A Bayesian approach to object detection

被引：0

作者：

Nikulin, V ^{[1
]}

机构：

[1] CSIRO, Div Marine Res, Hobart, Tas, Australia

来源：

IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE | 1998年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A binary hypothesis testing is a common tool in the task of object detection. Experiments with real images have confirmed an effectiveness of Neyman-Pearson Detectors based on locally weighted sample mean and variance. In line with Bayesian approach threshold parameter will be defined as a function of prior distribution. According to the basic idea of Gibbs Sampler the neighbourhood system of image element determines distribution of this element. Using concepts of the first part of this paper and taking any image as an initial lye can form sequence of images in order to develop this dependence. As a result quality of object detection will be improved significantly.

引用

页码：809 / 813

页数：5

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