摘要
小波变换具有数据压缩和检测信号局部突变的能力,而SIFT(尺度不变特征变换)对于平移、旋转、缩放和部分遮挡具有不变性。结合小波变换与SIFT特征提出了一种有效的工件图像匹配方法。该方法将原始图和模板图做小波分解以获得粗尺度的平滑图像;利用DoG算子对工件图像进行关键点检测,进而用欧氏距离对关键点进行特征匹配,最后对特征点进行错配消除。因此,两者优势的结合不但可以有效减少工件图像匹配的计算量,而且还可以减弱对于图像采集平台拍摄方位、拍摄距离、角度、光照条件等的依赖性,提高算法的实用性。
Wavelet transform provides itself with the capability of data compression and detecting local signal mutation, while scale invariant feature transform （SIFT） have the invariant ability of translation, rotation, scale and part of occlusion. An effective method of work-piece image matching is proposed by combining wavelet transform with SIFT. The smooth image of coarse scale can be obtained through wavelet decomposition for the original image and the model image by this method. The key points are detected using difference of gaussians （DoG） operator and are matched using Euclidean distance, then the points of error matching are eliminated. Therefore, combination of these two methods can effectively reduce computation cost for work-piece image matching and the dependence on image acquisitive position, direction, light condition etc, which improves the algorithm application.
出处
《机械科学与技术》
CSCD
北大核心
2009年第5期638-642,共5页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金项目(10872160)
陕西省教育厅省级重点实验室(机械制造装备重点实验室)重点科研计划项目(05JS29)资助
关键词
工件识别
图像匹配
SIFT
小波分解
work-piece recognition
image matching
scale invariant feature transform
wavelet decomposition
