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.

Method and apparatus for determining measured quantities with a centroid detecting encoder

Abstract: A plurality of marks are carried on a movable member, the position of which represents a measured quantity. An image of a mark is projected onto a surface having a large number of detectors. The output signals of the detectors are fed into an evaluation circuit, which sequentially determines the intensity distribution on the detector and, thereby, the position of the mark by means of comparison of fixed threshold values with each of the detector signals and determines the centroid of the thus obtained intensity value distribution. The position of the centroid is a measure of the quantity to be measured.

Object Detection Using A Shape Codebook

Abstract: This paper presents a method for detecting categories of objects in real-world images. Given training images of an object category, our goal is to recognize and localize instances of those objects in a candidate image. The main contribution of this work is a novel structure of the shape codebook for object detection. A shape codebook entry consists of two components: a shape codeword and a group of associated vectors that specify the object centroids. Like their counterpart in language, the shape codewords are simple and generic such that they can be easily extracted from most object categories. The associated vectors store the geometrical relationships between the shape codewords, which specify the characteristics of a particular object category. Thus they can be considered as the “grammar” of the shape codebook. In this paper, we use Triple-Adjacent-Segments (TAS) extracted from image edges as the shape codewords. Object detection is performed in a probabilistic voting framework. Experimental results on public datasets show performance similiar to the state-of-the-art, yet our method has significantly lower complexity and requires considerably less supervision in the training (We only need bounding boxes for a few training samples, do not need figure/ground segmentation and do not need a validation dataset).

Adaptive centroid-finding algorithm for freeform surface measurements

Abstract: Wavefront sensing systems measure the slope or curvature of a surface by calculating the centroid displacement of two focal spot images. Accurately finding the centroid of each focal spot determines the measurement results. This paper studied several widely used centroid-finding techniques and observed that thresholding is the most critical factor affecting the centroid-finding accuracy. Since the focal spot image of a freeform surface usually suffers from various types of image degradation, it is difficult and sometimes impossible to set a best threshold value for the whole image. We propose an adaptive centroid-finding algorithm to tackle this problem and have experimentally proven its effectiveness in measuring freeform surfaces.

Method for training of subspace coded gaussian models

Abstract: A method for compressing multiple dimensional gaussian distributions with diagonal covariance matrixes includes clustering a plurality of gaussian distributions in a multiplicity of clusters for each dimension. Each cluster can be represented by a centroid having a mean and a variance. A total decrease in likelihood of a training dataset is minimized for the representation of the plurality of gaussian distributions.

Fast algorithm to calculate generalized centroid of interval type-2 fuzzy set

Abstract: The relationship between the switch point on the membership function and the generalized centroid in an interval type-2 fuzzy set is discussed.A close form representation of the switch point for calculating the endpoint of the generalized centroid is provided in both discrete and continuous conditions.Then an opposite direction searching method for computing the switch point is proposed.The convergence of the switch point is proved.Experimental result shows that the approach is computationally faster than the commonly used Enhanced Karnik-Mendel method without loosing numeric precision.