DYNAMIC IMAGE SEGMENTATION SYSTEM WITH MULTI-SCALING SYSTEM FOR GRAY SCALE IMAGE

Ken'ichi Fujimoto, Mio Musashi, Tetsuya Yoshinaga

2010

Abstract

In this paper, we describe an image segmentation technique for a gray scale image by utilizing the nonlinear dynamics of two respective discrete-time dynamical systems. The authors have proposed a discrete-time dynamical system that consists of a global inhibitor and chaotic neurons that can generate oscillatory responses. By utilizing oscillatory responses, our system can perform dynamic image segmentation, which denotes segmenting image regions in an image and concurrently exhibiting segmented images in time series, for a binary image. In order that our system can work for a gray scale image, we introduce a multi-scaling system as a pre-processing unit of our system. It is also made of a discrete-time dynamical system and can find an image region composed of pixels with different gray levels by multi-scaling gray levels of pixels. In addition, it can compute the proximity between pixels based on their multi-scaled gray levels. Computed proximity becomes significant information for designing parameters in our system. We demonstrated that our dynamic image segmentation system with the multi-scaling system works well for a gray scale image.

References

  1. Ackerman, M. J. (1991). The visible human project. J. Biocommun., 18(2):14.
  2. Aihara, K., Takabe, T., and Toyoda (1990). Chaotic neural networks. Phys. Lett. A, 144(6-7):333-340.
  3. Fujimoto, K., Musashi, M., and Yoshinaga, T. (2008). Discrete-time dynamic image segmentation system. Electron Lett., 44(12):727-729.
  4. Fujimoto, K., Musashi, M., and Yoshinaga, T. (2009). Reduced model of discrete-time dynamic image segmentation system and its bifurcation analysis. Int. J. Imag. Syst. Tech. (in Press).
  5. Hartigan, J. A. and Wong, M. A. (1979). A K-means clustering algorithm. Appl. Stat., 28:100-108.
  6. Pal, N. R. and PAL, S. K. (1993). A review on image segmentation techniques. Pattern recognit., 26(9):1277- 1294.
  7. Wang, D. and Terman, D. (1995). Locally excitatory globally inhibitory oscillator networks. IEEE Trans. Neural Netw., 6(1):283-286.
  8. Zhao, L., Furukawa, R. A., and Carvalho, A. C. (2003). A network of coupled chaotic maps for adaptive multi-scale image segmentation. Int. J. Neural Syst., 13(2):129-137.
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Paper Citation


in Harvard Style

Fujimoto K., Musashi M. and Yoshinaga T. (2010). DYNAMIC IMAGE SEGMENTATION SYSTEM WITH MULTI-SCALING SYSTEM FOR GRAY SCALE IMAGE . In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010) ISBN 978-989-674-018-4, pages 159-162. DOI: 10.5220/0002689701590162


in Bibtex Style

@conference{biosignals10,
author={Ken'ichi Fujimoto and Mio Musashi and Tetsuya Yoshinaga},
title={DYNAMIC IMAGE SEGMENTATION SYSTEM WITH MULTI-SCALING SYSTEM FOR GRAY SCALE IMAGE},
booktitle={Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)},
year={2010},
pages={159-162},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002689701590162},
isbn={978-989-674-018-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)
TI - DYNAMIC IMAGE SEGMENTATION SYSTEM WITH MULTI-SCALING SYSTEM FOR GRAY SCALE IMAGE
SN - 978-989-674-018-4
AU - Fujimoto K.
AU - Musashi M.
AU - Yoshinaga T.
PY - 2010
SP - 159
EP - 162
DO - 10.5220/0002689701590162