원문정보
초록
영어
Feature-points matching is an important concept in binocular stereo vision. The procession of multi-scale feature-points matching in classical Harris-SIFT algorithm is time-consuming and has high complexity when describing the feature-points. This paper proposed a new improved Harris-SIFT algorithm based on rotation-invariant LBP (Local binary patterns) operator. Firstly, the Harris operator is used to extract feature points from DOG (Difference of Gaussian) scale space. Then, the dominant direction of feature point is calculated and 81-dimensional rotation-invariant LBP descriptors are extracted when the rotation matching window is coordinated to this direction. At last, Best-Bin-First (BBF) algorithm is used to search the matching points between the two sets of feature points. Experimental results show that the proposed algorithm is lower time-consuming than classical Harris-SIFT algorithm and remains the similar matching correct rate.
목차
1. Introduction
2. Harris Feature-Points Detection
2.1. DOG Scale Space
2.2. Harris Operator
2.3. Orientation Assignment
3. LBP Feature Operator
3.1. Classical LBP Operator
3.2. Rotation-Invariant LBP Operator
3.3. Rotation-Invariant LBP Feature Operator
4. The Proposed Algorithm
5. Experimental Results and Discussion
5.1. Experiment on the Benchmark Images
5.2. Experiment on Our Captured Images
6. Conclusion
Acknowledgements
References