An improved time-adaptive self-organizing map for high-speed shape modeling. (English) Zbl 1183.68534

Izadi, Mohammad; Safabakhsh, Reza

Pattern Recognition 42, No. 7, 1361-1370 (2009).

Summary: An improved active contour model based on the time-adaptive self-organizing map with a high convergence speed and low computational complexity is proposed. For this purpose, the active contour model based on the original time-adaptive self-organizing map is modified in two ways: adaptation of the speed parameter and reduction of the number of neurons. By adapting the speed parameter, the neuron motion speed is determined based on the distance of each neuron from the shape boundary which results in an increase in the speed of convergence of the contour. Using a smaller number of neurons, the computational complexity is reduced. To achieve this, the number of neurons used in the contour is determined based on the boundary curvature. The proposed model is studied and compared with the original time-adaptive self-organizing map. Both models are used in several experiments including a tracking application. Results reveal the higher speed and very good performance of the proposed model for real-time applications.

MSC:

68T10	Pattern recognition, speech recognition
68W05	Nonnumerical algorithms

Keywords:

active contour model; time-adaptive self-organizing map; TASOM; adaptive speed parameter; boundary curvature; person tracking

References:

[1]	Kass, M.; Witkin, A.; Terzopoulos, D., Snakes: active contour models, International Journal of Computer Vision, 1, 4, 312-331 (1988)
[2]	Cootes, T. F.; Taylor, C. J.; Cooper, D. H.; Graham, J., Active shape models—their training and application, Computer Vision and Image Understanding, 61, 1, 38-59 (1995)
[3]	Staib, L. H.; Duncan, J. S., Boundary finding with parametrically deformable models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 11, 1061-1075 (1992)
[5]	Han, C.; Kerwin, W. S.; Hatsukami, T. S.; Hwang, J. N.; Yuan, C., Detecting objects in image sequences using rule-based control in an active contour model, IEEE Transactions on Biomedical Engineering, 50, 6 (2003)
[6]	Neumann, A., Graphical Gaussian shape models and their application to image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 3 (2003)
[7]	Shah-Hosseini, H.; Safabakhsh, R., TASOM: a new time adaptive self-organizing map, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 33, 2, 271-282 (2003)
[8]	Pentland, A. P.; Sclaroff, S., Closed-form solutions for physically based shape modeling and recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 7, 715-729 (1991)
[9]	Staib, L. H.; Duncan, J. S., Boundary finding with parametrically deformable models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 11, 1061-1075 (1992)
[12]	Caselles, V.; Kimmel, R.; Sapiro, G., Geodesic active contours, International Journal of Computer Vision, 22, 1, 61-79 (1997) · Zbl 0894.68131
[13]	Cohen, L. D.; Kimmel, R., Global minimum for active contour models: a minimal path approach, International Journal of Computer Vision, 24, 1, 57-78 (1997)
[14]	Han, C.; Hatsukami, T. S.; Hwang, J. N.; Yuan, C., A fast minimal path active contour model, IEEE Transactions on Image Processing, 10, 865-873 (2001) · Zbl 1036.68614
[15]	Kohonen, T., Self-organized formation of topologically correct feature maps, Biological Cybernetics, 43, 59-69 (1982) · Zbl 0466.92002
[16]	Abrantes, A. J.; Marques, J. S., A class of constrained clustering algorithms for object boundary extraction, IEEE Transactions on Image Processing, 5, 11, 1507-1521 (1996)
[17]	Venkatesh, Y. N.; Rishikesh, N., Self-organizing neural networks based on spatial isomorphism for active contour modeling, Pattern Recognition, 33, 7, 1239-1250 (2000)
[20]	Shah-Hosseini, H.; Safabakhsh, R., A TASOM-based algorithm for active contour modeling, Pattern Recognition Letters, 24, 1361-1373 (2003) · Zbl 1048.68113
[21]	Shah-Hosseini, H.; Safabakhsh, R., TASOM: a new time adaptive self-organizing map, IEEE Trans. Systems, Man, and Cybernetics, Part: B: Cybernetics, 33, 2 (2003)
[23]	Garland, M.; Zhou, Y., Quadric-based simplification in any dimension, ACM Transactions on Graphics, 24, 2, 209-239 (2005)