Skip to main content
SpringerLink
Log in

Image feature detection algorithm based on the spread of Hessian source

  • Special Issue Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

Image feature detection can be obtained from many methods including the feature point detection. This paper adopts the image feature point detection method based on second-order characteristics of point and the image feature detection algorithm based on the Hessian matrix to detect more feature points. By combining the gray-scale-based image-matching technology with the feature-based image feature detection technology, we propose a Hessian algorithm to obtain more matching points, which can search for matching more quickly. The proposed algorithm overcomes the traditional matching methods that have Ergodic properties of the search strategy. Experiments demonstrate the speed and accuracy of the proposed algorithm, and we use the correct detected feature points to realize image registration, image fusion and image stitching.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Koenderink, J.: The structure of images. Biol. Cybern. 50(5), 363–370 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  2. Ng, J., Clay, S.T., Barman, S.A., et al.: Maximum likelihood estimation of vessel parameters from scale space analysis. Image Vis. Comput. 28(1), 55–63 (2010)

    Article  Google Scholar 

  3. Marimon, D., Bonnin, A., Adamek, T., et al.: DARTs: Efficient scale-space extraction of DAISY keypoints, 2010 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2416–2423 (2010)

  4. Badura, S., Foltán, S.: Advanced scale-space, invariant, low detailed feature recognition from images-car brand recognition, 2010 International Multiconference on Computer Science and Information Technology, pp. 19–23 (2010)

  5. Chen, B.F., Cai, Z.X.: Harris corner detection based on theory of scale-space. Zhongnan Daxue Xuebao (Ziran Kexue Ban)/J Central South Univ (Sci Technol) 36(5), 751–754 (2005)

    Google Scholar 

  6. Wang, Y.X., Zhang, Y.J., Wang, X.H.: Mean-shift object tracking through 4-D scale space. J Electron Inf Technol 32(7), 1626–1632 (2010)

    Google Scholar 

  7. Hessian introduction and destructions. http://blog.csdn.net/forevercnet/article/details/6951870, 2011-11-09

  8. Weickert, J.: Scale-space properties of nonlinear diffusion filtering with a diffusion tensor, Report No. 110, Laboratory of Technomathematics, University Of Kaiserslautern, Germany, pp. 1–29 (1994)

  9. Barrera, F., Lumbreras, F., Sappa, A.D.: Multimodal template matching based on gradient and mutual information using scale-space, 17th IEEE International Conference on Image Processing, pp. 2749–2752 (2010)

  10. Romeny, B.M.T.H., Florack, L.: A multiscale geometric model of human vision. The perception of visual information, pp. 87–126. Springer, New York (1997)

    Book  Google Scholar 

  11. Tapu, R., Zaharia,T., Preteux, F.: A scale-space filtering-based shot detection algorithm, 2010 IEEE 26th Convention of Electrical and Electronics Engineers in Israel, pp. 919–923 (2010)

  12. Lixin, J., Huini, W., Yulong, C.: Research on Super Resolution Reconstruction Based on Scale Space, 2010 International Forum on Information Technology and Applications, pp. 92–95 (2010)

  13. Burger, M., Gilboa, G., Osher, S., et al.: Nonlinear inverse scale space methods. Commun Math Sci 4(1), 179–212 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  14. Lakemond, R., Fookes, C., Sridharan, S.: Affine adaptation of local image features using the Hessian matrix, Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 496–501 (2009)

  15. Ming, L.: Research On Algorithm Of Head Detection Based On Curvature Scale Space, Hunan University, pp. 112–113 (2010)

  16. Kapsalas , P., Kollias, S.: Shape-stable region boundary extraction via affine morphological scale space, International conference on Multimedia, pp. 1215–1218 (2010)

  17. Liu, L.: Research on Image Feature Detection Method Based on Heat Source Scale Space Theory, Xiamen University, pp. 101–103 (2011)

  18. Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vision 60(1), 63–86 (2004)

    Article  Google Scholar 

  19. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

    Article  Google Scholar 

  20. Liu, X., Wang, C., Yao, H., Zhang, L.: The scale of edges, IEEE Conference on Computer Vision and Pattern Recognition, pp. 462–269 (2012)

  21. Zhang, L., Han, Y., Yang, Y., Song, M., Yan, S., Tian, Q.: Discovering discriminative graphlets for aerial image categories recognition. IEEE Trans. Image Process. 22(12), 5071–5084 (2013)

    Article  MathSciNet  Google Scholar 

  22. Zhang, L., Yang, Y., Gao, Y., Yu, Y., Wang, C., Li, X.: A probabilistic associative model for segmenting weakly supervised images. IEEE Trans. Image Process. 23(9), 4150–4159 (2014)

    Article  MathSciNet  Google Scholar 

  23. Zhang, L., Gao, Y., Xia, Y., Dai, Q., Li, X.: A fine-grained image categorization system by cellet-encoded spatial pyramid modeling, IEEE Transactions on Industrial Electronics, accepted (2014)

  24. Zhang, L., Gao, Y., Hong, C., Feng, Y., Zhu, J., Cai, D.: Feature correlation hypergraph: exploiting high-order potentials for multimodal recognition. IEEE Trans. Cybern. 44(8), 1408–1419 (2014)

    Article  Google Scholar 

  25. Zhang, L., Song, M., Liu, X., Sun, L., Chen, C., Bu, J.: Recognizing architecture styles by hierarchical sparse coding of blocklets. Inf. Sci. 254, 141–154 (2014)

    Article  Google Scholar 

  26. Zhang, L., Song, M., Liu, X., Bu, J., Chen, C.: Fast multi-view segment graph kernel for object classification. Sig. Process. 93(6), 1597–1607 (2013)

    Article  Google Scholar 

  27. Zhang, L., Gao, Y., Xia, Y., Lu, K., Shen, J., Ji, R.: Representative discovery of structure cues for weakly-supervised image segmentation. IEEE Trans. Multimedia 16(2), 470–479 (2014)

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Editor and anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. This work is partially supported by the National Natural Science Foundation of China under Grant No. 61373147 and the Natural Science Foundation of Fujian Province under Grant No. 2010J01353 and the Project of Xiamen Science and Technology Program under Grant No. 3502Z20133041.

Author information

Authors and Affiliations

  1. School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, 361024, China

    Zhu Shunzhi, Liu Lizhao & Chen Si

Authors
  1. Zhu Shunzhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liu Lizhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chen Si
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhu Shunzhi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shunzhi, Z., Lizhao, L. & Si, C. Image feature detection algorithm based on the spread of Hessian source. Multimedia Systems 23, 105–117 (2017). https://doi.org/10.1007/s00530-015-0453-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-015-0453-x

Keywords

Search

Navigation