Yu Luo, Liu Hongying, Pi Xitian, Chen Weiming, Xu Shuai, Yan Xu
School of Biomedical Engineering, Chongqing University, Chongqing, 400030.
Key Laboratories for National Defense Science and Technology of Innovative Micro-Nano Devices and System Technology, Chongqing University, Chongqing, 400030.
Zhongguo Yi Liao Qi Xie Za Zhi. 2017 May 30;41(3):170-174. doi: 10.3969/j.issn.1671-7104.2017.03.004.
In order to realize the requirement of precise eye tracking in clinical, a pupil center location algorithm based on the least square method is proposed. First, the eye image was captured by the camera under the infrared light, and then the two-valued image was obtained after preprocessing. Use the number of pixels that form the outline which was Extracting from the two-valued image to carry out the rough filtration of the pupil. The ellipse curve was fitting by the randomly select 6 pixels on the filtered contours, and then calculated the variance of distance between the center and the edge of the ellipse. The ellipse center is ellipse center, where the variance of the distance is Minimum. The algorithm has advantage of accurate identification the pupil center when there is white spot interference and squint. Experiments show that the pupil center can be located quickly and real-timely by this algorithm.
为了实现临床中精确眼动追踪的需求，提出了一种基于最小二乘法的瞳孔中心定位算法。首先，通过相机在红外光下采集眼睛图像，然后经过预处理得到二值图像。利用从二值图像中提取的构成轮廓的像素数量对瞳孔进行粗滤。通过在滤波后的轮廓上随机选取6个像素拟合椭圆曲线，然后计算椭圆中心与边缘之间距离的方差。椭圆中心即距离方差最小的点为椭圆中心。该算法在有白斑干扰和斜视情况下具有准确识别瞳孔中心的优势。实验表明，通过该算法能够快速、实时地定位瞳孔中心。
