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.

Ensuring the Centroid of an Interval Type-2 Fuzzy Set

Abstract: In computing with words, establishing a fuzzy set (FS) model for a word to capture its uncertainties is an important issue. An interval type-2 (IT2) FS can be used to model a word. How to establish an IT2 FS from the collected data about a word has been a challenging problem. It has been reported that one way is to extract the centroid of an IT2 FS from the collected data and to obtain geometric parameters of its footprint of uncertainty (FOU) such that its centroid matches the extracted one. How to extract the centroid of an IT2 FS from the collected data about a word has thoroughly been studied. However, there exists no method for obtaining FOU parameters for an IT2 FS such that its centroid matches the desired one. To fill this gap, this paper presents an approach to obtaining FOU parameters for an IT2 FS by establishing equations using the centroid requirement. To propose this approach, a sufficient and necessary condition for ensuring the centroid of an IT2 FS is developed. Using this sufficient and necessary condition, two equations about all of the FOU parameters are established. To obtain the FOU parameters, all of them except two are predetermined so that the established equations can be simplified to two single-variable equations. The other two FOU parameters can then be determined by solving these two single-variable equations using existing root-finding algorithms. Among existing root-finding algorithms, the false position algorithm is recommended. The overall merits of the proposed approach are its simplicity in implementation and its applicability to IT2 FSs with arbitrary FOU shapes. In addition, numerical examples are provided to further illustrate how to apply the proposed approach to obtain FOU parameters for an IT2 FS.

A Novel Method for Wheel Rim Recognition

Abstract: The recognition of the wheel rim is a crucial area for vehicle safety. We present a novel recognition method for the detection of wheel rim whose projective curve is an ellipse. An image preprocessing method including the Otsu algorithm to binary the gray image, removing spurious areas and region filling algorithm is introduced to segment the rim area correctly. Then the ellipse rim region is considered as a 2D sheet and the centroid coordinate of the ellipse region is identified by the formula of calculating mass center. At last, all the five parameters to construct the ellipse of wheel rim are obtained according to the geometrical property that the optimum vertex on the semiaxis is the point on the edge of ellipse whose distance between the centroid and the point is maximum or minimum. The experimental results show that the proposed method recognizes the wheel rim efficiently and precisely.

A hybrid localization algorithm based on DV-Distance and the twice-weighted centroid for WSN

Abstract: In recent years, wireless sensor networks (WSN) have a wide application prospects. So it has attracted great interests in several related research fields and industries. Based on the characteristics of DV-Distance and centroid location algorithm, a novel hybrid localization algorithm based on DV-Distance and the twice-weighted centroid in wireless sensor network is proposed. The main principle of the hybrid scheme is using the DV-Distance localization algorithm to get the cumulative distance and the rough-estimated coordinate for calculating twice weighted factors. Twice-weighted centroid computation by weighted factors reflects that different anchor nodes have respective influence degrees in the process of determining the localization coordinate. The experimental results show that the proposed localization algorithm can improve the localization accuracy compared with the DV-Distance and the centroid localization algorithm in same experiment environment. The proposed algorithm can increase location accuracy by 20% under the density of anchor over 20%.

Multivariate angle scale descriptor for shape retrieval

Abstract: Boundary based shape descriptors have been widely used in image retrieval problems. A plethora of contour-based descriptors regarding the shape as a 1-D signal sequences, can be found in literature. Centroid Contour Distance –CCD- as well as Angle Code Histogram (ACH) are well known and extensively used descriptors. In this paper the Angle Scale Descriptor -ASD- is introduced which is based on the angle sequences as they are computed at different scales. It is a multivariate approach where each feature vector consists of angle values produced across by the different scales and includes information from fine to coarser scales. This descriptor provides information for shape boundary which could be efficiently combined with other descriptors. In the present paper, improvement of the Angle Code when using the multi-scale representation will be shown, as well as the enhancement of the overall retrieval process when fusing the ASD with CCD descriptors that provide complementary shape information.