Representative Data Points Research Articles

ASOD: Arbitrary shape object detection

Arbitrary shape object detection, which is mostly related to computer vision and image processing, deals with detecting objects from an image. In this paper, we consider the problem of detecting arbitrary shape objects as a clustering application by decomposing images into representative data points, and then performing clustering on these points. Our method for arbitrary shape object detection is based on COMUSA which is an efficient algorithm for combining multiple clusterings. Extensive experimental evaluations on real and synthetically generated data sets demonstrate that our method is very accurate and efficient.

A space-efficient on-line method of computing quantile estimates

The task of computing an estimate for the quantile ( ζ q ) for an unknown distribution F (i.e., F( ζ q ) = q) is usually performed by the “sample quantile” method, which computes the ⌊ Nq⌋ + 1 smallest element from the set of N observations, and thus requires that all N samples be retained in memory. This paper introduces a recursive method of estimating ζ q based on the fact that if the terminal nodes of a uniform d-ary tree are assigned random values, independently drawn from a distribution F, then the minimax alue of the root node converges to a specified quantile of F for very tall trees. The new estimate is shown to be almost as precise as that produced by the sample quantile method and, like it, is guaranteed to converge to ζ q when the sample is large for any arbitrary distribution F. However, in contrast to the sample quantile computation the proposed method requires the retention in storage of at most log 2 N representative data points, where N is the number of samples observed in the past. Moreover, the estimate can be updated quickly using an average of 4, and a maximum of 2 log 2 N, comparisons with each new observation.

Method for analyses of cyclic physiological data that are nonstationary in time.

The proposed method of studying biologic rhythms permits the biologist to view physiological data dynamically without assuming that the data are stationary in time. Vector representation of data points is employed, and the summation of the vectors (train of vectors) produces a summation dial that is able to detect dynamic changes in the time of the peak as well as random walks (arrhythmia).