Topic
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.
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TL;DR: This paper focuses on clustering analysis using a K-means approach for fatigue feature dataset extraction to group the dataset as closely as possible (homogeneity) for the scattered dataset.
Abstract: This paper focuses on clustering analysis using a K-means approach for fatigue feature dataset extraction. The aim of this study is to group the dataset as closely as possible (homogeneity) for the scattered dataset. Kurtosis, the wavelet-based energy coefficient and fatigue damage are calculated for all segments after the extraction process using wavelet transform. Kurtosis, the wavelet-based energy coefficient and fatigue damage are used as input data for the K-means clustering approach. K-means clustering calculates the average distance of each group from the centroid and gives the objective function values. Based on the results, maximum values of the objective function can be seen in the two centroid clusters, with a value of 11.58. The minimum objective function value is found at 8.06 for five centroid clusters. It can be seen that the objective function with the lowest value for the number of clusters is equal to five; which is therefore the best cluster for the dataset.
14 citations
TL;DR: In this paper, the centroid of a prime ring is characterized by the action on a fixed polynomial in noncommuting variables on algebras satisfying certain d-freeness conditions.
Abstract: We characterize certain maps by their action on a fixed polynomial in noncommuting variables on algebras satisfying certain d -freeness condition. Consequently, a characterization of the centroid of a prime ring is obtained.
14 citations
Patent•
05 Dec 2001
TL;DR: In this article, a new neural model for direct classification, DC, is introduced for acoustic/pictorial data compression, which is based on the Adaptive Resonance Theorem and Kohonen Self Organizing Feature Map neural models.
Abstract: A new neural model for direct classification, DC, is introduced for acoustic/pictorial data compression. It is based on the Adaptive Resonance Theorem and Kohonen Self Organizing Feature Map neural models. In the adaptive training of the DC model, an input data file is vectorized into a domain of same size vector subunits. The result of the training (step 10 to 34 ) is to cluster the input vector domain into classes of similar subunits, and develop a center of mass called a centroid for each class to be stored in a codebook (CB) table. In the compression process, which is parallel to the training (step 33 ), for each input subunit, we obtain the index of the closest centroid in the CB. All indices and the CB will form the compressed file, CF. In the decompression phase (steps 42 to 52 ), for each index in the CF, a lookup process is performed into the CB to obtain the centroid representative of the original subunit. The obtained centroid is placed in the decompressed file. The compression is realized because the size of the input subunit ((8 or 24)*n 2 bits) is an order of magnitude larger than its encoding index log 2 [size of CB] bits. In order to achieve a better compression ratio, LZW is performed on CF (step 38 ) before storing (or transmitting) it.
14 citations
28 Aug 2013
TL;DR: An iterative method is presented which quickly provides a centroid of the population in shape space which can be used as a rough template estimate or as initialization for template estimation methods.
Abstract: Computing a template in the Large Deformation Diffeomorphic Metric Mapping framework is a key step for the shape analysis of anatomical structures, but can lead to very computationally expensive algorithms in the case of large databases. We present an iterative method which quickly provides a centroid of the population in shape space. This centroid can be used as a rough template estimate or as initialization for template estimation methods.
14 citations
TL;DR: The idea of shape matching is applied to track edge features automatically in a pair of AVHRR IR satellite images through the correspondence of two shape-specific points of the edges in two sequential images, and the whole edge's properties of rotation, translation, and scaling could be obtained.
Abstract: The idea of shape matching is applied to track edge features automatically in a pair of AVHRR IR satellite images. The centroid and radius weighted mean are chosen as shape-specific points of the edges in two sequential images. Through the correspondence of these two shape-specific points, the whole edge's properties of rotation, translation, and scaling could be obtained. Also, a better correspondence in the second image is chosen according to the similarity comparison. After that, the total velocities of the points in the pattern can be computed. Velocity components in normal and tangential directions for certain points on the edge are also computed through a simple trial-and-error procedure and vector decomposition. >
14 citations