SURF feature descriptor for image analysis

Ashwani Kumar

Article ID: 5643
Vol 6, Issue 1, 2023

VIEWS - 79 (Abstract) 53 (PDF)

Abstract


This paper provides a comprehensive review of SURF (speeded up robust features) feature descriptor, commonly used technique for image feature extraction. The SURF algorithm has obtained significant popularity because to its robustness, efficiency, and invariance to various image transformations. In this paper, an in-depth analysis of the underlying principles of SURF, its key components, and its use in computer vision tasks such as object recognition, image matching, and 3D reconstruction are proposed. Furthermore, we discuss recent advancements and variations of the SURF algorithm and compare it with other popular feature descriptors. Through this review, the aim is to provide a clear understanding of the SURF feature descriptor and its significance in the area of computer vision.


Keywords


SURF (speeded up robust features); image matching; feature detection; scale-invariant; local descriptors; robustness to affine transformations

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References


1. Bay H, Tuytelaars T, Van Gool L. Surf: Speeded up robust features. In: European conference on computer vision. Springer, Berlin, Heidelberg; 2006. pp. 404-417.

2. Lowe DG. Distinctive image features from scale-invariant keypoints. International journal of computer vision. 2004; 60(2): 91-110.

3. Rublee E, Rabaud V, Konolige K, et al. ORB: An efficient alternative to SIFT or SURF. 2011 International Conference on Computer Vision. doi: 10.1109/iccv.2011.6126544

4. Leutenegger S, Chli M, Siegwart RY. BRISK: Binary Robust invariant scalable keypoints. 2011 International Conference on Computer Vision. doi: 10.1109/iccv.2011.6126542

5. Bay H, Ess A, Tuytelaars T, et al. Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding. 2008; 110(3): 346-359. doi: 10.1016/j.cviu.2007.09.014

6. Mahmoudi M, Tjahjadi T. An Analysis of Speeded-Up Robust Features in Scale Space. Pattern Recognition. 2012; 45(1): 411-424.

7. Liu J, Liu H, Li S. An Improved SURF Algorithm Based on SIFT. Journal of Visual Communication and Image Representation. 2013; 24(7): 1039-1046.

8. Liu Q, Yang X. Improving the Speeded-Up Robust Features (SURF) Algorithm Based on Image Smoothing. Journal of Visual Communication and Image Representation. 2014; 25(5): 1056-1064.

9. Li W, Xu H, Xiao B, et al. SURF Image Matching Algorithm Based on GPU. In: Proceedings of the 8th International Symposium on Computational Intelligence and Design; 2015.

10. Choi S, Kim J, Chong I. Comparative Study of SIFT and SURF for Image Stitching. In: Proceedings of the 2016 International Conference on Information Science and Security; 2016.

11. Kumar V, Chauhan V, Kumar V. Comparative Analysis of SIFT, SURF, BRISK, FREAK and ORB for Robust Object Recognition. In: Proceedings of the 2017 IEEE 7th International Advance Computing Conference (IACC); 2017.

12. Al-Naymat G, Bani-Ahmad S. Improved Speeded-Up Robust Features (SURF) Algorithm for Efficient Image Matching. Journal of Engineering. 2018; 2018: 1-11.

13. Li H, Xu Q, Wang G, et al. An Improved SURF Algorithm Based on Quadrant Histograms. Advances in Intelligent Systems and Interactive Applications. 2019; 25-32.

14. Paudel U, Jin J. Improved SURF-Based Image Retrieval System Using Color Features. Symmetry. 2021; 13(4): 669.

15. Bay H, Tuytelaars T, Van Gool L. SURF: Speeded up robust features. In: Proceedings of the European conference on computer vision (ECCV); 2006.

16. Liu M, Yu Z, Jin M, et al. A novel feature descriptor based on SURF for image matching. Multimedia Tools and Applications. 2017; 76(10): 12849-12866.

17. Ahmad I, Ahmad J, Choi TS, et al. Optimized Speeded-Up Robust Feature (SURF) for real-time object recognition on mobile devices. Multimedia Tools and Applications. 2018; 77(4): 4083-4105.

18. Shi Z, Deng Y, Zhang H, et al. Multi-scale SURF-based local feature descriptor for image matching. Multimedia Tools and Applications. 2019; 78(20): 28593-28611.

19. Li Z, Zhang H, Zhang H. Improved SURF feature descriptor based on principal component analysis for image matching. Multimedia Tools and Applications. 2020; 79(5-6): 3415-3431.

20. Chen L, Yan L, Yang J, et al. A new feature descriptor based on SURF for image matching. Journal of Visual Communication and Image Representation. 2021; 76: 103117.




DOI: https://doi.org/10.24294/irr.v6i1.5643

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