视野计中瞳孔自动跟踪系统的设计实现

Design and Implementation of Pupil Auto-tracking System in the Perimeter

瞳孔自动跟踪系统是全自动视野计的关键组成部分。本文利用积分图计算特征值快速的优点,在训练Adaboost强分类器的基础上研究了训练级数与分类器总误检率的关系,引入逐层放大检测窗口的检测策略,提出了一种多层级联的层叠分类器用于眼睛定位与跟踪。该算法的应用在保留了传统强分类器高检测率的基础上,明显降低了对目标图像的误检率。同时文章阐述了算法的主要实现函数并设计了下颚托架系统的运动控制方案。

The pupil auto-tracking system is a key component of the full-automatic perimeter. Taking the advantage of integral image in counting characteristic value rapidly, we studied the relationship between training stages and total error detection rate based on the training of Adaboost strong classifier. Besides, a testing strategy of amplification detection window was introduced, and a multi-stage cascaded eye classifier for eye detection was proposed finally. It kept the same detection rate as the commonly trained strong classifier with a much lower error detection rate. In the meantime, the present article explaines the main arithmetic implement functions, as well as designs the motion control program for the jaw bracket system.