Abstract
We present a broad algorithmic framework for transforming an image comprised of pixels into a vectorized image segmented into polygons that can be subsequently used in image processing and understanding. A digital image is processed to extract edge pixel chains and a constrained Delaunay triangulation of the edge contour set is performed to yield triangles that cover the pixelated image without crossing edge contours. Each triangle is attributed a color by a Monte Carlo sampling of pixels within it. A combination of rules, each of which models an elementary perceptual grouping criterion, determines which adjacent triangles should be merged. A grouping graph is formed with vertices representing triangles and edges between vertices that correspond to adjacent triangles to be merged according to the combination of grouping rules. A connected component analysis on the grouping graph then yields collections of triangles that form polygons segmenting and vectorizing the image.
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Prasad, L., Skourikhine, A.N. (2005). Vectorized Image Segmentation via Trixel Agglomeration. In: Brun, L., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2005. Lecture Notes in Computer Science, vol 3434. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31988-7_2
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DOI: https://doi.org/10.1007/978-3-540-31988-7_2
Publisher Name: Springer, Berlin, Heidelberg
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