid, individual Mamdani and Sugeno fuzzy method.