Fast edge integration based active contours for color images

Computers and Electrical Engineering

Volumn 35, Issue 1, 2009, Pages 141-149

  Zheng, Ying ^a   Li, Guangyao ^a   Sun, Xiehua ^b   Zhou, Xinmin ^a  

^a TONGJI UNIVERSITY   (China)

^b China Liliang University   (China)

Author keywords

Active contour; Color image segmentation; Squared local contrast; The GACV model

Indexed keywords

COLOR; DIGITAL IMAGE STORAGE; GEODESY; IMAGE ENHANCEMENT; IMAGE SEGMENTATION; MATHEMATICAL MODELS; NUMERICAL METHODS; OPTICAL PROPERTIES;

ACTIVE CONTOUR; COLOR IMAGE SEGMENTATION; COLOR IMAGES; DIRECTIONAL INFORMATIONS; DRIVING FORCES; EDGE INTEGRATIONS; EDGE LOCATIONS; FAST MARCHING METHODS; GEODESIC ACTIVE CONTOUR MODELS; GEOMETRIC ACTIVE CONTOURS; NARROW BANDS; NUMERICAL SCHEMES; REGION SEGMENTATIONS; SQUARED LOCAL CONTRAST; THE GACV MODEL; UNCONDITIONALLY STABLES; UNIFIED FRAMEWORKS;

IMAGE PROCESSING;

EID: 57849116805     PISSN: 00457906     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compeleceng.2008.09.006     Document Type: Article

References (34)

1. Kass M., Witkin A., and Terzopoulo D. Snakes: active contour models. Int J Comput Vision 29 (1987) 321-331
2. Xu C., and Prince J.L. Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7 (1998) 359-369
3. Osher S.J., and Sethian J.A. Fronts propagation with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phys 79 (1988) 12-49
4. Xu C, Yezzi A, Prince J. On the relationship between parametric and geometric active contours. In: Proceedings of Asilomar conference on signal, systems and computers, vol. 3, 2000. p. 483-9.
5. Caselles V., and Kimmel R. Geodesic active contours. Int J Comput Vision 1 (1997) 61-79
6. Chan T.F., and Vese L.A. Active contours without edges. IEEE Trans Image Process 10 (2001) 266-277
7. Zhu SC, Lee TS, Yuille AL. Region competition: unifying snakes, region growing, energy/Bayes/mdl for multi-band image segmentation. In: Proceedings of fifth international conference of computer vision, vol. 2, 1995. p. 416-23.
8. Zhu S.Y. Comprehensive analysis of edge detection of color image processing. Opt Eng 38 (1999) 612-625
9. Sapiro G. Vector (self) snakes: a geometric framework for color, texture, and multi-scale image segmentation. In: IEEE computer society conference on computer vision and pattern recognition, vol. 1, 1996. p. 817-20.
10. Sapiro G. Color snakes. Comput Vision Image Understand 2 (1997) 243-247
11. Paragios N, Deriche R. Geodesic active regions for texture segmentation, INRIA Research Report, 3440, 1998.
12. Goldenberg R., Kimmel R., Rivlin E., and Rudzsky M. Fast geodesic active contours. IEEE Trans Image Process 10 (2001) 1467-1474
13. Pi L., Shen C.M., Li F., and Fan J.S. A variational formulation for segmenting desired objects in color images. Image Vision Comput 25 (2007) 1414-1421
14. Koepfler G., Lopez C., and Rudin L. A multi-scale algorithm for image segmentation by variational method. SIAM J Numer Anal 31 (1994) 282-299
15. Shah J. Curve evolution and segmentation functionals: application to color images. In: Proceedings of international conference on image processing, vol. 1, 1996. p. 461-4.
16. F Dibos, G Koepfler, Color segmentation using a variational formulation. In: Actes du 16me Colloque GRETSI Grenoble, vol. 9, 1997. p. 367-70.
17. Chan T.F., Sandberg B.Y., and Vese L.A. Active contours without edges for vector-value images. J Visional Commun Image Represent 2 (2002) 130-141
18. Park HK, Kim DY, Chung MJ. Color contour extraction with adaptive color model: color snake. In: Proceedings of international conference on advanced robotics, vol. 8, 2001. p. 229-33.
19. Brook A, Kimmel R, Sochen NA. Geometry motived variational segmentation for color images. In: Proceedings of the third international conference on scale-space and morphology in computer vision, vol. 2106, 2001. p. 362-70.
20. Xie XH, Mirmehdi MJ. Level-set based geometric color snake with region support. In: Proceedings of the IEEE 10th international conference on image processing, vol. 2, 2003. p. 153-5.
21. Brox T., and Weickert J. Level set based image segmentation with multiple regions. Pattern Recogn 3175 (2004) 415-423
22. Chen L., Zhou Y., Wang Y.G., and Yang J. GACV: geodesic-aided C-V method. Pattern Recogn 39 (2006) 1391-1395
23. Pi L., Fan J.S., and Shen C.M. Color image segmentation for objects of interest with modified geodesic active contour method. J Math Imaging Vision 27 (2007) 51-57
24. Yu SY, Zhang Y, Wang Y, Yang J. Color- texture image segmentation by combing region and photometric invariant edge information. In: MCAM 2007, vol. 4577, 2007. p. 286-94.
25. Liu CZ, Ma JL, Ye GY. Medical image segmentation by geodesic active contour incorporating region statistical information. In: Proceedings of 4th international conference on fuzzy systems and knowledge discovery, vol. 3, 2007. p. 63-7.
26. Enright D., Losasso F., and Fedkiw R. A fast and accurate semi-Lagrangian particle level set method. Comput Struct 83 (2005) 479-490
27. Cumani A. Edge detection in multi-spectral images. CVGIP: Graphical Models Image Process 53 (1991) 40-51
28. Naik S.K., and Murthy C.A. Standardization of edge magnitude in color images. IEEE Trans Image Process 15 (2006) 2588-2595
29. Koschan A. A comparative study on color edge detection. In: Proceedings of 2nd Asian conference on computer vision (ACCV'95), vol. 3, 1995. p. 574-9.
30. Zenzo S.D. A note on the gradient of multi image. Comput Vision, Graphics, Image Process 33 (1986) 211-218
31. Vasilevskiy A, Siddiqi K. Flux maximizing geometric flows. In: International conference on computer vision, vol. 1, 2001. p. 149-154.
32. Wricker J., Romeny T.H., and Viergever M.A. Efficient and reliable scheme for nonlinear diffusion filtering. IEEE Trans Image Process 7 (1998) 398-410
33. Cohen L.D., and Kimmel R. Fast marching the global minimum of active contours. Image Process 1 (1996) 16-19
34. Virginie D. Level set methods and fast marching methods. Eur J Mech 19 (2000) 531-532