Abstract: Aiming at the problemsof too much iterative times in selecting initial centroids stochastically for K-Means algorithm,a method is proposed to optimize the initial centroids through cutting the set into k segmentations and select one point in each segmentation as initial centroids for iterative computing. A new valid function called clustering-index is defined as the sum of clustering-density and clustering-significance and can be used to search the optimization of k in the internal of [1,n(1/2) ]. The simulation experiment with IRIS data set shows that the proposed algorithm converges faster and the value k found is close to the actual value,which proves the validity of the algorithm.

Abstract: We present a weak-lensing study of the cluster A520 based on Advanced Camera for Surveys (ACS) data. The excellent data quality provides a mean source density of ~109 arcmin–2, which improves both resolution and significance of the mass reconstruction compared to a previous study based on Wide Field Planetary Camera 2 (WFPC2) images. We take care in removing instrumental effects such as the charge trailing due to radiation damage of the detector and the position-dependent point-spread function. This new ACS analysis confirms the previous claims that a substantial amount of dark mass is present between two luminous subclusters where we observe very little light. The centroid of the dark peak in the current ACS analysis is offset to the southwest by ~1' with respect to the centroid from the WFPC2 analysis. Interestingly, this new centroid is in better agreement with the location where the X-ray emission is strongest, and the mass-to-light ratio estimated with this centroid is much higher (813 ± 78 M ☉/L R☉) than the previous value; the aperture mass with the WFPC2 centroid provides a consistent mass. Although we cannot provide a definite explanation for the dark peak, we discuss a revised scenario, wherein dark matter with a more conventional range (σDM/m DM < 1 cm2 g–1) of self-interacting cross-section can lead to the detection of this dark substructure. If supported by detailed numerical simulations, this hypothesis opens up the possibility that the A520 system can be used to establish a lower limit of the self-interacting cross-section of dark matter.

Abstract: This paper presents a biometric technique for identification of a person using the iris image. The iris is first segmented from the acquired image of an eye using an edge detection algorithm. The disk shaped area of the iris is transformed into a rectangular form. Described moments are extracted from the grayscale image which yields a feature vector containing scale, rotation, and translation invariant moments. Images are clustered using the k-means algorithm and centroids for each cluster are computed. An arbitrary image is assumed to belong to the cluster whose centroid is the nearest to the feature vector in terms of Euclidean distance computed. The described model exhibits an accuracy of 98.5%.

Abstract: The prevalent approach to image-based localization is matching interest points detected in the query image to a sparse 3D point cloud representing the known world The obtained correspondences are then used to recover a precise camera pose The state-of-the-art in this field often ignores the availability of a set of 2D descriptors per 3D point, for example by representing each 3D point by only its centroid In this paper we demonstrate that these sets contain useful information that can be exploited by formulating matching as a discriminative classification problem Since memory demands and computational complexity are crucial in such a setup, we base our algorithm on the efficient and effective random fern principle We propose an extension which projects features to fern-specific embedding spaces, which yields improved matching rates in short runtime Experiments first show that our novel formulation provides improved matching performance in comparison to the standard nearest neighbor approach and that we outperform related randomization methods in our localization scenario

Abstract: The Student's-t mixture model, which is heavily tailed and more robust than the Gaussian mixture model, has recently received great attention on image processing. In this paper, we propose a robust non-rigid point set registration algorithm using the Student's-t mixture model. Specifically, first, we consider the alignment of two point sets as a probability density estimation problem and treat one point set as Student's-t mixture model centroids. Then, we fit the Student's-t mixture model centroids to the other point set which is treated as data. Finally, we get the closed-form solutions of registration parameters, leading to a computationally efficient registration algorithm. The proposed algorithm is especially effective for addressing the non-rigid point set registration problem when significant amounts of noise and outliers are present. Moreover, less registration parameters have to be set manually for our algorithm compared to the popular coherent points drift (CPD) algorithm. We have compared our algorithm with other state-of-the-art registration algorithms on both 2D and 3D data with noise and outliers, where our non-rigid registration algorithm showed accurate results and outperformed the other algorithms.

Abstract: In this paper, we propose a blind modulation classifier that differs from most existing classifiers. A low complexity minimum distance centroid estimator is suggested to estimate the channel gain and carrier phase jointly. The estimation is achieved by minimizing a signal-to-centroid distance. A new non-parametric likelihood function is proposed for fast classification with unknown noise variance and distribution. Numerical results show that the estimator provides reliable estimation of signal centroids, enabling an accurate classification with a non-parametric likelihood function. When different channel conditions are simulated, the proposed blind classifier achieves similar classification accuracy versus non-blind state-of-the-art classifiers while being more robust and having much lower complexity.

