Abstract
Multi-scale connectivity measures have been introduced in the context of shape analysis and image segmentation. They are computed by progressive shape decomposition of binary images. This paper presents an efficient method to compute them based on the dual-input Max-Tree algorithm. Instead of handling a stack of binary images, one for each scale, the new method reads a single gray-level image, with each level associated to a unique scale. This reduces the component labeling iterations from a total number equal to the number of scales to just a single pass of the image. Moreover, it prevents the repetitive decomposition of each component under study, for the remaining scale range, since these information are already mapped from the input image to the tree hierarchy. Synthetic and real image examples are given and performance issues are discussed.
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Ouzounis, G.K. (2009). An Efficient Algorithm for Computing Multi-scale Connectivity Measures. In: Wilkinson, M.H.F., Roerdink, J.B.T.M. (eds) Mathematical Morphology and Its Application to Signal and Image Processing. ISMM 2009. Lecture Notes in Computer Science, vol 5720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03613-2_28
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DOI: https://doi.org/10.1007/978-3-642-03613-2_28
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