Abstract
In this paper a framework for defining scale-spaces, based on the computational geometry concepts of α-shapes, is proposed. In this approach, objects (curves or surfaces) of increasing convexity are computed by selective sub-sampling, from the original shape to its convex hull. The relationships with the Empirical Mode Decomposition (EMD), the curvature motion-based scale-space and some operators from mathematical morphology, are studied. Finally, we address the problem of additive image/signal decomposition in fluorescence video-microscopy. An image sequence is mainly considered as a collection of 1D temporal signals, each pixel being associated with its temporal intensity variation.
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Chessel, A., Cinquin, B., Bardin, S., Salamero, J., Kervrann, C. (2009). Computational Geometry-Based Scale-Space and Modal Image Decomposition. In: Tai, XC., Mørken, K., Lysaker, M., Lie, KA. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2009. Lecture Notes in Computer Science, vol 5567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02256-2_64
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DOI: https://doi.org/10.1007/978-3-642-02256-2_64
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