摘要
为了降低尺度不变特征变换(SIFT)算法的复杂度,提高算法的实时性,克服算法提取的特征点不是角点的缺点,该文提出了一种新的Harris-SIFT算法。该算法首先用Harris算子提取图像的特征点,然后为每个特征点定义主方向,最后将特征描述子的坐标旋转到与特征点的主方向一致,计算出每个特征点的特征向量描述子。双目立体视觉图像匹配实验结果说明了该算法的有效性。
In order to reduce the scale invariant feature transform （SIFT） algorithm＇s complexity, improve the real-time performance of the algorithm and ensure that the feature points are comers, a new Harris-SIFT algorithm is proposed. The feature points of images are detected by using Harris operator, the main orientation for each feature point is calculated, and lastly, the feature point descriptors are generated after rotating the coordinates of the descriptors relative to the feature points＇ main orientations. Experimental results of image matching in binocular stereo vision demonstrate the effectiveness of the proposed algorithm.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2010年第4期546-550,共5页
Journal of University of Electronic Science and Technology of China
基金
江苏省自然科学基金(BK2003005)
