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.

Robust video fingerprinting based on affine covariant regions

Abstract: This paper proposes a robust video fingerprinting method based on affine covariant regions. In video fingerprinting, a video clip is identified using short feature vectors referred to as fingerprints. In the proposed method, local fingerprints based on the centroid of gradient orientations are extracted from affine covariant regions detected in each key-frame. For the region detection, the maximally stable extremal region (MSER) detector which is considered to have high repeatability and low complexity is used. For reliable matching of the local fingerprints, only spatio-temporally consistent matches are taken into account. The experimental results show that the proposed method is robust against both geometric and non-geometric transformations.

Evaluation of Nonlinear Quantum Time Correlation Functions within the Centroid Dynamics Formulation

Abstract: A method to evaluate nonlinear centroid correlation functions is presented that is amenable to simple numerical computation. It can be implemented with the centroid molecular dynamics method for approximate quantum dynamics with no additional assumptions. Two nonlinear correlation functions are evaluated for a model potential using this scheme and compared with results from exact quantum calculations.

Estimation and Statistical Bounds for Three-Dimensional Polar Shapes in Diffuse Optical Tomography

Abstract: Voxel-based reconstructions in diffuse optical tomography (DOT) using a quadratic regularization functional tend to produce very smooth images due to the attenuation of high spatial frequencies. This then causes difficulty in estimating the spatial extent and contrast of anomalous regions such as tumors. Given an assumption that the target image is piecewise constant, we can employ a parametric model to estimate the boundaries and contrast of an inhomogeneity directly. In this paper, we describe a method to directly reconstruct such a shape boundary from diffuse optical measurements. We parameterized the object boundary using a spherical harmonic basis, and derived a method to compute sensitivities of measurements with respect to shape parameters. We introduced a centroid constraint to ensure uniqueness of the combined shape/center parameter estimate, and a projected Newton method was utilized to optimize the object center position and shape parameters simultaneously. Using the shape Jacobian, we also computed the Cramer-Rao lower bound on the theoretical estimator accuracy given a particular measurement configuration, object shape, and level of measurement noise. Knowledge of the shape sensitivity matrix and of the measurement noise variance allows us to visualize the shape uncertainty region in three dimensions, giving a confidence region for our shape estimate. We have implemented our shape reconstruction method, using a finite-difference-based forward model to compute the forward and adjoint fields. Reconstruction results are shown for a number of simulated target shapes, and we investigate the problem of model order selection using realistic levels of measurement noise. Assuming a signal-to-noise ratio in the amplitude measurements of 40 dB and a standard deviation in the phase measurements of 0.1deg, we are able to estimate an object represented with an eighth-order spherical harmonic model having an absorption contrast of 0.15 cm-1 and a volume of 4.82 cm3 with errors of less than 10% in object volume and absorption contrast. We also investigate the robustness of our shape-based reconstruction approach to a violation of the assumption that the medium is purely piecewise constant.

Evaluation of particle swarm optimization based centroid classifier with different distance metrics

Abstract: The nearest centroid classifier (NCC) is based on finding the arithmetic means of the classes from the training instances and unseen-class instances are classified by measuring the distance to these means. It may work well if the classes are well separated which is not the case for many practical datasets. In this paper, particle swarm optimization (PSO) is utilized to find the centroids under an objective function to minimize the error of classification. Three different measures are investigated namely the Euclidean distance, the Mahalanobis distance and a weighted distance to represent the distance function. The performance is tested on eight practical datasets. Simulation results show that the PSO based centroid classifier improves the classification results especially for datasets that the basic NCC does not handle well.