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.

The Riemann Barycenter Computation and Means of Several Matrices

Abstract: An iterative definition of any n variable mean function is given in this article, which iteratively uses the two-variable form of the corresponding two-variable mean function. This extension method omits recursivity which is an important improvement compared with certain recursive formulas given before by Ando-Li-Mathias, PetzTemesi. Furthermore it is conjectured here that this iterative algorithm coincides with the solution of the Riemann centroid minimization problem. Certain simulations are given here to compare the convergence rate of the different algorithms given in the literature. These algorithms will be the gradient and the Newton mehod for the Riemann centroid computation. Keywords—means, matrix means, operator means, geometric mean, Riemannian center of mass

Improved pupil center localization method for eye-gaze tracking-based human-device interaction

Abstract: This paper presents an improved pupil center (PC) localization method for eye-gaze tracking. In the proposed method, an input infrared eye image is repeatedly binarized with a finite number of different thresholds to produce a stack of binary images. Among all the blobs, which are groups of connected binary pixels in the binary image stack, we find a blob whose shape is the most similar to pupil, in terms of the size, the aspect ratio, the moments, and the circularity. Consequently, the centroid of the final resultant blob is regarded as the PC location. Experimental results show that the proposed method outperforms conventional ones.

A Novel Fire Identification Algorithm Based on Improved Color Segmentation and Enhanced Feature Data

Abstract: In order to improve the accuracy of fire identification based on video in the Internet-of-Things environment, this article proposes a new fire identification algorithm by merging fire segmentation and multifeature fusion of fire. First, according to the relationship between R and Y channels, the improved YCbCr models are established for initial fire segmentation under reflection and nonreflection conditions, respectively. Simultaneously, the reflection and nonreflection conditions are judged by comparing the areas obtained by the two improved YCbCr models. Second, an improved region growing algorithm is proposed for fine fire segmentation by making use of the relationship between the seed point and its adjacent points. The seed points are determined using the weighted average of centroid coordinates of each segmented image. Finally, the quantitative indicators of fire identification are given according to the variation coefficient of fire area, the dispersion of centroid, and the circularity. Extensive experiments were conducted, and the experimental results demonstrate that the proposed fire detection method considerably outperforms the traditional methods on average in terms of three performance indexes: precision, recall, and $F1$ -score. Specifically, compared with the deep learning method, the precision of the proposed method is slightly higher. Although the recall of the proposed method is slightly lower than the deep learning method, its computation complexity is low.

Neighborhood centroid opposite-based learning Harris Hawks optimization for training neural networks

Abstract: The Harris Hawks Optimization Algorithm is a new metaheuristic optimization that simulates the process of Harris Hawk hunting prey (rabbit) in nature. The global and local search processes of the algorithm are performed by simulating several stages of cooperative behavior during hunting. To enhance the performance of this algorithm, in this paper we propose a neighborhood centroid opposite-based learning Harris Hawks optimization algorithm (NCOHHO). The mechanism of applying the neighborhood centroid under the premise of using opposite-based learning technology to improve the performance of the algorithm, the neighborhood centroid is used as a reference point for the generation of the opposite particle, while maintaining the diversity of the population and make full use of the swarm search experience to expand the search range of the reverse solution. Enhancing the probability of finding the optimal solution and the improved algorithm is superior to the original Harris Hawks Optimization algorithm in all aspects. We apply NCOHHO to the training of feed-forward neural network (FNN). To confirm that using NCOHHO to train FNN is more effective, five classification datasets are applied to benchmark the performance of the proposed method. Comprehensive comparison and analysis from the three aspects of mean, variance and classification success rate, the experimental results show that the proposed NCOHHO algorithm for optimization FNN has the best comprehensive performance and has more outstanding performance than other metaheuristic algorithms in terms of the performance measures.