视觉测量中亚像元图像特征定位算法

提出一种利用Facet模型进行图像特征高精度定位的新方法。通过构造一组二维的正交多项式基底，能快速拟合Facet模型的曲面参数方程来表示图像灰度分布，再沿曲面上最大梯度方向的二阶导数的零交叉高精度定位图像角点和边缘特征。该方法采用拟合方法代替插值方法，能减少对噪声的敏感，可提高曲面小片构成的灵活性与边缘检测的精度及计算效率。通过对多幅不同真实图像进行试验验证，结果证明该方法稳定可靠、精度较高，能在视觉测量中有效地提高测量精度。

IMAGE SUB-PIXEL FEATURE LOCATION ALGORITHM ON VISION MEASUREMENT SYSTEM

A new algorithm for effective sub-pixel image fea- tures detection based on facet model is proposed.Based on the idea that the discrete image intensity function can be considered as sampled and noisy approximation of the underlying con- tinuous or piecewise continuous image intensity function,a piece continuous function estimate called facets are used to represent the gray level intensity of the image pixel,which can be quickly gained by fitting a quadratic or cubic approximation surface on the constructed two-dimensional discrete chebyshev polynomials basis.The high precision image features of feature point and edge can be located with directional sec- ond-derivative zero-crossing operator on a local maximal gra- dient direction on the fitting facet surface.The image noise effect can be greatly reduced by using the image gray surface fitting method instead of the image interpolation,and can effi- ciently reduce the computational time.Experiments on many real images of a sub-pixel feature point detector and a sub-pixel edge detector are carried out,the results show that this method is stable,effective and precise localization,this sub-pixel fea- ture detector can effectively improve the precision of the vision calibration and measurement system.