关键词: 尺度不变的特征变换, 显著边缘特征, 小波边缘检测, 度量函数, 随机一致性检验

Abstract: Considering that the Scale Invariant Feature Transform (SIFT) algorithm extracts a great number of feature points, consumes a lot of matching time but with low matching accuracy, a fast image registration algorithm based on local significant edge features was proposed. Then SIFT algorithm was used to extract feature points, while wavelet edge detection was also used to extract image edge to establish feature points around the edge of the neighborhood characteristics, which filtered out points with a significant edge feature characteristic as significant feature points. A feature vector was formed by the shape-context operator and edge features. Euclidean distance was used as the match metric function to preliminarily match the feature points extracted from different images. Afterwards, RANdom SAmple Consensus (RANSAC) algorithm was applied to eliminate the mismatching points. The experimental results show that the algorithm effectively controlled the number of feature points, improved qulity of the feature points, reduced the feature search space and enhanced the efficiency of the feature matching.

Key words: Scale Invariant Feature Transform (SIFT), significant edge feature, wavelet edge detection, metric function, RANdom SAmple Consensus (RANSAC)