Abstract
In this paper we present a method to detect edges in images. The method consists of using a 3x3 pixel mask to scan the image, moving it from left to right and from top to bottom, one pixel at a time. Each time it is placed on the image, an agglomerative hierarchical cluster analysis is applied to the eight outer pixels. When there is more than one cluster, it means that window is on an edge, and the central pixel is marked as an edge point. After scanning all the image, we obtain a new image showing the marked pixels around the existing edges of the image. Then a thinning algorithm is applied so that the edges are well defined. The method results to be particularly efficient when the image is contaminated. In those cases, a previous restoration method is applied.
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Keywords
- Edge Detection
- Edge Point
- Central Pixel
- Pattern Recognition Letter
- Agglomerative Hierarchical Cluster Analysis
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References
Allende, H., Galbiati, J.: A non-parametric to filter for digital image restoration, using cluster analysis. Pattern Recognition Letters 25, 841–847 (2004)
Carrasco, R.C., Forcada, M.L.: A note on the Nagendraprasad-Wang-Gupta thinning algorithm. Pattern Recognition Letters 16(5), 539–541 (1995)
Prewitt, J.M.S.: Object enhancement and extraction. In: Rosenfeld, A., Lipkin, B.S. (eds.) Picture Processing and Psychophysics, pp. 75–149. Academic Press, New York (1970)
Pingle, K.K.: Line of vision perception by to computer. In: Grasselli, A. (ed.) Automatic Interpretation and Classification of Images, pp. 277–284. Academic Press, A. Grasselli, New York (1969)
Allende, H., Galbiati, J., Vallejos, R.: Digital Image Restoration Using Autoregressive Time Series Models. In: Proceedings of the Second Latino-American Seminar on Radar Remote Sensing, ESA-SP-434, pp. 53–59 (1998)
Allende, H., Galbiati, J., Vallejos, R.: Robust Image Modeling on Image Processing. Pattern Recognition Letters 22(11), 1219–1231 (2001)
Bustos, H.O.: Robust statistics in SAR image processing. ESA-SP 407, 81–89 (1997)
da Costa Freitas, C., Frery, A.C., Correia, A.H.: Generalized Distributions for Multilook Polarimetric SAR Data under the Multiplicative Model. Technical Report (June 2002)
Kashyap, R.L., Eom, K.B.: Robust Image Techniques with an Image Restoration Application. IEEE Transactions on Acoustics, Speech and Signal Processing 36(8), 1313–1325 (1988)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. J. Wiley, N. York (1990)
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Allende, H., Galbiati, J. (2005). Edge Detection in Contaminated Images, Using Cluster Analysis. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_97
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DOI: https://doi.org/10.1007/11578079_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
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