Vectorized image segmentation via trixel agglomeration

Authors:
Lakshman Prasad;Alexei N. Skourikhine
Affiliations:
Los Alamos National Laboratory, Los Alamos, NM;Los Alamos National Laboratory, Los Alamos, NM
Venue:
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Year:
2005

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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.