Abstract
The present paper focuses on edge detection in grayscale images. The image is represented by a graph in which the nodes represents the pixels and the edges reflect the connectivity. A cost function is defined using the spatial coordinates of the nodes and the grey levels present in the image. The minimisation of the cost function leads to new spatial coordinates for each node. Using an adequate cost function, the density of points in the regions with large gradient values is increased. The new grid is then fed into an edge detector, which uses the geometric characteristics of the graph. The result is a sub-graph representing the edges present in the original image. The algorithm is tested on real images and the results are compared to existing edge detection techniques.
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Noel, G., Djouani, K., Hamam, Y. (2011). Edge Detection in Grayscale Images Using Grid Smoothing. In: Kim, Th., Adeli, H., Robles, R.J., Balitanas, M. (eds) Ubiquitous Computing and Multimedia Applications. UCMA 2011. Communications in Computer and Information Science, vol 150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20975-8_12
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DOI: https://doi.org/10.1007/978-3-642-20975-8_12
Publisher Name: Springer, Berlin, Heidelberg
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