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A multi-criteria approach based on descriptive, correlation and multivariate statistics and mathematical morphology was proposed to investigate long-term morphological changes in a metropolitan region (Attica, Greece) representative of the 'Mediterranean compact city' archetype.
Thus, an image processing technique based on mathematical morphology was used to solve this problem.
Mathematical morphology was initially introduced for image analysis and was primarily aimed on formalization of microscopy images by extracting quantitative information (Matheron 1971, 1975; Najman and Talbot 2010).
A method based on the principle of mathematical morphology was proposed, which can be used to thicken the colored regions in a color volume dataset (CVD) obtained from slicing an SCM, so that an SCM with thickened colored regions can be formed.
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In [8], a contrast enhancement technique using mathematical morphology is presented, called morphological contrast enhancement.
A novel signal representation using fuzzy mathematical morphology is developed.
Mathematical morphology is used for smoothing image and eliminating small holes.
A new candidates segmentation method, based on mathematical morphology, is proposed.
Mathematical morphology is a discipline within the field of image processing that involves the structural analysis of images.
Mathematical morphology is presented in [11] for detecting symmetrical fault during power swing and it is based on monitoring shape of signal.
Mathematical morphology is often used in binary image processing for pre- or post processing, such as morphological filtering, thinning, and pruning[17].
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