Skip to main content
Log in

Geometric Analysis of Particle Motion in a Vector Image Field

  • Published:
Journal of Mathematical Imaging and Vision Aims and scope Submit manuscript

Abstract

This paper proposes a geometrical model for the Particle Motion in a Vector Image Field (PMVIF) method. The model introduces a c-evolute to approximate the edge curve in the gray-level image. The c-evolute concept has three major novelties: (1) The locus of Particle Motion in a Vector Image Field (PMVIF) is a c-evolute of image edge curve; (2) A geometrical interpretation is given to the setting of the parameters for the method based on the PMVIF; (3) The gap between the image edge’s critical property and the particle motion equations appeared in PMVIF is padded. Our experimental simulation based on the image gradient field is simple in computing and robust, and can perform well even in situations where high curvature exists.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. I. Pollak, A. Willsky and H. Krim, “Image Segmentation and Edge Enhancement with Stabilized Inverse Diffusion Equations,” IEEE Transactions on Image Processing, Vol. 9, pp. 256–266, 2000.

    Article  MATH  Google Scholar 

  2. S. Teboul, L. Feraud, and G. Aubert, “Variational Approach for Edge-Preserving Regularization Using Coupled PDE’s,” IEEE Transactions on Image Processing, Vol. 7, pp. 387–397, 1998.

    Article  Google Scholar 

  3. P. Perona and J. Malik, “Scale-Space and Edge Detection Using Anisotropic Diffusion,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 12, No. 7, pp. 629–639, July 1990.

    Article  Google Scholar 

  4. R. Malladi and J. Sethian, “A Unified Approach to Noise Removal, Image Enhancement, and Shape Recovery,” IEEE Transactions on Image Processing, Vol. 5, No. 11, pp. 1554–1568, Nov. 1996.

    Article  Google Scholar 

  5. D. Chung and G. Sapiro, “Segmenting Skin Lesions with Partial-Differential-Equations-Based Image Processing Algorithms,” IEEE Transactions on Medical Imaging, Vol. 19, no 7, pp. 763–767, July 2000.

    Article  Google Scholar 

  6. S. Zhu and A. Yuille, “Region Competition: Unifying Shakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 9, pp. 884–900, Sep.1996.

    Article  Google Scholar 

  7. D. Mumford and J. Shah, “Optimal Approximations by Piecewise Smooth Functions and Associated Variational Problems,” Communication on Pure and Applied Mathematics, Vol. 42, pp. 577–685, 1989.

    MathSciNet  MATH  Google Scholar 

  8. D. Noll, “Variational Methods in Image Restoration,” in Proceedings of the 8th French-German Conference in Optimization, Trier, Sept. 1996.

  9. G. Aubert and L. Vese, “A Variational Method in Image Recovery,” SIAM J. Numeral Analysis, Vol. 34, No. 5, pp. 1948–1979, 1997.

    Article  MathSciNet  MATH  Google Scholar 

  10. D. Geiger, A. Gupta, and L. Costa, “Dynamic programming for detecting, tracking, and matching deformable contour”, IEEE Trans. PAMI, Vol. 17, pp. 294–302, 1995.

    Google Scholar 

  11. C. Xu and J. L. Prince, “Snakes, Shapes, and Gradient Vector Flow,” IEEE Transactions on Image Processing, Vol. 7, No. 3, pp. 359–369, March 1998.

    Article  MathSciNet  MATH  Google Scholar 

  12. T. F. Chan and L. A. Vese, “Active Contours Without Edges,” IEEE Transactions on Image Processing, Vol. 10, No. 2, pp. 266–277, Feb. 2001.

    Article  MATH  Google Scholar 

  13. M. Kass, A. Witkin and D. Terzopoulos, “Snakes: Active Contour Models,” Int. J. Computer Vision, Vol. 1, pp. 321–331, 1988.

    Article  Google Scholar 

  14. R. Malladi, J. A. Sethian, and B. C. Vemuri, “Shape Modeling with Front Propagation: A Level Set Approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 2, pp. 158–175, Feb. 1995.