Abstract: The objective of the work was to develop a non-invasive methodology for image acquisition, processing and nonlinear trajectory analysis of the collective fish response to a stochastic event. Object detection and motion estimation were performed by an optical flow algorithm in order to detect moving fish and simultaneously eliminate background, noise and artifacts. The Entropy and the Fractal Dimension (FD) of the trajectory followed by the centroids of the groups of fish were calculated using Shannon and permutation Entropy and the Katz, Higuchi and Katz-Castiglioni’s FD algorithms respectively. The methodology was tested on three case groups of European sea bass ( Dicentrarchus labrax ), two of which were similar (C1 control and C2 tagged fish) and very different from the third (C3, tagged fish submerged in methylmercury contaminated water). The results indicate that Shannon entropy and Katz-Castiglioni were the most sensitive algorithms and proved to be promising tools for the non-invasive identification and quantification of differences in fish responses. In conclusion, we believe that this methodology has the potential to be embedded in

Abstract: In this paper, we use centroid distance and axis of least inertia method for plant leaf classification. For this propose the RGB (Red, Green, Blue) image are converted to the binary image. Then, Canny operator is applied to the binary image to recognize the edges of the image before thinning the edges. After that, the boundary of the image is traced to sample the shape. Sampling helps us to avoid time-consuming computations. We compute the centroid distance of these points and distance of sampling points from axis of least inertia line. By selecting a fixed start point and normalizing the distances, the proposed method is shown to be invariant to image transformations (translation, rotation, reflection and scaling) and robust to minor deformations and occlusions. In this study, probabilistic neural network (PNN) has been used as a classifier. Two public leaf datasets including: Swedish leaf dataset and Flavia dataset are evaluated. Experimental results demonstrate the superior performance of the proposed feature in plant leaf classification.

Abstract: In this paper, a flexible probabilistic method is introduced for non-rigid point registration, which is motivated by the pioneering research named Coherent Point Drift (CPD). Being different from CPD, our algorithm is robust and outlier-adaptive, which does not need prior information about data such as the appropriate outlier ratio when the point sets are perturbed by outliers. We consider the registration as the alignment of the data (one point set) to a set of Gaussian Mixture Model centroids (the other point set), and initially formulate it as maximizing the likelihood problem, then the problem is solved under Expectation---Maximization (EM) framework. The outlier ratio is also formulated in EM framework and will be updated during the EM iteration. Moreover, we use the volume of the point set region to determine the uniform distribution for modeling the outliers. The resulting registration algorithm exhibits inherent statistical robustness and has an explicit interpretation. The experiments demonstrate that our algorithm outperforms the state-of-the-art method.

Abstract: The centroid method for the correction of turbulence consists in computing the Karcher-Fr echet mean of the sequence of input images. The direction of deformation between a pair of images is determined by the optical ow. A distinguishing feature of the centroid method is that it can produce useful results from an arbitrarily small set of input images. Source Code The source code and a online demo are accessible at the IPOL web page of this article 1 .

Abstract: In cross-domain text classification, topic labels for documents of a target domain are predicted by leveraging knowledge of labeled documents of a source domain, having equal or similar topics with possibly different words. Existing methods either adapt documents of the source domain to the target or represent both domains in a common space. These methods are mostly based on advanced statistical techniques and often require tuning of parameters in order to obtain optimal performances. We propose a more straightforward approach based on nearest centroid classification: profiles of topic categories are extracted from the source domain and are then adapted by iterative refining steps using most similar documents in the target domain. Experiments on common benchmark datasets show that this approach, despite its simplicity, obtains accuracy measures better or comparable to other methods, obtained with fixed empirical values for its few parameters.

Abstract: Location technology is becoming more and more important in wireless sensor networks. The weighted centroid localization offers a fast and simple algorithm for the location equipment in wireless sensor networks. The algorithm derives from the centroid measurement and calculation device of the adjacent anchor in the average coordinate. After the analysis of the radio propagation loss model, the most appropriate log-distance distribution model is selected to simulate the signal propagation. Based on the centroid algorithm and the weighted centroid algorithm, this paper proposes an ellipse centroid localization algorithm. This algorithm makes use of ellipse’s characteristic to estimate the unknown node’s coordinate. The main idea of ellipse centroid localization algorithm is the precision control factor that can control the algorithm’s location precision. In ellipse centroid localization algorithm, node is extended as anchor in order to strengthen anchor density’s dynamic characteristic. The simulation result shows the ellipse centroid localization algorithm is more effective than the centroid algorithm and the weighted centroid precision algorithm

