Skip to main content
Article thumbnail
Location of Repository

An extension of min/max flow framework

By Hongchuan Yu, Mohammed Bennamoun and Chin-seng Chua


In this paper, the min/max flow scheme for image restoration is revised. The novelty consists of the fol-\ud 24 lowing three parts. The first is to analyze the reason of the speckle generation and then to modify the\ud 25 original scheme. The second is to point out that the continued application of this scheme cannot result\ud 26 in an adaptive stopping of the curvature flow. This is followed by modifications of the original scheme\ud 27 through the introduction of the Gradient Vector Flow (GVF) field and the zero-crossing detector, so as\ud 28 to control the smoothing effect. Our experimental results with image restoration show that the proposed\ud 29 schemes can reach a steady state solution while preserving the essential structures of objects. The third is\ud 30 to extend the min/max flow scheme to deal with the boundary leaking problem, which is indeed an\ud 31 intrinsic shortcoming of the familiar geodesic active contour model. The min/max flow framework pro-\ud 32 vides us with an effective way to approximate the optimal solution. From an implementation point of\ud 33 view, this extended scheme makes the speed function simpler and more flexible. The experimental\ud 34 results of segmentation and region tracking show that the boundary leaking problem can be effectively\ud 35 suppressed

Topics: csi
Year: 2009
OAI identifier:

Suggested articles


  1. A computational approach to edge detection, doi
  2. A multiphase level set framework for image segmentation doi
  3. A novel technique for unsupervised doi
  4. A unified approach to noise removal, image doi
  5. Active contours without edges, doi
  6. active contours,
  7. Area and length minimizing flows doi
  8. Axioms and fundamental doi
  9. Bayes/MDL for multiband image segmentation, doi
  10. Detection and location of moving objects doi
  11. detection and tracking of moving objects, doi
  12. (1992). detection by nonlinear diffusion (II), doi
  13. (2008). Disk Used ARTICLE IN PRESS Please cite this article in press as: H. Yu et al., An extension of min/max flow framework, Image Vis. Comput.
  14. (2008). Disk Used ARTICLE IN PRESSUN CO RR ECPlease cite this article in press as: H. Yu et al., An extension of min j.imavis.2008.05.006E/max flow framework, Image Vis. Comput.
  15. (1993). equations of image processing, doi
  16. (2008). et al./Image and Vision Computing xxx
  17. evolution and flow fields, in: doi
  18. Feature-oriented image enhancement using shock filter, doi
  19. for adaptive image enhancement and denoising, doi
  20. (1998). for shape segmentation, doi
  21. Forward-and-backward diffusion processes doi
  22. Geodesic active contours and level sets for the doi
  23. Geodesic active contours, doi
  24. Gradient vector flow fast geodesic doi
  25. High-order total variation-based image doi
  26. (2008). Image and Vision Computing xxx doi
  27. (1996). Image Process.
  28. Image segmentation using curve doi
  29. Image selective smoothing and edge doi
  30. Noise removal using smoothed normals and doi
  31. of material mixtures in MR volume data using voxel histograms, doi
  32. Partial-volume Bayesian classification doi
  33. Region competition: unifying snakes, region growing, and doi
  34. Region tracking via level set PDE’s without motion computation, doi
  35. Regularized Laplacian zero crossings as optimal
  36. Regularized shock filters and complex doi
  37. Scale–space and edge detection using anisotropic diffusion, doi
  38. (1989). Shortening embedded curves, doi
  39. Snakes, shapes, and gradient vector flow, doi
  40. Surface evolution under curvature flows, doi
  41. texture segmentation, in: doi
  42. using deterministic relaxation algorithms, in: doi
  43. (2002). using the Mumford and Shah model, doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.