Picture Labeling and Shape Descriptors for Machine Vision
Picture labeling can be used to give structure to the region of the digital plane occupied by a single-valued figure. Different labeling methods can be adopted, depending on the features one desires to extract. The distance transformation provides an adequate picture labeling when shape is of interest. In this paper, figures digitized on the square grid as well as on the hexagonal grid are taken into account. Distance transforms approximating at different extent the Euclidean distance transform are discussed, and criteria to identify simple shape features, as the local maxima and the layers, are introduced. More powerful shape descriptors can also be extracted, e.g., the skeleton. The structure of the distance transform allows the detection of all the skeletal pixels by using only local operations, and with low computational effort. The conditions to detect the skeletal pixels on the distance transform computed according to the city block distance function are given. Finally, it is shown how the distance transform constitutes a useful starting point to construct the Voronoi Diagram of a set of sources, not necessarily consisting of single pixels.
KeywordsLocal Maximum Voronoi Diagram Edge Point Hexagonal Grid Distance Transformation
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