Name
Title | ||
|---|---|---|
Be-Sift: A More Brief And Efficient Sift Image Matching Algorithm For Computer Vision |
Abstract | ||
|---|---|---|
In the area of computer vision, pattern recognition and image processing, image match is a research hotspot with important theoretical significance and practical value. Recently, the image matching algorithm based on SIFT has drown wide attention for its outstanding local feature matching performance. In view of high computation complexity, poor anti-noise ability, and difficulty for practical use of the original SIFT method, a more brief and efficient SIFT image matching algorithm: BE-SIFT is proposed in this paper for computer vision. Through collaborative improvement and optimization on detection of SIFT scale space feature points, allocation of key point principal direction, and calculation of feature descriptors, the proposed method has achieved more brief dimension expression and more efficient local feature match than other existing methods. Experiments based on the Affine Covariant Features dataset provided by Oxford VGG Group demonstrate that BE-SIFT has stronger robustness for image match under the condition of viewpoint changes, rotation + scale changes, blur changes and noise changes. What's more, the computational overhead is sharply reduced and the real-time performance is greatly improved while ensuring the uniqueness of local feature and satisfactory accuracy. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CIT/IUCC/DASC/PICOM.2015.81 | CIT/IUCC/DASC/PICOM 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY - UBIQUITOUS COMPUTING AND COMMUNICATIONS - DEPENDABLE, AUTONOMIC AND SECURE COMPUTING - PERVASIVE INTELLIGENCE AND COMPUTING |
Keywords | Field | DocType |
SIFT, BE-SIFT, image match, local feature, real-time | Template matching,Feature detection (computer vision),Computer science,Scale space,Principal curvature-based region detector,Artificial intelligence,Kanade–Lucas–Tomasi feature tracker,Computer vision,Pattern recognition,Feature (computer vision),Algorithm,Feature extraction,Histogram of oriented gradients | Conference |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Zhao | 1 | 59 | 5.07 |
| Hengzhu Liu | 2 | 86 | 23.28 |
| Yiliu Feng | 3 | 6 | 3.13 |
| Shandong Yuan | 4 | 0 | 0.34 |
| Wanzeng Cai | 5 | 0 | 1.01 |