Skip to main content
SpringerLink
Log in

Low-Textural Image Registration: Comparative Analysis of Feature Descriptors

  • Conference paper
  • First Online:
Computer Vision and Image Processing (CVIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1777))

Included in the following conference series:

  • 404 Accesses

Abstract

Industrial machine-vision (MV) applications require high-speed stitching of low-textural images from multiple high-resolution cameras for Field-of-View expansion. The most vital step in the stitching process is the effective and efficient extraction of features, which becomes challenging for low-textural images. This paper presents a comparative study of five popular feature descriptor algorithms for image stitching viz. Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF), Oriented Fast and Rotated BRIEF (ORB), Binary Robust invariant scalable keypoints (BRISK), and Accelerated-KAZE (AKAZE).

The focus of this paper is to present a study of the performance comparison among these feature extraction methods for low-textural images from real-time steel surface inspection systems. Primarily, synchronized images of steel rolled at room temperatures are obtained from a two-camera network with overlapping regions. Feature descriptor algorithms extract features from two images with an overlapping area and further match the features using K-Nearest Neighbour (KNN) algorithm. The performance of the five feature descriptor algorithms is evaluated using a low-textural dataset that consists of a set of 177 images captured from two cameras placed at a fixed distance from each other. The efficiency of these algorithms is quantitatively and qualitatively evaluated using execution time, sensitivity, and specificity. Finally, this paper provides guidelines for future research on problems with FOV expansion in industrial scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alcantarilla, P.F., Nuevo, J., Bartoli, A.: Fast explicit diffusion for accelerated features in nonlinear scale spaces. British Machine Vision Conference (BMVC) (2013)

    Google Scholar 

  2. Alzohairy, T., El-Dein, E.: Image mosaicing based on neural networks. Int. J. Comput. Appl. 975, 8887 (2016)

    Google Scholar 

  3. Bay, H., Tuytelaars, T., Gool, L.V.: SURF: Speeded-up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Google Scholar 

  4. Bonny, M., Uddin, M.: Feature-based image stitching algorithms. In: IWCI, pp. 198–203 (2016)

    Google Scholar 

  5. Brown, M., Lowe, D.G.: Recognizing panoramas. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 3, p. 1218. Nice, France (2003)

    Google Scholar 

  6. Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. Int. J. Comput. Vis. 74(1), 59–73 (2007)

    Google Scholar 

  7. Chien, H.: When to use what feature? sift, surf, orb, or a-kaze features for monocular visual odometry, pp. 1–6. Palmerston North, IVCNZ (2016)

    Google Scholar 

  8. Colucci, F.: Keep one eye out (2012). http://www.aviationtoday.com/av/issue/cover/Keep-One-Eye-Out_75101.html. Accessed 1 May 2020

  9. Gao, J., Kim, S.J., Brown, M.S.: Constructing image panoramas using dual-homography warping. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 201, pp. 49–56. Colorado Springs, CO, USA (2011)

    Google Scholar 

  10. Gauglitz, S.: Evaluation of interest point detectors and feature descriptors for visual tracking. Int. J. Comput. Vision 94(3), 335–360 (2011)

    Article  MATH  Google Scholar 

  11. Goa, F., Goa, F.: Stitching (2017)

    Google Scholar 

  12. Hoang, V.-D., Tran, D.-P., Nhu, N.G., Pham, T.-A., Pham, V.-H.: Deep feature extraction for panoramic image stitching. In: Nguyen, N.T., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds.) ACIIDS 2020. LNCS (LNAI), vol. 12034, pp. 141–151. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-42058-1_12

    Chapter  Google Scholar 

  13. Horst, R., Negin, M.: Vision system for high-resolution dimensional measurements and on-line SPC: web process application. IEEE Trans. Ind. Appl. 28, 993–997 (1992)

    Article  Google Scholar 

  14. Imaging, S.: Distributed aperture systems. http://www.sarnoffimaging.com/research-and-development/vision-technologies/embeddedvision/distributed-aperture-systems. Accessed 16 May 2020

  15. Jain, R., Kasturi, R., Schunck, B.G.: Machine vision. McGraw-Hill, Inc. (1995)

    Google Scholar 

  16. Jia, J., Tang, K.C.: Eliminating structure and intensity misalignment in image stitching. In: Tenth IEEE International Conference on Computer Vision, ICCV2005, pp. 1651–1658. Beijing, China (2005)

    Google Scholar 

  17. Joshi, K.: Open access a survey on real-time image stitching (2020)

    Google Scholar 

  18. Kaynig, V., Fischer, B., M, B.J.: Probabilistic image registration and anomaly detection by nonlinear warping. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. Anchorage, Alaska, USA (2008)

    Google Scholar 

  19. Khan, N.: Sift and surf performance evaluation against various image deformations on benchmark dataset. In: IEEE International Conference on Digital Image Computing Techniques and Applications, pp. 501–506. DICTA (2011)

    Google Scholar 

  20. Kumar, A.: Computer-vision-based fabric defect detection: a survey. IEEE Trans. Ind. Electron. 55, 348–363 (2008)

    Article  Google Scholar 

  21. Lai, W.S., Gallo, O., Gu, J., Sun, D., Yang, M.H., Kautz, J.: Video stitching for linear camera arrays (2019). arXiv preprint arXiv:1907.13622

