Abstract
In this paper a new method for edge detection in grayscale images is presented. It is based on the use of the Kohonen self-organizing map (SOM) neural network combined with the methodology of Canny edge detector. Gradient information obtained from different masks and at different smoothing scales is classified in three classes (Edge, Non Edge and Fuzzy Edge) using an hierarchical Kohonen network. Using the three classes obtained, the final stage of hysterisis thresholding is performed in a fully automatic way. The proposed technique is extensively tested with success.
Keywords
- Receiver Operating Characteristic
- Gradient Magnitude
- Edge Pixel
- Ground Truth Image
- Sobel Operator
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Sampaziotis, P., Papamarkos, N. (2005). Automatic Edge Detection by Combining Kohonen SOM and the Canny Operator. 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_98
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DOI: https://doi.org/10.1007/11578079_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
eBook Packages: Computer ScienceComputer Science (R0)
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