Abstract
Using the classical snake algorithm it is difficult to detect the contour of an object with complex concavities. Whereas the GVF (Gradient Vector Flow) method successfully detects the concavity of a contour, but consumes lots of time to compute the energy map. In this paper, we propose a fast snake algorithm to reduce computation time and to improve the performance of detecting and tracking the contour. In order to represent the object’s contour accurately, a snake point inserting and deleting strategy is also proposed. Simulation results from a sequence of images show that our method performs well in detecting and tracking the object’s contour.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kass, M., Witkin, A., Terzopoulos, D.: Snake: Active Contour Models. Int’l J. Computer Vision 1(4), 321–331 (1987)
Xu, C., Prince, J.L.: Snakes, Shapes, and Gradient Vector Flow. IEEE Trans. Image Processing 7(3), 359–369 (1998)
Lam, K.M., Yan, H.: Fast Greedy Algorithm for Active Contour. Electron Letters 30, 21–23 (1994)
Lin, Y.T., Chang, Y.L.: Tracking Deformable Objects with the Active Contour Model. In: International Conference on Multimedia Computing and System, pp. 608–609 (1997)
Pardas, M., Sayrol, E.: Motion Estimation Based Tracking of Active Contours. Pattern Recognition Letters 22, 1447–1456 (2001)
Kim, S.H., Alatter, A., Jang, J.W.: Accurate Contour Detection Based on Snake for Objects with Boundary Concavities. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2006. LNCS, vol. 4141, pp. 226–235. Springer, Heidelberg (2006)
Sum, K.W., Cheung, Y.S.: Paul: Boundary Vector Field for Parametric Active Contours. Pattern Recognition 40(6), 1635–1645 (2007)
Choi, J.J., Kim, J.S.: Modified Energy Function of the Active Contour Model for the Tracking of Deformable Objects. International Journal of Precision Engineering and Manufacturing 7(1), 47–50 (2007)
Shin, J.H., Kim, S.J., Kang, S.K., Lee, S.W., Park, J.K., Abidi, B., Abidi, M.: Optical Flow-Based Real-time Object Tracking Using Non-prior Training Active Feature Model. Elsevier Real-Time Imaging 11, 204–218 (2005)
Seo, K.H., Shin, J.H., Kim, W., Lee, J.J.: Real-Time Object Tracking and Segmentation Using Adaptive Color Snake Model. International Journal of Control, Automation, and Systems 4(2), 236–246 (2006)
Liu, H., Jiang, G., Wang, L.: Multiple Objects Tracking Based on Snake Model and Selective Attention Mechanism. International Journal of Information Technology. 12(2), 76–86 (2006)
Goumeidane, A.B., Khamadja, M., Odet, C.: Parametric Active Contour for Boundary Estimation of Weld Defects in Radiographic Testing. In: 9th International Symposium on Signal Processing and Its Applications, 2007. ISSPA 2007, pp. 1–4 (2007)
Tseng, C.C., Hsieh, J.G., Jeng, J.H.: Active Contour Model via Multi-population Particle Swarm Optimization. Expert Systems with Applications 36, 5348–5352 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, JH., Hua, F., Jang, J.W. (2009). An Improved Object Detection and Contour Tracking Algorithm Based on Local Curvature. In: Ślęzak, D., Pal, S.K., Kang, BH., Gu, J., Kuroda, H., Kim, Th. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2009. Communications in Computer and Information Science, vol 61. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10546-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-10546-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10545-6
Online ISBN: 978-3-642-10546-3
eBook Packages: Computer ScienceComputer Science (R0)