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.
Papers published on a yearly basis
Papers
More filters
01 Jan 2010
TL;DR: In this paper, the authors presented both an internal and external accuracy assessment of four different methods for measuring the centroid of a signalized planar target captured by a terrestrial laser scanner.
Abstract: SUMMARY This paper presents both an internal and external accuracy assessment of four different methods for measuring the centroid of a signalized planar target captured by a terrestrial laser scanner. The planar targets used in this project are composed of a black background and a white circle printed on 8½ by 11 inches plain sheet of paper using a consumer level LaserJet printer. The first two methods tested define the centroid of a target to be the mean and median of the cluster of points belonging to the white circle in the point cloud. The latter two methods are more advanced, and they take advantage of the planar nature of the target as well as the intensity difference between the circle and the background to strengthen the centroid derivation through a combination of least-squares plane fitting and circle fitting. The main benefit of the four presented methodologies is that no specialized and/or laser scanner dependent targets need to be utilized. And it will be demonstrated in this paper that using the two advanced methods can yield position measurement precision and accuracy far superior to the simple mean or median computations. In fact, sub-millimetre precision and accuracy is achievable from using low cost paper targets provided that an appropriate target measurement algorithm like the latter two methodologies proposed in this paper is adopted.
22 citations
26 Jul 2009
TL;DR: A novel shape descriptor based on the histogram matrix of pixel-level structural features that can measure circularity, smoothness, and symmetry of shapes, and be used to recognize shapes.
Abstract: A novel shape descriptor based on the histogram matrix of pixel-level structural features is presented. First, length ratios and angles between the centroid and contour points of a shape are calculated as two structural attributes. Then, the attributes are combined to construct a new histogram matrix in the feature space statistically. The proposed shape descriptor can measure circularity, smoothness, and symmetry of shapes, and be used to recognize shapes. Experimental results demonstrate the effectiveness of our method.
22 citations
TL;DR: A novel statistical outlier detection method is designed to identify cluster centroids automatically from the decision graph, so that the number of clusters is also automatically determined.
22 citations
05 Jan 2004
TL;DR: A node centroid method with Hill-Climbing to solve the well-known matrix bandwidth minimization problem, which is to permute rows and columns of the matrix to minimize its bandwidth while being much faster than the newly-developed algorithms.
Abstract: We propose a node centroid method with Hill-Climbing to solve the well-known matrix bandwidth minimization problem, which is to permute rows and columns of the matrix to minimize its bandwidth. Many heuristics have been developed for this NP-complete problem including the Cuthill-McKee (CM) and the Gibbs, Poole and Stockmeyer (GPS) algorithms. Heuristics such as simulated annealing, tabu search and GRASP have been used, where tabu search and the GRASP with path relinking have achieved significantly better solution quality than the CM and GPS algorithms. Experimentation shows that the node centroid method achieves the best solution quality when compared with these while being much faster than the newly-developed algorithms. Also, the new algorithm achieves better solutions than the GPS algorithm in comparable time.
22 citations
TL;DR: A statistical model is developed that integrates the principle of shape analysis into a mixture-model-based likelihood formulated for QTL mapping, allowing specific QTLs for global and local shape variability to be mapped, respectively.
Abstract: As the consequence of complex interactions between different parts of an organ, shape can be used as a predictor of structural–functional relationships implicated in changing environments. Despite such importance, however, it is no surprise that little is known about the genetic detail involved in shape variation, because no approach is currently available for mapping quantitative trait loci (QTLs) that control shape. Here, we address this problem by developing a statistical model that integrates the principle of shape analysis into a mixture-model-based likelihood formulated for QTL mapping. One state-of-the-art approach for shape analysis is to identify and analyze the polar coordinates of anatomical landmarks on a shape measured in terms of radii from the centroid to the contour at regular intervals. A procrustes analysis is used to align shapes to filter out position, scale and rotation effects on shape variation. To the end, the accurate and quantitative representation of a shape is produced with aligned radius-centroid-contour (RCC) curves, that is, a function of radial angle at the centroid. The high dimensionality of the RCC data, crucial for a comprehensive description of the geometric feature of a shape, is reduced by principal component (PC) analysis, and the resulting PC axes are treated as phenotypic traits, allowing specific QTLs for global and local shape variability to be mapped, respectively. The usefulness and utilization of the new model for shape mapping in practice are validated by analyzing a mapping data collected from a natural population of poplar, Populus szechuanica var tibetica, and identifying several QTLs for leaf shape in this species. The model provides a powerful tool to compute which genes determine biological shape in plants, animals and humans.
22 citations