Comparative study of feature extraction algorithms for panorama stitching

Research Article
Open access

Comparative study of feature extraction algorithms for panorama stitching

Tianxingjian Ding 1*
  • 1 Zhejiang University    
  • *corresponding author 3200104836@zju.edu.cn
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/16/20230900
ACE Vol.16
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-023-3
ISBN (Online): 978-1-83558-024-0

Abstract

Panorama stitching is a fascinating and rapidly advancing research field. By integrating many photographs that were taken from various angles and viewpoints, with various exposure and color settings, a seamless image is primarily the aim of panorama stitching. This paper investigates the performance of three widely used feature extraction algorithms Speeded-Up Robust Features (SURF), Scale-Invariant Feature Transform (SIFT), and Oriented FAST and Rotated BRIEF (ORB) for panorama stitching. The study compares these algorithms in terms of accuracy, robustness, and speed. Results indicate that while SURF and SIFT produce more accurate and robust results than ORB, they require longer processing time. The study evaluates the approach on a real-world dataset and demonstrates its effectiveness in creating seamless and visually appealing panoramas. This study provides valuable insights into the trade-offs between different feature extraction algorithms and presents a practical solution for panorama stitching applications.

Keywords:

panorama stitching, feature extraction algorithms, SURF, SIFT, ORB

Ding,T. (2023). Comparative study of feature extraction algorithms for panorama stitching. Applied and Computational Engineering,16,249-256.
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References

[1]. H. Bay, T. Tuytelaars, and L. Van Gool, "SURF: Speeded up robust features," in 2006 European Conference on Computer Vision, Graz, Austria, 2006, pp. 404-417. DOI: 10.1007/11744023_32.

[2]. M. Calonder, V. Lepetit, C. Strecha, and P. Fua, "BRIEF: Binary Robust Independent Elementary Features," in 2010 European Conference on Computer Vision, Heraklion, Greece, 2010, pp. 778-792. DOI: 10.1007/978-3-642-15567-3_56.

[3]. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "ORB: An efficient alternative to SIFT or SURF," in 2011 International Conference on Computer Vision, Barcelona, Spain, 2011, pp. 2564-2571. DOI: 10.1109/ICCV.2011.6126544.

[4]. K. Mikolajczyk and C. Schmid, "Scale and Affine Invariant Interest Point Detectors," International Journal of Computer Vision, vol. 60, no. 1, pp. 63-86, Nov. 2004. DOI: 10.1023/B:VISI.0000027790.02288.f2.

[5]. T. Tuytelaars and L. Van Gool, "Wide Baseline Stereo Matching Based on Local, Affinely Invariant Regions," in 2000 British Machine Vision Conference, Bristol, UK, 2000, pp. 412-422.

[6]. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, "Speeded-Up Robust Features (SURF)," Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, Jun. 2008. DOI: 10.1016/j.cviu.2007.09.014.

[7]. L. Liu and W. Ouyang, "SIFT Meets CNN: A Decade Survey of Instance Retrieval," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 8, pp. 1545-1565, Aug. 2017. DOI: 10.1109/TPAMI.2016.2631055.

[8]. P. J. Burt and R. J. Kolczynski, "Enhanced Image Capture Through Fusion," in 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA, 2003, vol. 2, pp. II-173-II-180. DOI: 10.1109/CVPR.2003.1211459.

[9]. Z. Lin, W. Gao, J. Jia, et al., "CapsNet Meets SIFT: A Robust Framework for Distorted Target Categorization," Neurocomputing, vol. 464, pp. 290-316, Oct. 2021. DOI: 10.1016/j.neucom.2021.06.101.

[10]. H.-J. Chien, C.-C. Chuang, C.-Y. Chen, et al., "When to Use What Feature? SIFT, SURF, ORB, or A-KAZE Features for Monocular Visual Odometry," in 2016 International Conference on Image and Vision Computing New Zealand, Palmerston North, New Zealand, 2016, pp. 1-9. DOI: 10.1109/IVCNZ.2016.7804432.

Cite this article

Ding,T. (2023). Comparative study of feature extraction algorithms for panorama stitching. Applied and Computational Engineering,16,249-256.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-023-3(Print) / 978-1-83558-024-0(Online)
Editor：Marwan Omar, Roman Bauer, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.16
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. H. Bay, T. Tuytelaars, and L. Van Gool, "SURF: Speeded up robust features," in 2006 European Conference on Computer Vision, Graz, Austria, 2006, pp. 404-417. DOI: 10.1007/11744023_32.

[2]. M. Calonder, V. Lepetit, C. Strecha, and P. Fua, "BRIEF: Binary Robust Independent Elementary Features," in 2010 European Conference on Computer Vision, Heraklion, Greece, 2010, pp. 778-792. DOI: 10.1007/978-3-642-15567-3_56.

[3]. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "ORB: An efficient alternative to SIFT or SURF," in 2011 International Conference on Computer Vision, Barcelona, Spain, 2011, pp. 2564-2571. DOI: 10.1109/ICCV.2011.6126544.

[4]. K. Mikolajczyk and C. Schmid, "Scale and Affine Invariant Interest Point Detectors," International Journal of Computer Vision, vol. 60, no. 1, pp. 63-86, Nov. 2004. DOI: 10.1023/B:VISI.0000027790.02288.f2.

[5]. T. Tuytelaars and L. Van Gool, "Wide Baseline Stereo Matching Based on Local, Affinely Invariant Regions," in 2000 British Machine Vision Conference, Bristol, UK, 2000, pp. 412-422.

[6]. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, "Speeded-Up Robust Features (SURF)," Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, Jun. 2008. DOI: 10.1016/j.cviu.2007.09.014.

[7]. L. Liu and W. Ouyang, "SIFT Meets CNN: A Decade Survey of Instance Retrieval," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 8, pp. 1545-1565, Aug. 2017. DOI: 10.1109/TPAMI.2016.2631055.

[8]. P. J. Burt and R. J. Kolczynski, "Enhanced Image Capture Through Fusion," in 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA, 2003, vol. 2, pp. II-173-II-180. DOI: 10.1109/CVPR.2003.1211459.

[9]. Z. Lin, W. Gao, J. Jia, et al., "CapsNet Meets SIFT: A Robust Framework for Distorted Target Categorization," Neurocomputing, vol. 464, pp. 290-316, Oct. 2021. DOI: 10.1016/j.neucom.2021.06.101.

[10]. H.-J. Chien, C.-C. Chuang, C.-Y. Chen, et al., "When to Use What Feature? SIFT, SURF, ORB, or A-KAZE Features for Monocular Visual Odometry," in 2016 International Conference on Image and Vision Computing New Zealand, Palmerston North, New Zealand, 2016, pp. 1-9. DOI: 10.1109/IVCNZ.2016.7804432.

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