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.

Centroid, Leaf-centroid, and Internal-centroid

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.

A combined Mamdani-Sugeno fuzzy approach for localization in wireless sensor networks

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.

A Cooperative Learning-Based Clustering Approach to Lip Segmentation Without Knowing Segment Number

Abstract: It is usually hard to predetermine the true number of segments in lip segmentation. This paper, therefore, presents a clustering-based approach to lip segmentation without knowing the true segment number. The objective function in the proposed approach is a variant of the partition entropy (PE) and features that the coincident cluster centroids in pattern space can be equivalently substituted by one centroid with the function value unchanged. It is shown that the minimum of the proposed objective function can be reached provided that: 1) the number of positions occupied by cluster centroids in pattern space is equal to the true number of clusters and 2) these positions are coincident with the optimal cluster centroids obtained under PE criterion. In implementation, we first randomly initialize the clusters provided that the number of clusters is greater than or equal to the ground truth. Then, an iterative algorithm is utilized to minimize the proposed objective function. For each iterative step, not only is the winner, i.e., the centroid with the maximum membership degree, updated to adapt to the corresponding input data, but also the other centroids are adjusted with a specific cooperation strength, so that they are each close to the winner. Subsequently, the initial overpartition will be gradually faded out with the redundant centroids superposed over the convergence of the algorithm. Based upon the proposed algorithm, we present a lip segmentation scheme. Empirical studies have shown its efficacy in comparison with the existing methods.

RSSI-based Triangle and Centroid Location in Wireless Sensor Network

Abstract: Node location is one of the key technologies in wireless sensor network.RSSI-based location is a hotspot in nowadays.For resolving biggish error in RSSI-based location,the paper presents a new method of location,RSSI-based triangle and centroid location,using triangle and centroid method to reduce the error of RSSI measurement.Simulation experiments prove that this algorithm can obviously improve the location accuracy compared to trilateration.