    Article  Google Scholar 

  15. V. Caselles, F. Catte, and T. Coll etc., “A Geometric Model for Active Contours in Image Processing,” Numer. Math., Vol. 66, pp. 1–31, 1993.

  16. L. D. Cohen and I. Cohen, “Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 11, pp. 1131–1147, Nov. 1993.

    Article  Google Scholar 

  17. C. Xu, A. Yezzi, and J. L. Prince, “On the Relation between Parametric and Geometric Active Contours,” Technical Report JHU/ECE, pp. 99–14, December 3, 1999.

  18. V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic Active Contours”, Int. J. Computer Vision, Vol. 22, No. 1, pp. 61–79, 1997.

    Article  MATH  Google Scholar 

  19. C. Xu and J. L. Prince, “Generalized Gradient Vector Flow External Force for Active Contours,” Signal Processing, vol. 71, pp. 131–139, 1998.

    Article  MATH  Google Scholar 

  20. K. Siddiqi, Y. Lauziere, and A. Tannenbaum, “Area and Length Minimizing Flows for Shape Segmentation,” IEEE Transactions on Image Processing, Vol. 7, No. 3, pp. 433–443, March 1998.

    Article  Google Scholar 

  21. T. Cootes, C. Taylor, D. Cooper, and J. Graham, “Active Shape Models—Their Training and Application,” Computer Vision and Image Understanding, Vol. 61, No. 1, pp. 38–59, Jan. 1995.

    Article  Google Scholar 

  22. Y. Wang and L. Staib, “Boundary Finding with Prior Shape and Smoothness Models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 7, pp: 738–743, July 2000.

    Article  Google Scholar 

  23. W. Ma and B. Manjunath, “EdgeFlow: A technique for Boundary Detection and Image Segmentation,” IEEE Transactions on Image Processing, Vol. 9, No. 8 pp. 1375–1388, Aug.2000.

  24. M. Tabb and N. Ahuja, “Multiscale Image Segmentation by Integrated Edge and Region Detection,” IEEE Transactions on Image Processing, Vol. 6, No. 5, pp. 642–655, May 1997.

    Article  Google Scholar 

  25. N. Anant and L. Udpa, “Boundary Detection Using Simulation of Partial Motion in Vector Image Field,” IEEE Transactions on Image Processing, Vol. 8, No. 11, pp. 1560–1571, Nov. 1999.

    Article  Google Scholar 

  26. K. Haris, S. Efstratiadis, and N. Maglaveras, “Hybrid Image Segmentation Using Watersheds and Fast Region Merging”, IEEE Transactions on Image Processing, Vol. 7, No. 12, pp. 1684–1699, Dec. 1998.

  27. K. Cho and P. Meer, “Image Segmentation from Consensus Information,” Computer Vision and Image Understanding, Vol. 68, No. 1, pp. 72–89, Oct. 1997.

    Article  Google Scholar 

  28. C. Leung and F. Lam, “Maximum Segmented Image Information Thresholding,” Graphical Models Image Processing, Vol. 60, No. 1, pp. 57–76, Jan. 1998.

    Article  Google Scholar 

  29. A. Koster, K. Vinceken, and C. Graaf, “Heuristic Linking Models in Multiscale Image Segmentation,” Computer Vision and Image Processing, Vol. 65, No. 3, pp. 382–402, 1997.

    Article  Google Scholar 

  30. S. Kwok and A. Constantinides, “A Fast Recursive Shortest Spanning Tree for Image Segmentation and Edge Detection,” IEEE Transactions on Image Processing, Vol. 6, No. 2, pp. 328–332, Feb. 1997.

    Article  Google Scholar 

  31. R. Henkel, “Segmentation in Scale Space,” in Proceedings of the 6th Int. Conf. On Computer Analysis of Images and Pattern, CAIP’ 95, Prague, 1995.

