LBP-guided active contours

Pattern Recognition Letters

Volumn 29, Issue 9, 2008, Pages 1404-1415

  Savelonas, Michalis A ^a   Iakovidis, Dimitris K ^a   Maroulis, Dimitris ^a  

^a UNIVERSITY OF ATHENS   (Greece)

Author keywords

Active contours; Local binary patterns; Texture segmentation

Indexed keywords

BINARY SEQUENCES; IMAGE SEGMENTATION; MATHEMATICAL MODELS; TEXTURES; VECTORS;

ACTIVE CONTOURS; LOCAL BINARY PATTERNS; TEXTURE SEGMENTATION;

PATTERN RECOGNITION;

EID: 43349095374     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2008.02.013     Document Type: Article

References (53)

1. Acharyya M., and Kundu M.K. An adaptive approach to unsupervised texture segmentation using M-band wavelet transform. Sig. Proc. 81 (2001) 1337-1356
2. Akgul Y.S., and Kambhamettu C. A coarse-to-fine deformable contour optimization framework. IEEE Trans. Pattern Anal. Machine Intell. 25 2 (2003) 174-186
3. Allili, M.S., Ziou, D., Bentabet, L., 2004. A robust level set approach for image segmentation and statistical modeling. In: Proc. Adv. Conc. on Intelligent Vision Systems (ACIVS), pp. 243-251.
4. Aubert G., and Vese L. A variational method in image recovery. SIAM J. Numer. Anal. 34 5 (1997) 1948-1979
5. Aujol J.F., and Chan T.F. Combining geometrical and textured information to perform image classification. J. Vision Comm. Image Represent. 17 5 (2006) 1004-1023
6. Aujol J.F., Aubert G., and Blanc-Feraud L. Wavelet-based level set evolution for classification of textured images. IEEE Trans. Image Process. 12 12 (2003) 1634-1641
7. Awate, S.P., Tasdizen, T., Whitaker, R.T., 2006. Unsupervised texture segmentation with nonparametric neighborhood statistics. In: Proc. Eur. Conf. on Computer Vision (ECCV), pp. 494-507.
8. Brodatz P. Textures: A Photographic Album for Artists and Designers (1996), Dover, New York, NY
9. Caselles V., Kimmel R., and Sapiro G. Geodesic active contours. Internat J. Comput. Vision 22 (1997) 61-79
10. Chan T.F., and Vese L.A. Active contours without edges. IEEE Trans. Image Process. 7 (2001) 266-277
11. Chan T., Sandberg B., and Vese L. Active contours without edges for vector-valued images. J. Vision Comm. Image Represent. 11 (2002) 130-141
12. Deng Y., and Manjunath B.S. Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Anal. Machine Intell. 23 8 (2001) 800-810
13. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification. second ed. (2001), Wiley
14. Feng, X., Pietikäinen, M., Hadid, A., Xie, H., 2005. A novel real time system for facial expression recognition. In: Proc. Aff. Comput. on Intelligent Interaction. Lecture Notes in Computer Sciences, vol. 3784, pp. 248-256.
15. He Y., Luo Y., and Hu D. Unsupervised texture segmentation via applying geodesic active regions to Gaborian feature space. IEEE Trans. Eng. Comput. Technol. (2004) 272-275
16. Hermes L., and Buhmann J.M. A minimum entropy approach to adaptive image polygonization. IEEE Trans. Image Process. 12 10 (2003) 1243-1258
17. Huang, X., Metaxas, D., Chen, T., 2004. Metamorphs: deformable shape and texture models. In: Proc. IEEE Internat. Conf. on Computer Vision and Pattern Recognition (ICVPR), pp. 1496-1503.
18. Kass M., Witkin A., and Terzopoulos D. Snakes: active contour models. Internat J. Comput. Vision 1 (1988) 321-331
19. Kimmel R. Geometric Level Set Methods in Imaging, Vision and Graphics (2003), Springer-Verlag
20. Lee S.H., and Seo J.K. Level set-based bimodal segmentation with stationary global minimum. IEEE Trans. Image Process. 15 9 (2006) 2843-2852
21. Lee, S.M., Abott, A.L., Clark, N.A., Araman, P.A., 2005. Active contours on statistical manifolds and texture segmentation. In: Proc. IEEE Internat. Conf. on Image Processing (ICIP), vol. 3, pp. 828-831.
22. Lehmann, T., Bredno, J., Spitzer, K., 2001. Texture-adaptive active contour models. In: Proc. Internat. Conf. on Advanced Pattern Recognition, vol. 2013, pp. 387-396.
23. Li J., and Wang J.Z. Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans. Pattern Anal. Machine Intell. 25 9 (2003) 1075-1088
24. Liapis S., Sifakis E., and Tziritas G. Colour and texture segmentation using wavelet frame analysis, deterministic relaxation, and fast marching algorithms. J. Vision Comm. Image Represent. 15 (2004) 1-26
25. Lucieer A., and Stein A. Texture-based landform segmentation of LiDAR imagery. Internat J. Appl. Earth Obs. Geoinf. 6 (2005) 261-270
