Spatial and Spectral Morphological Template Matching
Identifieur interne : 004671 ( Hal/Corpus ); précédent : 004670; suivant : 004672Spatial and Spectral Morphological Template Matching
Auteurs : Jonathan Weber ; Sébastien LefèvreSource :
- Image and Vision Computing [ 0262-8856 ] ; 2012-12.
English descriptors
Abstract
Template matching is a very topical issue in a wide range of imaging applica- tions. Mathematical morphology offers the hit-or-miss transform, an operator which has been successfully applied for template matching in binary images. More recently, it has been extended to greyscale images and even to multi- variate images. Nevertheless, these extensions, despite being relevant from a theoretical point-of-view, might lack practical interest due to the inherent diffi- culty to set up correctly the transform and its parameters (e.g. the structuring functions). In this paper, we propose a new and more intuitive operator which allows for morphological template matching in multivariate images from both a spatial and spectral point of view. We illustrate the potential of this operator in the context of remote sensing.
Url:
DOI: 10.1016/j.imavis.2012.07.002
Links to Exploration step
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<front><div type="abstract" xml:lang="en">Template matching is a very topical issue in a wide range of imaging applica- tions. Mathematical morphology offers the hit-or-miss transform, an operator which has been successfully applied for template matching in binary images. More recently, it has been extended to greyscale images and even to multi- variate images. Nevertheless, these extensions, despite being relevant from a theoretical point-of-view, might lack practical interest due to the inherent diffi- culty to set up correctly the transform and its parameters (e.g. the structuring functions). In this paper, we propose a new and more intuitive operator which allows for morphological template matching in multivariate images from both a spatial and spectral point of view. We illustrate the potential of this operator in the context of remote sensing.</div>
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