Research of different feature detection and matching algorithms on panoramic image

Research Article
Open access

Research of different feature detection and matching algorithms on panoramic image

Jindong Xiao 1*
  • 1 Shenzhen University    
  • *corresponding author 2020151007@email.szu.edu.cn
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/15/20230808
ACE Vol.15
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-021-9​
ISBN (Online): 978-1-83558-022-6

Abstract

Image stitching is the process of combining numerous photos to make a panorama. The technique of image stitching has rapidly advanced and grown to be a significant area of digital image processing. Many image stitching methods have been proposed and studied in prior study. In this paper, the image stitching process is implemented using different algorithms. For keypoints detection, the algorithms of Harris corner detection, SIFT(Scale-Invariant Feature Transform), SURF(Speeded Up Robust Feature) and ORB(Oriented FAST and Rotated BRIEF) algorithms are applied, then use different methods (i.e., Brute Force, etc.) for feature matching. The RANSAC(Random Sample Consensus) method is used to calculate a homography matrix from matched feature vectors and use it to warp the images. Image blending and cropping methods are proposed to enhance the image quality. Given groups of the self-captured images, experiments have been down to shown the performance of different techniques.

Keywords:

panoramic mosaic, SIFT, Harris, SURF, ORB, blending, warping, homography

Xiao,J. (2023). Research of different feature detection and matching algorithms on panoramic image. Applied and Computational Engineering,15,38-51.
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References

[1]. Zhaobin Wang, and Zekun Yang. Review on image-stitching techniques. 2020, Multimedia Systems 26: 413-430.

[2]. Sánchez J, Monzón N, Salgado De La Nuez A. An analysis and implementation of the harris corner detector. 2018, Image Processing on Line.

[3]. Lowe, D.G. Distinctive Image Features from Scale-Invariant Keypoints. 2014, International Journal of Computer Vision 60, 91–110.

[4]. Yan Ke and R. Sukthankar, PCA-SIFT: a more distinctive representation for local image descriptors, 2004. IEEE Conference on Computer Vision and Pattern Recognition. 137-149.

[5]. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. 2006 European Conference on Computer Vision. 3951, 404–417.

[6]. Herbert B., Andreas E., Tinne T. and Luc Van G.: Speeded up Robust Feature (SURF), 2008 Computer Vision and Image Understanding, 110 (3): 346- 359.

[7]. Utsav S., Darshana M. and Asim B.: Image Registration of Multi-View Satellite Images Using Best Feature Points Detection and Matching Methods from SURF, SIFT and PCA-SIFT 1(1): 2014 European Conference on Computer Vision 8-18.

[8]. E. Rublee, et al. ORB: An efficient alternative to SIFT or SURF. 2011 International conference on computer vision, 1-11.

[9]. M. Calonder, V. Lepetit, C. Strecha, and P. Fua. Brief: Binary robust independent elementary features. 2010, European Conference on Computer Vision, 1-10.

[10]. S. A. Bakar, X. Jiang, X. Gui, and G. Li, Image Stitching for Chest Digital Radiography Using the SIFT and SURF Feature Extraction by RANSAC Algorithm Image Stitching for Chest Digital Radiography Using the SIFT and SURF Feature Extraction by RANSAC Algorithm, 2020, European Conference on Computer Vision 1-12.

[11]. Fischler, Martin A., and Robert C. Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. 2020 Communications of the ACM 24.6, 381-395.

Cite this article

Xiao,J. (2023). Research of different feature detection and matching algorithms on panoramic image. Applied and Computational Engineering,15,38-51.

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：978-1-83558-021-9​(Print) / 978-1-83558-022-6(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.15
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Zhaobin Wang, and Zekun Yang. Review on image-stitching techniques. 2020, Multimedia Systems 26: 413-430.

[2]. Sánchez J, Monzón N, Salgado De La Nuez A. An analysis and implementation of the harris corner detector. 2018, Image Processing on Line.

[3]. Lowe, D.G. Distinctive Image Features from Scale-Invariant Keypoints. 2014, International Journal of Computer Vision 60, 91–110.

[4]. Yan Ke and R. Sukthankar, PCA-SIFT: a more distinctive representation for local image descriptors, 2004. IEEE Conference on Computer Vision and Pattern Recognition. 137-149.

[5]. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. 2006 European Conference on Computer Vision. 3951, 404–417.

[6]. Herbert B., Andreas E., Tinne T. and Luc Van G.: Speeded up Robust Feature (SURF), 2008 Computer Vision and Image Understanding, 110 (3): 346- 359.

[7]. Utsav S., Darshana M. and Asim B.: Image Registration of Multi-View Satellite Images Using Best Feature Points Detection and Matching Methods from SURF, SIFT and PCA-SIFT 1(1): 2014 European Conference on Computer Vision 8-18.

[8]. E. Rublee, et al. ORB: An efficient alternative to SIFT or SURF. 2011 International conference on computer vision, 1-11.

[9]. M. Calonder, V. Lepetit, C. Strecha, and P. Fua. Brief: Binary robust independent elementary features. 2010, European Conference on Computer Vision, 1-10.

[10]. S. A. Bakar, X. Jiang, X. Gui, and G. Li, Image Stitching for Chest Digital Radiography Using the SIFT and SURF Feature Extraction by RANSAC Algorithm Image Stitching for Chest Digital Radiography Using the SIFT and SURF Feature Extraction by RANSAC Algorithm, 2020, European Conference on Computer Vision 1-12.

[11]. Fischler, Martin A., and Robert C. Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. 2020 Communications of the ACM 24.6, 381-395.

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