  32. R. Henkel, “Segmentation with Synchronizing Neural Oscillators”, ZKW-Report, 04/94, Center for Cognitive Science, Bremen, 1994.

  33. M. Michel, Variational Methods in Image Segmentation: with seven image processing experiments, Boston, Mass.: Birkhhauser, 1995.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zheru Chi.

Additional information

Chenggang Lu received his Bachelor of Science and PhD degrees from Zhejiang University in 1996 and 2003, respectively. Since 2003, he has been with VIA Software (Hang Zhou), Inc. and Huawei Technology, Inc. His research interests include image processing, acoustic signaling processing, and communication engineering.

Zheru Chi received his BEng and MEng degrees from Zhejiang University in 1982 and 1985 respectively, and his PhD degree from the University of Sydney in March 1994, all in electrical engineering. Between 1985 and 1989, he was on the Faculty of the Department of Scientific Instruments at Zhejiang University. He worked as a Senior Research Assistant/Research Fellow in the Laboratory for Imaging Science and Engineering at the University of Sydney from April 1993 to January 1995. Since February 1995, he has been with the Hong Kong Polytechnic University, where he is now an Associate Professor in the Department of Electronic and Information Engineering. Since 1997, he has served on the organization or program committees for a number of international conferences. His research interests include image processing, pattern recognition, and computational intelligence. Dr. Chi has authored/co-authored one book and nine book chapters, and published more than 140 technical papers.

Gang Chen received his Bachelor of Science degree from Anqing Teachers College in 1983 and his PhD degree in the Department of Applied Mathematics at Zhejiang University in 1994. Between 1994 and 1996, he was a postdoctoral researcher in electrical engineering at Zhejiang University. From 1997 to 1999, he was a visiting researcher in the Institute of Mathematics at the Chinese University of Hong Kong and the Department of Electronic and Information Engineering at The Hong Kong Polytechnic University. Since 2001, he has been a Professor at Zhejiang University. He has been the Director of the Institute of DSP and Software Techniques at Ningbo University since 2002. His research interests include applied mathematics, image processing, fractal geometry, wavelet analysis and computer graphics. Prof. Chen has co-authored one book, co-edited five technical proceedings and published more than 80 technical papers.

(David) Dagan Feng received his ME in Electrical Engineering & Computing Science (EECS) from Shanghai JiaoTong University in 1982, MSc in Biocybernetics and Ph.D in Computer Science from the University of California, Los Angeles (UCLA) in 1985 and 1988 respectively. After briefly working as Assistant Professor at the University of California, Riverside, he joined the University of Sydney at the end of 1988, as Lecturer, Senior Lecturer, Reader, Professor and Head of Department of Computer Science/School of Information Technologies, and Associate Dean of Faculty of Science. He is Chair-Professor of Information Technology, Hong Kong Polytechnic University; Honorary Research Consultant, Royal Prince Alfred Hospital, the largest hospital in Australia; Advisory Professor, Shanghai JiaoTong University; Guest Professor, Northwestern Polytechnic University, Northeastern University and Tsinghua University. His research area is Biomedical & Multimedia Information Technology (BMIT). He is the Founder and Director of the BMIT Research Group. He has published over 400 scholarly research papers, pioneered several new research directions, made a number of landmark contributions in his field with significant scientific impact and social benefit, and received the Crump Prize for Excellence in Medical Engineering from USA. More importantly, however, is that many of his research results have been translated into solutions to real-life problems and have made tremendous improvements to the quality of life worldwide. He is a Fellow of ACS, HKIE, IEE, IEEE, and ATSE, Special Area Editor of IEEE Transactions on Information Technology in Biomedicine, and is the current Chairman of IFAC-TC-BIOMED.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lu, C., Chi, Z., Chen, G. et al. Geometric Analysis of Particle Motion in a Vector Image Field. J Math Imaging Vis 26, 301–307 (2006). https://doi.org/10.1007/s10851-006-9002-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10851-006-9002-8

Keywords

Navigation