Object detetion and image segmentation using texture pressure energy in parametric active contour models

Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an

Volumn 31, Issue 4, 2008, Pages 649-657

  Vard, Ali Reza ^a   Nilchi, Ahmad Reza Naghsh ^a   Moallem, Payman ^a  

^a UNIVERSITY OF ISFAHAN   (Iran)

Author keywords

Energy function; Image segmentation; Parametric active contour models; Texture features

Indexed keywords

IMAGE TEXTURE; OBJECT DETECTION; OBJECT RECOGNITION; TEXTURES;

ACTIVE CONTOUR MODEL; ENERGY FUNCTIONS; IMAGE PROCESSING APPLICATIONS; MOMENT BASED METHOD; OBJECT BOUNDARIES; PARAMETRIC ACTIVE CONTOURS; TEXTURE FEATURES; TEXTURE PROPERTIES;

IMAGE SEGMENTATION;

EID: 47049126899     PISSN: 02533839     EISSN: 21587299     Source Type: Journal    
DOI: 10.1080/02533839.2008.9671418     Document Type: Article

References (21)

1. Almageed, W. A., and Smith, C. E., 2004. “Active Deformable Models Using Density Estimation,”. International Journal of Image and Graphics, Special Issue on Deformable Models for Image Analysis and Pattern Recognition, 4 (3):343–361.
2. Amini, A. A., Weymouth, T. E., and Jain, R. C., 1990. “Using Dynamic Programming for Solving Variational Problems in Vision,”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (9):855–867.
3. Caselles, V., Catte, F., Coll, T., and Dibos, F., 1993. “A Geometric Model for Active Contours in Image processing,”. Numerische Mathematik, 66 (1):1–31.
4. Cheng, J., Foo, S. W., and Krishnan, S. M., “Boundary Detection in Echocardiographic Images by Directional Gradient Vector Flow,”. IEEE International Workshop on Biomedical Circuits & Systems. Singapore. pp. S3/3–S1–4.
5. Cootes, T. F., Taylor, C. J., Cooper, D. H., and Graham, J., 1995. “Active Shape Models-Their Training and Application,”. Computer Vision and Image Understanding, 61 (1):38–59.
6. Gunn, S. R., and Nixon, M. S., 1997. “A Robust Snake Implementation; a Dual Active Contour,”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (1):63–68.
7. Hamarneh, G., Chodorowski, A., and Gustavsson, T., “Active Contour Models:Application to Oral Lesion Detection in Color Images,”. IEEE International Conference on Systems Man, and Cybernetics. Vol. 4, pp. 2458–2463.
8. Ivins, J., and Porrill, J., “Active Region Models for Segmenting Medical Images,”. Proceedings of the 1st International Conference on Image Processing. Austin, TX, USA. pp. 227–231.
9. Kass, M., Witkin, A., and Terzopoulos, D., “Snakes:Active Contour Models,”. First International Conference on Computer Vision. London, UK. pp. 259–268.
10. Li, C., Liu, J., and Fox, M. D., “Segmentation of Edge Preserving Gradient Vector Flow:an Approach Toward Automatically Initializing and Splitting of Snakes,”. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA. pp. 162–167.
11. Malladi, R., Sethian, J. A., and Vemuri, B. C., 1995. “Shape Modeling with Front Propagation:a Level Set Approach,”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17 (2):158–175.
12. McInerney, T., and Terzopoulos, D., 2000. “T-snakes:Topology Adaptive Snakes,”. Medical Image Analysis, 4 (2):73–91.
13. Salembier, P., Torres, L., Meyer, F., and Gu, C., 1995. “Region-Based Video Coding Using Mathematical Morphology,”. Proceedings of IEEE, 83 (6):843–857.
14. Schaub, H., and Smith, C., “Color Snakes for Dynamic Lighting Conditions on Mobile Manipulation Platforms,”. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and System. Albuquerque, NM, USA. Vol. 2, pp. 1272–1277.
15. Seo, K., Shin, J., Kim, W., and Lee, J., 2006. “Real- Time Object Tracking and Segmentation Using Adaptive Color Snake Model,”. International Journal of Control, Automation, and Systems, 4 (2):236–246.
16. Tuceryan, M., and Jain, A. K., 1993. “Texture Analysis,”. In The Handbook of Pattern Recognition and Computer Vision, Edited by:Pau, L. F., and Wang, P.S.P., 235–276. Singapore:World Scientific Publishing Co.
17. Tuceryan, M., 1994. “Moment Based Texture Segmentation,”. Pattern Recognition Letters, 15 (7):659–668.
18. Wang, D., 1998. “Unsupervised Video Segmentation Based on Watersheds and Temporal Tracking,”. IEEE Transactions on Circuits and Systems for video Technology, 8 (5):539–546.
19. Williams, D., and Shah, M., 1992. “A Fast Algorithm for Active Contours and Curvature Estimations,”. Computer Vision, Graphics, and Image Processing:Image Understanding, 55 (1):14–26.
20. Xu, C., and Prince, J. L., “Gradient Vector Flow:A New External Force for Snakes,”. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Baltimore, MD, USA. pp. 66–71.
21. Yang, L., Meer, P., and Foran, D. J., 2005. “Unsupervised Segmentation Based on Robust Estimation and Color Active Contour Models,”. IEEE Transactions on Information Technology in Biomedicine, 9 (3):475–486.