  22. Lamkin, M., Ringgenberg, K., Lamkin, J.: Distributed multi - aperture camera array. US 2019/0246044 A1 (2019)

    Google Scholar 

  23. Leutenegger, S., Chli, M., Siegwart, R.: Brisk: Binary robust invariant scalable keypoints. In: International Conference on Computer Vision, pp. 2548–2555 (2011)

    Google Scholar 

  24. Levin, A., Zomet, A., Peleg, S., Weiss, Y.: Seamless image stitching in the gradient domain. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 377–389. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24673-2_31

    Chapter  Google Scholar 

  25. Li, J., Zhao, Y., Ye, W., Yu, K., Ge, S.: Attentive deep stitching and quality assessment for 360 omnidirectional images. IEEE J. Select. Top. Signal Process 14, 209–221 (2019)

    Article  Google Scholar 

  26. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004). http://link.springer.com/10.1023/B:VISI.0000029664.99615.94

  27. Luo, Q., Fang, X., Liu, L., Yang, C., Sun, Y.: Automated visual defect detection for flat steel surface: a survey. IEEE Trans. Instrum. Meas. 69(3), 626–644 (2020)

    Article  Google Scholar 

  28. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  29. Mistry, S., Patel, A.: Image stitching using Harris feature detection. Int. Res. J. Eng. Technol 03(04), 1363–1369, (2016). https://www.irjet.net/

  30. Nayar, S.: Catadioptric omnidirectional camera. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 482–488 (1997)

    Google Scholar 

  31. Peleg, S., Rousso, B., Rav-Acha, A., Zomet, A.: Mosaicing on adaptive manifolds. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1144–1154 (2000)

    Article  Google Scholar 

  32. Pusztai, Z., Hajder, L.: Quantitative comparison of feature matchers implemented in opencv3. Computer Vision Winter Workshop (2016)

    Google Scholar 

  33. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: IEEE International Conference on Computer Vision (ICCV) (2011)

    Google Scholar 

  34. Sanders-Reed, J., Koon, P.: Vision systems for manned and robotic ground vehicles. Proc. SPIE 7692, 1–12 (2010)

    Google Scholar 

  35. Shen, C., Ji, X., Miao, C.: Real-time image stitching with convolutional neural networks. International Conference on Real-time Computing and Robotics (RCAR), pp. 192–197 (2019)

    Google Scholar 

  36. Shi, Z., Li, H., Cao, Q., Ren, H., Fan, B.: An image mosaic method based on convolutional neural network semantic features extraction. J. Signal Process. Syst 92, 435–444 (2020)

    Article  Google Scholar 

  37. Silva, R., Bruno, F., Gomes, P., Frensh, T., Monteiro, D.: Real time 360\(^{\circ }\) video stitching and streaming. In: ACM SIGGRAPH 2016 Posters, pp. 1–2. ACM, Anaheim, California (2016)

    Google Scholar 

  38. Sugimoto, T., Kawaguchi, T.: Development of a surface defect inspection system using radiant light from steel products in a hot rolling line. IEEE Trans. Instrum. Meas. 47, 409–416 (1998)

    Article  Google Scholar 

  39. Szeliski, R.: Image alignment and stitching: a tutorial. Found. Trend® Comput. Graph. Vis. 2(1), 1–104 (2007)

    Google Scholar 

  40. Technologies, R.: Advanced distributed aperture system (adas). http://www.raytheon.com/capabilities/products/adas/. Accessed 6 May 2020

  41. Urban, S., Weinmann, M.: Finding a good feature detector-descriptor combination for the 2D keypoint-based registration of TIS point clouds, pp. 121–128. Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences pp (2015)

    Google Scholar 

  42. Wang, L., Yu, W., Li, B.: Multi-scenes image stitching based on autonomous driving. In: 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), vol. 1, pp. 694–698 (2020)

    Google Scholar 

  43. Yan, M., Yin, Q., Guo, P.: Image stitching with single-hidden layer feedforward neural networks. International Joint Conference on Neural Networks (IJCNN), pp. 4162–4169 (2016)

    Google Scholar 

  44. Zhang, J., Chen, G., Jia, Z.: An image stitching algorithm based on histogram matching and sift algorithm. Int. J. Pattern Recognit Artif Intell. 31(4), 1–14 (2017)

    Article  Google Scholar 

  45. Zomet, A., Levin, A., Peleg, S., Weiss, Y.: Seamless image stitching by minimizing false edges. IEEE Trans. Image Process. 15(4), 969–977 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tata Steel Ltd., Jamshedpur, Jharkhand, India

    Vasanth Subramanyam

  2. National Institute of Technology, Jamshedpur, Jharkhand, India

    Vasanth Subramanyam, Jayendra Kumar & Shiva Nand Singh

  3. Miami College of Henan University, Henan, China

    Roshan Kumar

  4. Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India

    Arvind R. Yadav

Authors
  1. Vasanth Subramanyam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jayendra Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shiva Nand Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Roshan Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Arvind R. Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vasanth Subramanyam .

Editor information

Editors and Affiliations

  1. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Deep Gupta

  2. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Kishor Bhurchandi

  3. Indian Institute of Technology Ropar, Rupnagar, India

    Subrahmanyam Murala

  4. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  5. Indian Institute of Technology Roorkee, Roorkee, India

    Sanjeev Kumar

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Subramanyam, V., Kumar, J., Singh, S.N., Kumar, R., Yadav, A.R. (2023). Low-Textural Image Registration: Comparative Analysis of Feature Descriptors. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-31417-9_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31416-2

  • Online ISBN: 978-3-031-31417-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation