模式图像的特征提取与配准方法  

Feature Extraction and Matching Algorithm of Pattern Image

作　　者：杨洋[1] 项辉宇[1] 薛真[1] 魏景辉[1] 张德兵[1] 

机构地区：[1]北京工商大学,北京100048

出　　处：《包装工程》2017年第1期185-190,共6页Packaging Engineering

基　　金：2016年研究生科研能力提升计划

摘　　要：目的提高产品包装模式图像的匹配速度和准确性,得出算法中影响这两方面的关键因素。方法运用高斯金字塔对原始图像进行向下采样,将图像转换为尺度空间的表示方式;利用加速稳健特征(SURF)方法提取特征点,并对SURF方法提取的特征点用Brute-Force算法进行匹配;通过对每组匹配点欧式距离的排序,剔除误匹配点,仅保留一定数量的正确匹配点对。结果得出高斯金字塔、Hessian值、颜色空间对计算速度及获取特征点数量的影响。颜色空间的改变不影响特征点数量,在计算时间上的差别也可忽略。尺度空间缩小1/2时,计算速度能够提升75%,同时剔除2/3的冗余特征点。结论文中方法能够有效提升特征提取速度,并且具有匹配精度高、鲁棒性强的特点,同时对旋转角度的变化具有较强的适应性。The work aims to improve the matching speed and accuracy of product packaging pattern images and obtain the key factors that affect these two aspects in this algorithm. By using Gauss Pyramid to sample down the original images, the images were converted to a scale space representation. Then speed up robust features（SURF） method was adopted to extract feature points, and Brute-Force algorithm was used to match these points. Finally, after sorting the Euclidean distance of the matching points in each group, the mismatching points were eliminated and a certain quantity of correct matching points remained. The influence of Gauss Pyramid, Hessian value and color space on the calculation speed and the number of the extracted feature points were obtained. The change of color space did not affect the number of feature points, and the difference of the computation time could be ignored. When the scale space was reduced by 1/2, the computation speed could be improved by 75%, and 2/3 of the redundant feature points were eliminated at the same time. The method in the paper can effectively improve the speed of feature extraction, and is characterized by high matching precision and strong robustness. Meanwhile, it has strong adaptability to the change of rotation angles.

关 键 词：高斯金字塔 SURF 特征提取 配准 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]