Abstract: This work investigates the position centroid rendezvous/formation problems of multiple unicycle agents for the first time. By constructing a new output, the unicycle model is converted to canonical normal form via feedback linearisation approach. Then, we propose the centroid rendezvous and the centroid formation control laws, respectively, guaranteeing that all the agents globally meet at the common initial centroid location and the desired geometric pattern centred at the initial centroid. The proposed control laws are distributed, smooth and time-varying, ensuring the internal orientation states and the velocity inputs convergent to some fixed values and zero, respectively. All the results are proved under the communication scenarios of fixed directed balanced graph with a spanning tree. Simulation results verify the effectiveness and the robustness of the proposed control schemes.

Abstract: We present a very simple method for measuring the spatial coherence of quasi-monochromatic fields through the comparison of two measurements of the radiant intensity with and without a small obscuration at the test plane. From these measurements one can measure simultaneously the field’s coherence at all pairs of points whose centroid is the centroid of the obstacle. This method can be implemented without the need of any refractive or diffractive focusing elements.

Abstract: New, intelligent systems have been developed recently to improve the quality of match analysis These systems analyze the tactical behavior of the teams However, the existing methods leave room for improvement Thus, the main goal of this study is to refine the team centroid metric by considering all of the players on the team and the ball position Furthermore, this study analyzes the relation-ship between the centroids of the two opposing teams One 11-on-11 soccer match was analyzed to test the new centroid algorithm The results provided strong evidence of the positive relation between the centroids of the two teams over time in the x-axis (rs= 0781) and the x-axis (rs= 0707) This study confirmed the results of previous studies that analyzed the relationship between team centroids Furthermore, it was possible to prove the effectiveness of the new tactical metric and its relevance for adding information during a match

Abstract: The invention relates to a method for controlling array light beam co-target collimation of a target in a loop based on light spot centroid calculation. The coordinates of the centroid of a light spot on an imaging plane are obtained through a light spot centroid algorithm, a tilt control quantity which needs to be conducted on each light beam is obtained according to the quantitative relation between tilt control of a tilting mirror of the corresponding light beam and a light spot movement vector, and the centroid of the light spot is moved to a target spot of an image device through control over the tilting mirrors. Co-target collimation of all the light beams of the target in the loop is realized through sequential control cover the array light beams. There is no need to accurately describe or obtain a light path, and a control scheme is simple and convenient and easy to implement. The adopted centroid of the light spot serves as a performance evaluation function which is simple, convenient, high in real-time performance, and remarkable in effect, and consumes a short time. The method for controlling array light beam co-target collimation of the target in the loop based on light spot centroid calculation has extensive application prospects in satellite tracking, the directed energy technology and other fields.

Abstract: Centroids are practically important in type-1 and type-2 fuzzy logic systems as a method of defuzzification and type reduction. However, computational problems arise when membership functions (MF) have singleton spikes. The novel thresholding aggregation operators that were described in our companion paper “ New Classes of Threshold Aggregation Functions Based Upon the Tsallis q-Exponential with Applications to Perceptual Computing” produce such MFs with spikes. Such spikes may occur when modeling concepts defined on a real-valued domain, and they are also formed in unions of fuzzy sets in which some have MFs with discrete support and others have support defined on an interval. This paper presents a modified definition of the centroid of a fuzzy set that avoids the computational problems associated with the usual definition and reduces to this definition when MFs are continuous and normal (i.e., of unit height) on some interval. We also present an enhanced Karnik–Mendel-type algorithm to compute the modified centroid of interval type-2 fuzzy sets whose MFs have spikes.

Abstract: The centroid of three most closely spaced intersections of constant-range loci is conventionally used as trilateration estimate without rigorous justification. In this paper, we address the quality of trilateration intersections through range scaling factors. Several triangle centres, including centroid, incentre, Lemoine point, and Fermat point, are discussed in detail. Lemoine point (LP) is proposed as the best trilateration estimator thanks to the desired property that the total distance to three triangle edges is minimized. It is demonstrated through simulation that LP outperforms centroid localization without additional computational load. In addition, severe trilateration scenarios such as two-intersection cases are considered in this paper, and enhanced trilateration algorithms are proposed.

