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: The proposed algorithm can solve two weaknesses of the k-prototype algorithm, which is a well-known algorithm for clustering mixed data that has two main weaknesses, the use of mode as a cluster center for categorical attributes cannot accurately represent the objects and the algorithm may stop at the local optimum solution.
12 citations
TL;DR: The centroid, variance, eccentricity, and angle of the principal axes are suggested as pertinent parameters, and an image chopping technique for fast measurement of these parameters is described.
Abstract: In several applications it is necessary to get a fast evaluation of the shape and quality of an image of a point source. Since a complete description by means of the point spread function involves time-consuming data processing, it is necessary to limit the evaluation to a set of parameters which can be measured at high rates. Such a set can be defined in terms of the first and second moments of the distribution. In this article the centroid, variance, eccentricity, and angle of the principal axes are suggested as pertinent parameters, and an image chopping technique for fast measurement of these parameters is described.
12 citations
01 Apr 1997
TL;DR: The algorithm presented in this paper provides a platform for a noninvasive, multidimensional eye measurement system which can be used for clinical and research applications requiring the precise recording of eye movements in three-dimensional space.
Abstract: This paper describes a video eye-tracking algorithm which searches for the best fit of the pupil modeled as a circular disk. The algorithm is robust to common image artifacts such as the droopy eyelids and light reflections while maintaining the measurement resolution available by the centroid algorithm. The presented algorithm is used to derive the pupil size and center coordinates, and can be combined with iris-tracking techniques to measure ocular torsion. A comparison search method of pupil candidates using pixel coordinate reference lookup tables optimizes the processing requirements for a least square fit of the circular disk model. This paper includes quantitative analyses and simulation results for the resolution and the robustness of the algorithm. The algorithm presented in this paper provides a platform for a noninvasive, multidimensional eye measurement system which can be used for clinical and research applications requiring the precise recording of eye movements in three-dimensional space.
12 citations
TL;DR: This paper establishes approximation bounds for another microaggregation heuristic, providing better approximation guarantees of O(k2) for the squared distance measure and O( k) forThe distance measure.
Abstract: The NP-hard microaggregation problem seeks a partition of data points into groups of minimum specified size k, so as to minimize the sum of the squared euclidean distances of every point to its group's centroid. One recent heuristic provides an O(k3) guarantee for this objective function and an O(k2) guarantee for a version of the problem that seeks to minimize the sum of the distances of the points to its group's centroid. This paper establishes approximation bounds for another microaggregation heuristic, providing better approximation guarantees of O(k2) for the squared distance measure and O(k) for the distance measure.
12 citations
TL;DR: This work applies Centroid Linkage Clustering, to identify duplicated regions in an image, from matched key-points, and introduces a Graph Similarity Matching algorithm, to optimize false matches.
Abstract: Region duplication or copy–move forgery is an attack in which a region of an image is copied and pasted onto another location of the same image In the recent state–of–the–art, a number of key–point based methods have been proposed for copy–move forgery detection in digital images Though the problems of re–scaling and rotation in region duplication, have been sufficiently investigated using key–point based methods, post-processing based attacks such as flip, blur, brightness and noise, remain an open challenge in this field In this paper, we address the problem of copy–move forgery detection in images, plus aim to identify copied regions, having undergone different geometric (such as rotation, re–scale), and post–processing attacks (such as Gaussian noise, blurring and brightness adjustment) In the proposed algorithm we introduce a region based key–point selection concept, which is considerably more discriminative than single SIFT key–point extraction In this work, we apply Centroid Linkage Clustering, to identify duplicated regions in an image, from matched key-points Also, we introduce a Graph Similarity Matching algorithm, to optimize false matches Our experimental results demonstrate the efficiency of the proposed method in terms of forgery detection and localization efficiency, for a wide range of geometric and post-processing based attacks in region duplication
12 citations