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.

Shack-Hartmann centroid detection using the spiral phase transform.

Abstract: We present a Shack–Hartmann (SH) centroid detection algorithm capable to measure in presence of strong noise, background illumination and spot modulating signals, which are typical limiting factors of traditional centroid detection algorithms. The proposed method is based on performing a normalization of the SH pattern using the spiral phase transform method and Fourier filtering. The spot centroids are then obtained using global thresholding and weighted average methods. We have tested the algorithm with simulations and experimental data obtaining satisfactory results. A complete MATLAB package that can reproduce all the results can be downloaded from [http://goo.gl/o2JhD].

Situation-aware thresholding for recommendation

Abstract: In one embodiment, data relating to usage patterns of the user is stored, wherein the data includes information as to items which were used and the context in which they were used. The data is then clustered into clusters of data points. Then a centroid is determined for each of the clusters. A cluster similar to a current context of the user is selected by comparing a data point representing the current context of the user to one or more of the centroids. For each of one or more items, a threshold based on values for a plurality of the centroids with respect to the corresponding item, wherein a threshold is used to compare with centroid value of an item in a selected cluster to determine whether to recommend the item.

Pointing accuracy improvement using model-based noise reduction method

Abstract: A new method for improving centroid accuracy, thereby pointing accuracy, is proposed. Accurate centroid estimation is critical for free-space optical communications where the number of photons from the reference optical sources such as stars or an uplink beacon is limited. It is known that the centroid accuracy is proportional to the SNR. Presence of various noise sources during the exposure of CCD can lead to significant degradation of the centroid estimation. The noise sources include CCD read noise, background light, stray light, and CCD processing electronics. One of the most widely used methods to reduce the effects of the noise and background bias is the thresholding method, which subtracts a fixed threshold from the centroid window before centroid computation. The approach presented here, instead, utilizes the spot model to derive the signal boundary that is used to truncate the noise outside the signal boundary. This process effectively reduces both the bias and the noise. The effectiveness of the proposed method is demonstrated through simulations.

Accurate Sensing of Power Transformer Faults From Dissolved Gas Data Using Random Forest Classifier Aided by Data Clustering Method

Abstract: In this paper, a novel approach for accurate sensing of incipient faults occurring in power transformers is proposed using dissolved gas analysis (DGA) technique. The Duval pentagon method is a popular technique often used to interpret faults occurring in a power transformer based on DGA data. However, one potential limitation of conventional Duval pentagon method is the presence of rigid fault boundaries within the pentagon which often lead to misinterpretations, leading to poor detection accuracy. Considering this issue, in this paper a modification of Duval pentagon method is proposed, where instead of using rigidly separated distinct fault zones, a density-based clustering (DBSCAN) approach is used to increase the resiliency and the accuracy of fault detection technique. At first, DBSCAN is used to form different fault clusters within the Duval pentagon. Following this, the centroid corresponding to each fault cluster within the Duval pentagon is determined. For accurate sensing of incipient transformer faults Euclidean distances between the respective centroids and the fault points of the input DGA data are proposed as new distinguishing features in this work. The proposed distance parameters combined with the relative gas concentration measures are finally served as input features to the random forest (RF) classifier, which returned very high classification accuracy. The performance of the RF classifier is also compared with three benchmark machine classifiers, all of which delivered acceptable results. The proposed method can be used for sensing of power transformer faults using Duval pentagon method with increased accuracy.

Object recognition by alignment using invariant projections of planar surfaces

Abstract: This paper presents an efficient and robust alignment algorithm for recognizing 3D objects. We first show that for features from planar surfaces which undergo linear transformations in space, there exists a class of transformations that yield projections invariant to the surface motions, up to rotations in the image field. To use this property in recognition, we propose a new alignment approach based on centroid alignment of corresponding feature groups built on these invariant projections of planar surfaces. This method uses only a single pair of 2D model and data pictures for recognizing a 3D object. Some results on natural pictures are given.