摘要
在相似区域较多的图像匹配时,SIFT（Scale Invariant Feature Transform）算法的匹配计算（KDtree-BBF）较复杂,耗时长,很难满足实时性要求。提出一种改进的匹配算法,将特征点的周围邻域的主方向梯度作为特征之一,采用主方向梯度和欧式距离相结合的计算方法进行特征点的匹配。实验结果表明：改进的算法不仅简单易行,且对图像的旋转、缩放、光照变换均具有良好的鲁棒性,比较原Open SIFT算法还发现,改进算法的加速比范围为1.046~9.065。
In matching image with many similar regions,the original image matching algorithm（KDtree-BBF）based on SIFT（Scale Invariant Feature Transform）is complex,time-consuming,it is difficult to meet the real-time requirement.To overcome the shortcomings above,an improved algorithm is proposed.The method identifies the main gradient of direction of neighbor feature points as one of the features,which is combined with the distance similarity matching for matching.Experimental results show that the proposed algorithm not only is simple but also has a good robustness on the conditions of image rotating,zooming and lighting transformation.Compared with the original Open SIFT algorithm,the improved algorithm speedup ratio is in the range of 1.046~9.065.
出处
《计算机工程与应用》
CSCD
北大核心
2015年第13期149-152,共4页
Computer Engineering and Applications
基金
国家自然科学基金(No.91120002)
关键词
图像匹配
SIFT算子
主方向梯度
鲁棒性
image matching
Scale Invariant Feature Transform（SIFT）
main gradient of direction
robustness
