An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Reading the legends of Roman Republican coins
Journal on Computing and Cultural Heritage (JOCCH)
| Hi-index | 0.00 |
Feature-based image matching is one of the most fundamental issues in computer vision tasks. As the number of features increases, the matching process rapidly becomes a bottleneck. This paper presents a novel method to speed up SIFT feature matching. The main idea is to extend SIFT feature by a few pairwise independent angles, which are invariant to rotation, scale and illumination changes. During feature extraction, SIFT features are classified based on their introduced angles into different clusters and stored in multidimensional table. Thus, in feature matching, only SIFT features that belong to clusters, where correct matches may be expected are compared. The performance of the proposed methods was tested on two groups of images, realworld stereo images and standard dataset images, through comparison with the performances of two state of the arte algorithms for ANN searching, hierarchical k-means and randomized kd-trees. The presented experimental results show that the performance of the proposed method extremely outperforms the two other considered algorithms. The experimental results show that the feature matching can be accelerated about 1250 times with respect to exhaustive search without losing a noticeable amount of correct matches.