Edge Detection with the Parametric Filtering Method (Comparison with Canny Method)
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33122
Edge Detection with the Parametric Filtering Method (Comparison with Canny Method)

Authors: Yacine Ait Ali Yahia, Abderazak Guessoum

Abstract:

In this paper, a new method of image edge-detection and characterization is presented. “Parametric Filtering method" uses a judicious defined filter, which preserves the signal correlation structure as input in the autocorrelation of the output. This leads, showing the evolution of the image correlation structure as well as various distortion measures which quantify the deviation between two zones of the signal (the two Hamming signals) for the protection of an image edge.

Keywords: Edge detection, parametrable recursive filter, autocorrelation structure, distortion measurements.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1062766

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1290

References:


[1] J.R. Deller, Jr.J. Proakis, and J. H. L. Hansen, Discrete-Time processing of Speech signals. New York: Macmilian, 1993.
[2] R. Adre-Obrech, A new statistical approaches for automatic segmentation of continuous speech signals, IEEE Trans. Acoust., Speech, Signal Processing, vol 36.pp 29-40, Jan 1998.
[3] E. Vidal and A. Marzal, A review and new approach for automatic segmentation of speech signals, in Signal Processing V: Theories and Application, L. Torres et al. Eds. New York: Elsevier, 1990, vol 1 pp. 43-53.
[4] L. Rabiner and B.H. Juang, Fundamental of speech Recognition. Englewood Cliffs, NJ: Prentice-Hall, 1993.
[5] L. R. Rabiner, J.G Wilpon, and F. K. Soong, high performance connected digit. Recognition using hidden Markov models. IEEE Trans. Acoust., Speech, Signal Processing, vol. 37, pp 1214-1225, Aug 1989.
[6] E. Paksoy, K. Sanivasan, and A. Gersho, Variable rate speech coding with phonetic segmentation, in Proc. ICASSP, Minneapolis, MN, Apr.1993, vol. 2, pp 155-158.
[7] S. Wang and A. Gersho, Improved phonetically-segmentation vector excitation coding at 3.4 kb/s, in Proc. ICASSP San Francisco, CA, Mar 1992, vol. 1, pp.349-352.
[8] T.H. Li and J.D. Gibson, Discrimination of time series of, speech by parametric filtering, J. Amer. Stat. Assoc, vol. 91, pp. 284-296, Mar. 1996.
[9] T.H. Li and J.D. Gibson, Discrimination analysis of speech by parametric filtering, in Proc. IEEE conf Inform. Science, Systems, Princeton, NJ, Mar. 1994, pp. 575-580.
[10] M. Basseville and A. Benvenise, Detection of Abrupt Changes in Signals and Dynamical System. New York. Springer, 1986.
[11] M. Basseville and I.V. Nikoforov, Detection of Abrupt Changes, Theory and Application. Englewood Cliffs, NJ Prentice-Hall 1993.
[12] M. Basseville, Distance measures for signal processing and pattern recognition, Signal Processing, vol. 18, pp. 349-369, 1989.
[13] R.M. Gray, A. Bazo, Y. Matsuyama, Distortion measures for Speech processing, IEEE Trans. Acoust, Speech, Signal Processing, vol ASSP- 28, pp. 367-376, Apr. 1980.
[14] T. Svendsen and F.K. Soong, On the automatic segmentation of speech signals, in Proc. ICASSP. Dallas, TX, Apr. 1987, ^^. 77-80.
[15] J.G. Wilpon, B.H. Juang, and L.R. Rabiner, An investigation on the use of acoustic sub-word units for automatic speech recognition, in Proc. ICASSP, TX, Apr.1987, pp 821-824.
[16] S.M. Kay, Modern Spectral Estimation, Theory and Application. Englewood Cliffs NJ, Prentice-Hall, 1988.
[17] M. Lavielle, Detection of changes in the spectrum of a multidimensional process, IEE Trans. Signal processing, vol. 41, pp. 742-749 Feb. 1993.
[18] E. Parzen, Times series, statistics, and information, in New Directions in Time Series Analysis, P1. 1, D Brillinger, Eds New York: Springer- Verlag, 1992, pp 265-286.
[19] T.H. Li and J.D. Gibson, Speech Analysis and Segmentation by parametric filtering IEEE Trans on speech and audio processing. Vol.4, No 3 May 1996 pp 203-213.