Coarse Image Edge Detection using Self-adjusting Resistive-fuse Networks

Haichao Liang, Takashi Morie

2010

Abstract

We propose a model of coarse edge detection using self-adjusting resistive-fuse networks. The resistive-fuse network model is known as a nonlinear image processing model, which can detect coarse edges from images by smoothing noise and small regions. However, this model is hardly used in real environment because of the sensitive dependence on the parameters and the complexity of the annealing process. In this paper, we first introduce self-adjusting parameters to reduce the number of parameters to be controlled. Then, we propose a heating-and-cooling sequence for fast and robust edge detection. The proposed model can detect edges more correctly than the original one, even if an input image includes a gradation.

References

  1. Phan, R., Androutsos, D.: Content-based retrieval of logo and trademarks in unconstrained color image databases using color edge gradient co-occurrence histograms. Computer Vision and Image Understanding 114 (2010) 66-84
  2. Tsai, T. H., Chen, Y. C., Fang, C. L.: 2DVTE: A two-directional videotext extractor for rapid and elaborate design. Pattern Recognition 42 (2009) 1496-1510
  3. Toulminet, G., Bertozzi, M., Mousset, S., Bensrhair, A., Broggi, A.: Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis. IEEE Trans. on Image Processing 15 (2006) 2364-2375
  4. Naito, T., Ito, T., Kaneda, Y.: The obstacle detection method using optical flow estimation at the edge image. In: IEEE Intelligent Vehicles Symposium, Istanbul, Turkey (2007) 817-822
  5. Harris, J. G., Koch, C., Staats, E., Luo, J.: Analog hardware for detecting discontinuities in early vision. Int. Journal of Computer Vision 4 (1990) 211-223
  6. Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence 6 (1984) 721-741
  7. Kawashima, Y., Atuti, D., Nakada, K., Okada, M., Morie, T.: Coarse image region segmentation using region- and boundary-based coupled MRF models and their PWM VLSI implementation. In: Int. Joint Conf. on Neural Networks (IJCNN2009), Atlanta, USA (2009) 1559-1565
  8. Nakano, T., Morie, T., Ishizu, H., Ando, H., Iwata, A.: FPGA implementation of resistivefuse networks for coarse image-region segmentation. Intelligent Automation and Soft Computing 12 (2006) 307-316
  9. Blake, A.: Comparison of the efficiency of deterministic and stochastic algorithms for visual reconstruction. IEEE Trans. on Pattern Analysis and Machine Intelligence 11 (1989) 2-12
  10. Geiger, D., Girosi, F.: Parallel and deterministic algorithms from MRF's: Surface reconstruction. IEEE Trans. on Pattern Analysis and Machine Intelligence 13 (1991) 401-412
  11. Harris, J.G.: Analog models for early vision. PhD thesis, California Institute of Technology (1991)
  12. (USC-SIPI) http://sipi.usc.edu/database/.
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Paper Citation


in Harvard Style

Liang H. and Morie T. (2010). Coarse Image Edge Detection using Self-adjusting Resistive-fuse Networks . In Proceedings of the 10th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2010) ISBN 978-989-8425-14-0, pages 43-52. DOI: 10.5220/0003015600430052


in Bibtex Style

@conference{pris10,
author={Haichao Liang and Takashi Morie},
title={Coarse Image Edge Detection using Self-adjusting Resistive-fuse Networks},
booktitle={Proceedings of the 10th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2010)},
year={2010},
pages={43-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003015600430052},
isbn={978-989-8425-14-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2010)
TI - Coarse Image Edge Detection using Self-adjusting Resistive-fuse Networks
SN - 978-989-8425-14-0
AU - Liang H.
AU - Morie T.
PY - 2010
SP - 43
EP - 52
DO - 10.5220/0003015600430052