Minimal Neighborhood Mean Projection Function and Its Application to Eye Location

Abstract

PDF HTML阅读 XML下载 导出引用 引用提醒 最小邻域均值投影函数及其在眼睛定位中的应用 DOI: 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: Supported by the Opening Found of National Laboratory of Pattern Recognition of Institute of Automation, the Chinese Academy of Sciences (中国科学院自动化研究所模式识别国家重点实验室开放基金); the Graduate Innovation Fund of USTC under Grant No.KD2006042 (中国科学技术大学研究生创新基金) Minimal Neighborhood Mean Projection Function and Its Application to Eye Location Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:提出一种投影函数:最小邻域均值投影函数.该函数通过计算每条投影线上各像素点邻域均值的最小值来跟踪图像中的低灰度特征.与传统的积分投影函数和方差投影函数相比,它以求最小值的局部选择性代替传统投影函数的全局累加性,因此具有对片状噪声不敏感的特点.此外,在计算过程中,它还能记录最小值点的二维位置信息,是一个二维的搜索算子.最小邻域均值投影函数的这些特点使其非常适合于眼睛定位.它对眼睛,特别是瞳孔,总能够产生精确、鲁棒的响应.通过在CAS-PEAL数据库和BioID数据库上的实验表明,其定位正确率与精确度均高于传 Abstract:A projection function called minimal neighborhood mean projection function (MNMPF) is proposed. The projection function calculates and stores the minimal neighborhood mean of each pixel on each projection line, so that it is able to trace the low grayscale features in image. Compared with traditional projection functions, i.e. integral projection function (IPF) and variance projection function (VPF), MNMPF is insensitive to sheet noise, due to the local selectivity of its minimum operation. During the computation of MNMPF, the image locations of minima are recorded at the same time. This makes MNMPF a 2D operator. All these properties of MNMPF are very suitable for eye location. It can bring precise and robust response to eyes, especially pupils. Experiments on CAS-PEAL and BioID databases show its excellent correct rate and precision over traditional projection functions. 参考文献 相似文献 引证文献