Medical equipments high precise detection technology basing on morphology-harris operator
Yang-Yang Mei, Hai-Ming Xie, Lu Han, Shi-Jun Guo
.
DOI: 10.4236/jbise.2010.35075   PDF    HTML     4,682 Downloads   8,522 Views  

Abstract

Medical equipments related to life safety of human, it is important to detect by a high precise method. Image mosaic which based on Harris corner operator is a commonly used method in this area; Harris operator has low calculation burden, it is simple and stable, so it is more effective comparing with other feature point extracted operators. But in this algorithm, corner points can only be detected in a single-scale, there may be losing information of corner points, causing corner point location offset, extracting false corner points because of noise. In order to solve this question, the acquired images should be processed by dilation and erosion operation firstly, then do image mosaic. Results show that image noise can be eliminated effectively after those morphological processes, as well as the false positive noise generated by image glitch. The success rate of image mosaic and detection accuracy can be greatly improved through the Morphology-Harris operator. Measurement of precision instruments which based on this new method will improve the measurement accuracy, and the research in this area will promote the further development of machine vision technology.

Share and Cite:

Mei, Y. , Xie, H. , Han, L. and Guo, S. (2010) Medical equipments high precise detection technology basing on morphology-harris operator. Journal of Biomedical Science and Engineering, 3, 538-542. doi: 10.4236/jbise.2010.35075.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Szeliski, R. (1996) Video mosaics for virtual environments. IEEE Computer Graphics and Applications, 16(2), 22-30.
[2] Peleg, S. and Rousso, B. (2000) Mosaicing on adaptive manifolds. IEEE Transactions on PAMI, 22(10), 1144-1154.
[3] Brown, M. and Lowe, D.G. (2003) Recognising panoramas. Proceedings of IEEE International Conference on Computer Vision, 2(1), 1218-1225.
[4] Mikolajczyk, K. and Schmid, C. (2004) Scale & Affine invariant interest point detectors. International Journal of Computer Vision, 60(1), 63-86.
[5] Xie, D.H. and Zhan, Z.Q. (2003) Improving Harris corner detector. Journal of Geomatics, 28(2), 22-23.
[6] Harris, C. and Stephens, M.J. (1988) A combined corner and edge detector. Proceedings Fourth Alvey Vision Con- ference, Manchester, 1988, 147-151.
[7] Qian, W., Fu, Z.Z. and Liu, Q.Q. (2008) Voting- strategy-based approach to image registration. Opto-Electronic Engineering, 35(10), 86-91.
[8] Zhao, W.J., Gong, S.R. and Liu, C.P. (2008) Adaptive Harris corner detection algorithm. Computer Engineering, 34(10), 212-214.
[9] Zhang, J.F. and Xing, X. (2008) Static image registration research based on Harris corner. Science and technology information, 20, 9-10.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.