Titlebook: Mathematical Morphology and Its Applications to Signal and Image Processing; 11th International S Cris L. Luengo Hendriks,Gunilla Borgefors

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Semi-connections and Hierarchiesthat are obtained from such components. These hierarchies can extend data structures such as component-trees and partition-trees, and the associated filtering and segmentation paradigms, leading to improved image processing tools.

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Solving Problems in Mathematical Morphology through Reductions to the U-Curve Problemnciples of the UCS algorithm, present a constrained version of Serra’s formulation of the Tailor problem, prove that this problem is a U-curve problem, apply the UCS algorithm to solve it and compare the performance of UCS with another optimization algorithm. Besides, we present applications of UCS in the context of W-operator design.

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A Quasi-linear Algorithm to Compute the Tree of Shapes of ,D Imagesinear complexity when data quantization is low, typically 12?bits or less. To get that result, this paper introduces a novel representation of images that has some amazing properties of continuity, while remaining discrete.

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Salience-Based Parabolic Structuring Functions, and have low computational complexity. In addition, we discuss how to properly define adjunct morphological operators using the new spatially adaptive structuring functions. It is also possible to obtain flat adaptive structuring elements by thresholding the salience-based parabolic structuring functions.

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Attribute Controlled Reconstruction and Adaptive Mathematical Morphology is multi–scale and auto–dual. Compared with other methods, it is based on a given attribute but does not require a size parameter in order to determine appropriate regions. It is useful to extract objects of a given shape. Additionally, our reconstruction is a connected operator since quasi–flat zones do not create new contours on the image.