Abstract: We present a robust, dense, and accurate Shack-Hartmann spot dislocation map determination method based on a regularized optical flow algorithm that does not require obtaining the spot centroids. The method is capable to measure in presence of strong noise, background illumination and spot modulating signals, which are typical limiting factors of traditional centroid detection algorithms. Moreover, the proposed approach is able to face cases where some of the reference beam spots have not a corresponding one in the distorted Hartmann diagram, and it can expand the dynamic range of the Shack-Hartmann sensor unwrapping the obtained dense dislocation maps. We have tested the algorithm with both simulations and experimental data obtaining satisfactory results. A complete MATLAB package that can reproduce all the results can be downloaded from [http://goo.gl/XbZVOr].

Abstract: Now a day’s Signature verification is one of the most important features for checking the authenticity of a person. There are many security checking parameters like pin code, password, finger print checking but signature recognition is the most popular because it is quite accurate and cost efficient too. On the other hand one doesn’t have to remember the authentication key like pin code or password. The signature of a genuine signer stays almost constant. But there may be little difference between well practiced forgeries and the genuine signer. It is required to distinguish these differences. This paper presents grid based, contour based and area based approach for signature verification. Intersecting points and centroids of two equal half of the signature is being calculated and then those centroids are connected with a straight line and the angles of these intersecting points with respect to the centroids connecting lines are calculated. General Terms Signature Verification, Grid based approach, Centroid based approach and contour based approach.

Abstract: In this paper, we proposed a new ranking method for intuitionistic fuzzy numbers (IFNs) by using centroid and circumcenter of membership function and non-membership function of the intuitionistic fuzzy number. The method utilizes the midpoint of the circumcenter of membership and non-membership function of intuitionistic fuzzy number to define the ranking function for IFN satisfying the general axioms of ranking functions. The developed method has been illustrated by some examples and is compared with some existing ranking method to show its suitability.

Abstract: Object motion tracking has been done for videos with various methodologies The existing systems are mainly based on single feature and motion detection as a single object by taking the movement of the feature selected We propose a moving object tracking method that uses low-level features like centroid location of tracked object combined with the color feature Centroid location of each moving object in the current frame is computed using the greedy technique based on previous locations of the object in earlier frame Experimental result shows that the proposed tracking approach detects and tracks object moving in the scene efficiently and also can handle occlusion

Abstract: Due to the dynamic interaction, the motion of the arms alters the attitude and position of the base To stabilize the centroid position of the base during on-orbital manipulation, we proposed the modeling concept of the "Base Centroid Virtual Manipulator (BCVM)" for free-floating multi-arm space robotic systems Correspondingly, the trajectory planning method of the balance arm was addressed The movement direction and the position of each joint of the BCVM are the same as those of the real space manipulator (SM) The end-effector's position of the BCVM denotes the equivalent centroid of the corresponding SM By resolving the position-level kinematic equations, the singularity-free trajectory of the balance arm was then planned Based on the BCVM model, the stabilization ability of a given balance arm can be easily determined by analyzing the workspace of the BCVM Furthermore, the configuration and the mass properties of the balance arm can be optimized Simulation results of joint trajectory tracking task verified the proposed model and method

Abstract: The invention provides a multi-target tracking method integrating obvious characteristics and block division templates. A target motion area is detected by adoption of RGB component background difference and an iterative threshold, and the adaptive ability of a motion detection algorithm to scene illumination change is improved. Based on target area block division, a motion pixel color saliency weighted block centroid model, block centroid shifting fusion and a scale updating method, the calculation efficiency is high, the resistance to partial occlusion is high, and the similar color scene jamming ability is strong. The problem of multi-target measuring-tracking distribution is solved by adoption of two-level data association, and an occluded local area can be accurately positioned. Therefore, adaptive template updating is guided by an occlusion matrix, a reliable global centroid transfer vector is obtained by making use of effective colors and motion information of blocks, and finally, continuous, stable and fast multi-target tracking in complex scenes is realized. The multi-target tracking method integrating obvious characteristics and block division templates is applied to fields like intelligent video surveillance, in-air multi-target tracking and attacking, and multi-task tracking intelligent robots.

Abstract: This study presents a method for detecting an object by using several features Features are extracted based on the statistical distribution of points on the bitmap image of the shape It detect the shape of an image by using geometrical features and then select the clustered of image that matched that shape from a large database In this approach the image of object are divided into zones and find out some feature of each zone The extracted features are used for classification based on zones To make a decision for classification the feature of zone are extracted as per the boundary structure segment-the sum of ON or foreground pixels in each boundary segment, an angle between the starting and ending points, a distance between the starting and ending points, centroid of each zone, an angle between the starting and centroid points, a distance between the starting and centroid points, an angle between the centroid and ending points, a distance between the centroid and ending points etc