Class-specific, top-down segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2351, Issue , 2003, Pages 109-122

  Borenstein, Eran ^a   Ullman, Shimon ^a  

^a WEIZMANN INSTITUTE OF SCIENCE   (Israel)

Author keywords

Figure ground; Grouping and segmentation; Object classification; Top down processing

Indexed keywords

COMPUTER VISION; TEXTURES;

FIGURE GROUND; GENERAL CLASS; GREY LEVELS; OBJECT CLASSIFICATION; SEGMENTATION METHODS; SEGMENTATION RESULTS; TOPDOWN; VARYING BACKGROUND;

IMAGE SEGMENTATION;

EID: 84948984184     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47967-8_8     Document Type: Conference Paper

References (18)

1. K. Cho and P. Meer. Image segmentation from consensus information. Computer Vision and Image Understanding: CVIU, 68(1):72–89, 1997.
2. J.M.H. du Buf, M. Kardan, and M. Spann. Texture feature performance for image segmentation. Pattern Recognition, 23:291–309, 1990.
3. R. Duda, P. Hart, and D. Stork. Pattern classification, 2001.
4. A.K. Jain and F. Farrokhnia. Unsupervised texture segmentation using gabor filters. Pattern Recognition, 24:1167–1186, 1991.
5. M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. International Journal of Computer Vision, 1:321–331, 1987.
6. T. Leung and J. Malik. Contour continuity in region based image segmentation. In Fifth Euro. Conf. Computer Vision, Freiburg, Germany, 1998.
7. D. Mumford and J. Shah. Boundary detection by minimizing functionals. In IEEE Conf. on Computer Vision and Pattern Recognition, San Francisco, 1985.
8. A. Needham. Object recognition and object segregation in 4.5-month-old infants. Journal of Experimental Child Psychology, 78:3–24, 2001.
9. A. Needham and R. Baillargeon. Effects of prior experience in 4.5-month-old infants’ object segregation. Infant Behaviour and Development, 21:1–24, 1998.
10. M.A. Peterson. Object recognition processes can and do operate before figureground organization. Current Directions in Psychological Science, 3:105–111, 1994.
11. M.A. Peterson and B.S. Gibson. Shape recognition contributions to figure-ground organization in three-dimensional displays. Cognitive Psychology, 25:383–429, 1993.
12. M. Pietikainen, A. Rosenfeld, and I. Walter. Split and link algorithms for image segmentation. Pattern Recognition, 15(4):287–298, 1982.
13. E. Sali and S. Ullman. Combining class-specific fragments for object classification. In Proc. 10th British Machine Vision Conference, volume 1, pages 203–213, 1999.
14. E. Sharon, A. Brandt, and R. Basri. Fast multiscale image segmentation. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pages 70–77, South Carolina, 2000.
15. J. Shi and J. Malik. Normalized cuts and image segmentation. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pages 731–737, 1997.
16. A. Tremeau and N. Borel. A region growing and merging algorithm to color segmentation. Pattern Recognition, 30, No. 7:1191–1203, 1997.
17. S. Ullman, E. Sali, and M. Vidal-Naquet. A fragment based approach to object representation and classification. In Proc. of 4th international workshop on visual form, Capri, Italy, 2001.
18. A. Yuille and P. Hallinan. Deformable templates. In A. Blake and A. Yuille, editors, Active Vision, pages 21–38, MIT press, 1992.