Centroid

About: Centroid is a research topic. Over the lifetime, 4110 publications have been published within this topic receiving 53637 citations. The topic is also known as: barycenter (geometry) & geometric center of a plane figure.

A Proposal to Speed up the Computation of the Centroid of an Interval Type-2 Fuzzy Set

Abstract: This paper presents two new algorithms that speed up the centroid computation of an interval type-2 fuzzy set. The algorithms include precomputation of the main operations and initialization based on the concept of uncertainty bounds. Simulations over different kinds of footprints of uncertainty reveal that the new algorithms achieve computation time reductions with respect to the Enhanced-Karnik algorithm, ranging from 40 to 70%. The results suggest that the initialization used in the new algorithms effectively reduces the number of iterations to compute the extreme points of the interval centroid while precomputation reduces the computational cost of each iteration.

Times Series Averaging and Denoising from a Probabilistic Perspective on Time–Elastic Kernels

Abstract: In the light of regularized dynamic time warping kernels, this paper re-considers the concept of time elastic centroid for a set of time series. We derive a new algorithm based on a probabilistic interpretation of kernel alignment matrices. This algorithm expresses the averaging process in terms of a stochastic alignment automata. It uses an iterative agglomerative heuristic method for averaging the aligned samples, while also averaging the times of occurrence of the aligned samples. By comparing classification accuracies for 45 heterogeneous time series datasets obtained by first nearest centroid/medoid classifiers we show that: i) centroid-based approaches significantly outperform medoid-based approaches, ii) for the considered datasets, our algorithm that combines averaging in the sample space and along the time axes, emerges as the most significantly robust model for time-elastic averaging with a promising noise reduction capability. We also demonstrate its benefit in an isolated gesture recognition experiment and its ability to significantly reduce the size of training instance sets. Finally we highlight its denoising capability using demonstrative synthetic data: we show that it is possible to retrieve, from few noisy instances, a signal whose components are scattered in a wide spectral band.

Improved centroid extraction algorithm for autonomous star sensor

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.

Centroid tracker and aimpoint selection

Abstract: Autonomous fire and forget weapons have gained importance to achieve accurate first pass kill by hitting the target at an appropriate aim point. Centroid of the image presented by a target in the field of view (FOV) of a sensor is generally accepted as the aimpoint for these weapons. Centroid trackers are applicable only when the target image is of significant size in the FOV of the sensor but does not overflow the FOV. But as the range between the sensor and the target decreases the image of the target will grow and finally overflow the FOV at close ranges and the centroid point on the target will keep on changing which is not desirable. And also centroid need not be the most desired/vulnerable point on the target. For hardened targets like tanks, proper aimpoint selection and guidance up to almost zero range is essential to achieve maximum kill probability. This paper presents a centroid tracker realization. As centroid offers a stable tracking point, it can be used as a reference to select the proper aimpoint. The centroid and the desired aimpoint are simultaneously tracked to avoid jamming by flares and also to take care of the problems arising due to image overflow. Thresholding of gray level image to binary image is a crucial step in centroid tracker. Different thresholding algorithms are discussed and a suitable algorithm is chosen. The real-time hardware implementation of centroid tracker with a suitable thresholding technique is presented including the interfacing to a multimode tracker for autonomous target tracking and aimpoint selection. The hardware uses very high speed arithmetic and programmable logic devices to meet the speed requirement and a microprocessor based subsystem for the system control. The tracker has been evaluated in a field environment.

Study of range free centroid based localization algorithm and its improvement using particle swarm optimization for wireless sensor networks under log normal shadowing

Abstract: Localization deals with the determination of coordinates of unknown nodes for proper routing of data in wireless sensor networks. The centroid based localization algorithm (CLA) has been explored to a great extent till date. Its basic and improved form suffers from large localization error. In the present work the basic range free centroid based localization algorithm is studied under log normal shadowing and evaluated in terms of localization error. Further the basic CLA is improved by using the particle swarm optimization (PSO) under the same environment. The localization error for basic and PSO based CLA is calculated by varying the anchor ratio, communication range, number of unknown nodes, and network area. In every condition the PSO based CLA performs well over basic CLA in terms of reduction in localization error. The localization error in the proposed work is smallest as compared to other known centroid based localization algorithms.