26. Mäenpää, T., 2003. The Local Binary Pattern Approach to Texture Analysis-Extensions and Applications. Ph.D. Dissertation, Department of Electrical and Information Engineering, Oulou University, Finland.
27. Mäenpää T., and Pietikäinen M. Classification with color and texture: jointly or separately?. Pattern Recognition 37 8 (2004) 1629-1640
28. Mäenpää T., Viertola J., and Pietikäinen M. Optimising colour and texture features for real-time visual inspection. Pattern Anal. Appl. 6 3 (2003) 169-175
29. Martin, D., Fowlkes, C., Malik, J., 2001. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. Internat. Conf. on Computer Vision (ICCV), pp. 416-425.
30. Mumford D., and Shah J. Optimal approximation by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math. 42 (1989) 577-685
31. Ojala T., and Pietikäinen M. Unsupervised texture segmentation using feature distributions. Pattern Recognition 32 3 (1999) 477-486
32. Ojala T., Pietikäinen M., and Harwood D. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29 (1996) 51-59
33. Ojala T., Pietikäinen M., and Mäenpää T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Machine Intell. 24 7 (2002) 971-987
34. Osher S., and Sethian J. Fronts propagating with curvature-dependent speed: algorithms based on the Hamilton-Jacobi formulations. J. Comp. Physiol. 79 (1988) 12-49
35. Paclic, P., Duin, R., Kempen, G.V., Kohlus, R., 2002. Supervised segmentation of textures in backscatter images. In: Proc. IEEE Internat. Conf. on Pattern Recognition (ICPR), vol. 2, pp. 490-493.
36. Paragios, N., 2000. Geodesic active regions and level set methods contributions and applications in artificial vision. Ph.D. Dissertation, School of Computer Engineering, University of Nice, Sophia, Antipolis, France.
37. Paragios, N., Deriche, R., 1999. Geodesic active contours for supervised texture segmentation. In: Proc. IEEE Internat. Conf. on Computer Vision and Pattern Recognition, pp. 2422-2427.
38. Pichler O., Teuner A., and Hosticka B.J. A comparison of texture feature extraction using adaptive gabor filtering, pyramidal and structured wavelet transforms. Pattern Recognition 29 5 (1996) 733-742
39. Pujol O., and Radeva P. Texture segmentation by statistic deformable models. Internat J. Image Graph. 4 3 (2004) 433-452
40. Qing X. Texture segmentation using LBP embedded region competition. El. Lett. Comp. Vision Image Anal. 5 1 (2005) 41-47
41. Randen T., and Husøy J.H. Filtering for texture classification: a comparative study. IEEE Trans. Pattern Anal. Machine Intell. 21 4 (1999) 291-310
42. Rousson, M., Brox, T., Deriche, R., 2003. Active unsupervised texture segmentation on a diffusion based feature space. In: Proc. IEEE Internat. Conf. on Computer Vision and Pattern Recognition, Madison, Wisconsin, USA.
43. Sagiv C., Sochen N.A., and Zeevi Y. Integrated active contours for texture segmentation. IEEE Trans. Image Process. 1 1 (2004) 1-19
44. Sandberg B., and Chan T.F. A logic framework for active contours on multi-channel images. J. Vision Comm. Image Represent. 16 (2005) 333-358
45. Sandberg, B., Chan, T., Vese, L. 2002. A level-set and gabor-based active contour algorithm for segmenting textured images. In: Technical Report 39, Math. Department UCLA, Los Angeles, USA.
46. Sochen N., Kimmel R., and Malladi R. A general framework for low level vision. IEEE Trans. Image Process. 7 (1998) 310-318
47. Suri J.S., Liu K., Laxminarayan S.N., Zeng X., and L Reden. Shape recovery algorithms using level sets in 2-D/3-D medical imagery: A state-of-the-art review. IEEE Trans. Inform. Technol. Biomed. 6 1 (2002) 8-28
48. Theodoridis S., and Koutroumbas K. Pattern Recognition. third ed. (2006), Academic Press
49. Unser M., and Eden M. Nonlinear operators for improving texture segmentation based on features extracted by spatial filtering. IEEE Trans. Systems Man Cybernet. 20 4 (1990) 804-815
50. Vese L.A., and Chan T.F. A multiphase level set framework for image segmentation using the mumford and shah model. Internat J. Comput. Vision 50 3 (2002) 271-293
51. MIT Media Lab., 1995. Vision Texture Database. Available from: .
52. Xu C., and Prince J.L. Generalized gradient vector flow external forces for active contours. Sig. Proc. 71 (1998) 131-139
53. Zhang H., Gao W., Chen X., and Zhao D. Detection using spatial histogram features. Image Vision Comput. 24 (2006) 327-341