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.

An image sensor with fast objects' position extraction function

Abstract: This paper describes a kind of vision chip, which is an integration of an image processing circuit with photoreceptors, that has a function of extracting objects' positions in focal plane. The objects' positions are output as their coordinates, which are useful for further detailed image recognition processing. The extraction processing has two steps; first, the flags indicating the objects' center positions are generated by analog parallel processing circuit implemented by resistive network and comparators, and next, the coordinates of such flags are generated by x- and y-priority encoders and a novel successive masking circuit. The designed circuit is capable of centroid detection for 23 /spl times/ 23 pixels within 50 /spl mu/s, and the processing time is expected not to increase so much even if the number of pixels increases, which will represent an improvement over the conventional processor-based image processing system. We also proposed and evaluated another implementation of a centroid detector using pulse-width adder.

A method to determine the detector locations of the cone-beam projection of the balls' centers.

Abstract: In geometric calibration of cone-beam computed tomography (CBCT), sphere-like objects such as balls are widely imaged, the positioning information of which is obtained to determine the unknown geometric parameters. In this process, the accuracy of the detector location of CB projection of the center of the ball, which we call the center projection, is very important, since geometric calibration is sensitive to errors in the positioning information. Currently in almost all the geometric calibration using balls, the center projection is invariably estimated by the center of the support of the projection or the centroid of the intensity values inside the support approximately. Clackdoyle's work indicates that the center projection is not always at the center of the support or the centroid of the intensity values inside, and has given a quantitative analysis of the maximum errors in evaluating the center projection by the centroid. In this paper, an exact method is proposed to calculate the center projection, utilizing both the detector location of the ellipse center and the two axis lengths of the ellipse. Numerical simulation results have demonstrated the precision and the robustness of the proposed method. Finally there are some comments on this work with non-uniform density balls, as well as the effect by the error occurred in the evaluation for the location of the orthogonal projection of the cone vertex onto the detector.

Simple PI-PD tuning rules based on the centroid of the stability region for controlling unstable and integrating processes.

Abstract: Designing the parameters of a PI-PD controller is very challenging. Consequently, the centroid of the convex stability boundary locus approach was employed to overcome this challenge. Unfortunately, this approach requires deriving several equations for constructing the stability regions of the PI-PD controller. Also, it computes the centroid of the stability region based on visual observations without using any analytical methods. Therefore, it is time-consuming, and the accuracy of its computations is questionable. This paper suggests simple tuning rules for computing the gains of PI-PD controllers based on the centroid of the stability region to handle the limitations of the centroid of the convex stability boundary locus approach. A robustness analysis has also been conducted to gauge the performance of the proposed tuning rules. Moreover, several simulation examples and a real-time application have been considered for evaluating the effectiveness and the feasibility of the suggested approach.

Optical image and SAR image automatic registration method within multilevel multi-feature constraint

Abstract: The invention provides an optical image and SAR image automatic registration method within multilevel multi-feature constraint The optical image and SAR image automatic registration method within the multilevel multi-feature constraint comprises the following steps that optical images and SAR images are preprocessed, multi-scale level set segmentation is conducted, and a plane segmentation result is obtained; when similar plane targets exist, a coordinate set of centroid points of areas seemingly provided with the same name is calculated; when similar plane targets do not exist, multi-scale analysis is conducted on the images by means of wavelet transformation, extraction of lower-layer linear characteristics and point set matching are conducted on the thickest image, and lower-layer registration transformation parameters are extracted; high-layer linear characteristics is extracted, a control-point matching degree function is defined according to the lower-layer transformation parameters, and high-layer point set matching is conduced; finally, a matched point pair is precisely judged out through the KNN image from the structure, a wrong matched point pair is eliminated, transformation parameters of the matched point pair are obtained according to a polynomial transformational model, and a final registration result is obtained

Smallest Enclosing Circle Based Localization Approach for Wireless Sensor Networks

Abstract: A novel range-free localization approach, Smallest Enclosing Circle based Localization (SECL), has been proposed. This approach uses the center of smallest enclosing circle of neighboring anchor nodes to estimate the position of target. Comparing to Centroid, SECL considers the geometrical coverage of anchors rather than the mass center of them. Consequently, SECL is robust to imperfect topology, especially when the topology of anchors is depolyed unevenly. Simulation results show that SECL outperforms Centroid by an average of 10%, and even better when the topology is not uniform.