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: In this paper, the authors derived analytic formulae for the aberrations of the tip-tilt corrected wavefront as a function of the tracking algorithm and the tracking frequency.
Abstract: In order to provide criteria for the design of tip-tilt systems on large telescopes, we derive analytic formulae for the aberrations of the tip-tilt corrected wavefront as a function of the tracking algorithm and of the tracking frequency. We present a new definition for the required bandwidth of tip-tilt systems based upon the residual high order aberrations, and we suggest a new, simple method to eliminate the influence of coma on the image centroid measurement. The effect of the science camera pixel size on the image quality is investigated. A simple approximation is given for the power spectrum of the image motion, and measured power spectra are compared to this approximation. We show that in order to measure the rms image motion properly, it is important to run the measurement over a sufficient period of time (>10 min). In a second paper (Glindemann et al. 1997) in this journal, a tip-tilt system (CHARM) is presented where the parameters derived here are used to evaluate the system performance.
20 citations
Journal Article•
TL;DR: This paper presents new fast codebook search algorithm which uses sorting and centroid technique to search the closest codevector in the codebook and uses the mean absolute error as the quality factor.
Abstract: Vector Quantization(VQ) is an efficient technique for data compression and has been successfully used in various applications. In this paper we present new fast codebook search algorithm which uses sorting and centroid technique to search the closest codevector in the codebook. The proposed search algorithm is faster since it reduces number of Euclidean distance computation as compared to Exhaustive search algorithm while keep the image quality imperceptibly close to Exhaustive search algorithm. We have used the mean absolute error as the quality factor since it gives better feel of distortion. Also the proposed algorithm is compared with other codebook search algorithms given in literature and it is found that the performance parameter' average execution time and average number of Euclidean distance computation per image training vector of the proposed algorithm is considerably better compared to most of them.
20 citations
TL;DR: This note describes two operators that respond to step edges, but not to ramps; the first is similar to the digital Laplacian, but uses the max of the x and y second differences, and the second uses the difference between the mean and median gray levels in a neighborhood.
Abstract: This note describes two operators that respond to step edges, but not to ramps. The first is similar to the digital Laplacian, but uses the max, rather than the sum, of the x and y second differences. The second uses the difference between the mean and median gray levels in a neighborhood. The outputs obtained from these operators applied to a set of test pictures are compared with each other and with the standard digital Laplacian and gradient. A third operator, which uses the distance between the center and centroid of a neighborhood as an edge value, is also briefly considered; it turns out to be equivalent to one of the standard digital approximations to the gradient.
20 citations
TL;DR: The present work sheds light on studying the photon-electron interactions at sub-nanoparticle level, and Fourier transform-based demodulation also provides a superior strategy for other fast and reversible processes such as electrochromic and photothermal conversions.
Abstract: While conventional wisdom suggests the scattering centroid of a plasmonic nanoparticle reflects its geometric center, here we uncover the dependence of a scattering centroid of a single gold nanoro...
20 citations
TL;DR: A novel Model Adjustment algorithm, which makes use of training- set errors as well as training-set margins is proposed, and it is proved that for a linearly separable problem, proposed method converges to the optimal solution after finite updates using any learning parameter @h(@h>0).
Abstract: In the community of information retrieval, Centroid Classifier has been showed to be a simple and yet effective method for text categorization. However, it is often plagued with model misfit (or inductive bias) incurred by its assumption. Various methods have been proposed to address this issue, such as Weight Adjustment, Voting, Refinement and DragPushing. However, existing methods employ only one criterion, i.e., training-set error. Researches in machine learning indicate that training-set error based method cannot guarantee the generalization capability of base classifiers for unseen examples. To overcome this problem, we propose a novel Model Adjustment algorithm, which makes use of training-set errors as well as training-set margins. Furthermore, we prove that for a linearly separable problem, proposed method converges to the optimal solution after finite updates using any learning parameter @h(@h>0). The empirical assessment conducted on four benchmark collections indicates that proposed method performs slightly better than SVM classifier in prediction accuracy, as well as beats it in running time.
20 citations