Accurate boundary localization using dynamic programming on snakes

Proceedings of the 5th Canadian Conference on Computer and Robot Vision, CRV 2008

Volumn , Issue , 2008, Pages 261-268

  Mishra, Akshaya Kumar ^a   Fieguth, Paul ^a   Clausi, D A ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Curvature guided importance sampling; HMM; Snake; Statistical data fusion; Viterbi algorithm

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; ARTIFICIAL INTELLIGENCE; CHLORINE COMPOUNDS; COMPUTER VISION; HIDDEN MARKOV MODELS; IMAGE PROCESSING; IMAGING TECHNIQUES; ITERATIVE METHODS; MARKOV PROCESSES; MATHEMATICAL PROGRAMMING; MEDICAL IMAGING; ROBOTICS; SYSTEMS ENGINEERING;

ACCURACY IMPROVEMENT; BOUNDARY POINTS; COMPUTER AND ROBOT VISION; CURVATURE GUIDED IMPORTANCE SAMPLING; DEFORMABLE MODELING; EXTERNAL-; HIDDEN-MARKOV MODEL; HMM; IMAGE MEASUREMENTS; IMPORTANCE SAMPLING; MEAN-SQUARE ERRORS; SNAKE; STATISTICAL DATA FUSION; TEST IMAGES; VITERBI ALGORITHM; VITERBI SEARCH;

COMPUTATIONAL METHODS;

EID: 52049103822     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CRV.2008.13     Document Type: Conference Paper

References (20)

1. A. A. Amini. Using dynamic programming for solving variational problems in vision: applications involving deformable models for contours and surfaces. PhD thesis, Ann Arbor, MI, USA, 1990.
2. A. A. Amini, R. W. Curwen, and J. C. Gore. Snakes and splines for tracking non-rigid heart motion. In Computer Vision - ECCV'96, 4th European Conference on Computer Vision, pages 251-261, Cambridge, UK, 1996.
3. K. Bay. Starfish- they are huge, Sept. 2007. http://kahunabay.wordpress. com/2007/09/07/starfish-they-are-huge/.
4. M. Bertero, C. De Mol, and E. R. Pike. Linear inverse problems with discrete data. II. Stability and regularisation. Inverse Problems, 4(3):573-594, 1988.
5. L. D. Cohen. On active contour models and balloons. CVGIP:Image Understanding, 53(2):211-218, March 1991.
6. L. D. Cohen and I. Cohen. Finite-element methods for active contour models and balloons for 2-d and 3-d images. IEEE Trans. on Pattern Anal. Mach. Intell., 15(11):1131-1147, 1993.
7. C. A. Davatzikos and J. L. Prince. An active contour model for mapping the cortex. IEEE Trans. on Medical Imaging, 14(1):65-80, March 1995.
8. J. Hagenauer and P. Hoeher. A Viterbi algorithm with soft-decision outputs and its applications, volume 3, pages 1680-1686, 1989.
9. K. Hirata, E. Kasutani, and Y. Hara. On image segmentation for object-based image retrieval. In International Conference on Pattern Recognition, Quebec city, Quebec, Canada, pages III: 1031-1034, 2002.
10. J. Ho and W. Hwang. Segmenting microarray image spots using an active contour approach.
11. M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. International Journal of Computer Vision, 1(4):321-331, 1988.
12. R. Kwan, A. Evans, and G. Pike. MRI simulation-based evaluation of image-processing and classification methods. 18(11):1085-1097, November 1999.
13. E. A. Lehmann and R. C. Williamson. Particle filter design using importance sampling for acoustic source localisation and tracking in reverberant environments. EURASIP J. Appl. Signal Process., 2006(1): 168-168, 2005.
14. R. Malladi, J. A. Sethian, and B. C. Vemuri. Shape modeling with front propagation: A level set approach. IEEE Trans. on Pattern Anal. Mach. Intell., 17(2):158-175, 1995.
15. E. N. Mortensen and W. A. Barrett. Intelligent scissors for image composition. In SIGGRAPH '95: Proceedings of the 22nd annual conference on Computer graphics and interactive techniques, pages 191-198, New York, NY, USA, 1995. ACM.
16. F. Y. Shih and K. Zhang. Locating object contours in complex background using improved snakes. Computer. Visision. Image Underst., 105(2):93-98, January 2007.
17. A. J. Viterbi. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. on Information Theory, IT-13:260-269, 1967.
18. C. Xu and J. Prince. Snakes, shapes, and gradient vector flow. IEEE Trans, on Image Processing., 7(3):359-369, March 1998.
19. C. Xu and J. L. Prince. Johns hopkins university, image analysis and communications lab, gvf software, Sept. 1999. http://iacl.ece.jhu.edu/projects/ gvf/.
20. G. Zhou and J. Su. Named entity recognition using an hmm-based chunk tagger. In ACL '02: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pages 473-480, 2001.