Abstract
In the current paper, we present a series of algorithms to generate high quality, feature-sensitive, and adaptive meshes from a given grayscale image. The Canny’s edge detector is employed to guarantee that important image features are preserved in the meshes. A halftoning-based sampling strategy is adopted to provide feature-sensitive and adaptive point distributions in the image domain. A Delaunay-triangulation is used to generate initial triangulation of the image, followed by iterative mesh smoothing for mesh quality improvement. Experimental results on several medical images have shown that the proposed method is effective in producing adaptive meshes with high-quality and well-preserved features.
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Xu, M., Gao, Z., Yu, Z. (2014). Feature-Sensitive and Adaptive Mesh Generation of Grayscale Images. In: Zhang, Y.J., Tavares, J.M.R.S. (eds) Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications. CompIMAGE 2014. Lecture Notes in Computer Science, vol 8641. Springer, Cham. https://doi.org/10.1007/978-3-319-09994-1_18
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DOI: https://doi.org/10.1007/978-3-319-09994-1_18
Publisher Name: Springer, Cham
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