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Structuring element

About: Structuring element is a research topic. Over the lifetime, 997 publications have been published within this topic receiving 26839 citations.


Papers
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Journal ArticleDOI
TL;DR: The suitability of morphological filters for the derivation of normalized DSMs from the TDM in complex urban environments is discussed and the capability of the proposed approaches for a reduction of omission errors compared to basic MF-based methods when classifying ground pixels is confirmed.
Abstract: The TanDEM-X mission (TDM) is a spaceborne Radar interferometer which delivers a global digital surface model (DSM) with an unprecedented spatial resolution. This allows resolving objects above ground such as buildings. Extracting and characterizing those objects in an automated manner represents a challenging problem but opens simultaneously a broad range of large-area applications. In this paper, we discuss and evaluate the suitability of morphological filters (MFs) for the derivation of normalized DSMs from the TDM in complex urban Environments and introduce a novel region-growing-based progressive MF procedure. This approach is jointly proposed and can be combined with a postclassification processing scheme to specifically allow for a viable reconstruction of urban morphology in a challenging terrain. The filter approach comprises a multistep procedure using concepts of morphological image filtering, region growing, and interpolation techniques. Therefore, it extends the idea of progressive MFs. The latter aim to identify nonground pixels in the DSM by gradually increasing the size of a structuring element and applying iteratively an elevation difference threshold. After the identification of initial nonground pixels, here, potential nonground pixels are identified within each iteration, and their similarity with respect to neighboring nonground pixels is assessed. Pixels are finally labeled as nonground if a constraint is fulfilled. The postclassification processing scheme adapts techniques of object-based image analyses to further refine regions of classified nonground pixels. Digital terrain models are subsequently generated by interpolating between identified ground pixels. Experimental results are obtained for settlement areas that cover large parts of the cities of Izmir (Turkey) and Wuppertal (Germany). They confirm the capability of the proposed approaches for a reduction of omission errors compared to basic MF-based methods when classifying ground pixels, which is favorable in a mountainous Terrain with steep slopes.

34 citations

Proceedings ArticleDOI
15 Oct 2001
TL;DR: The erosion algorithm is based on a propagation scheme which resulted in an overall Euclidean distance transform algorithm very simple to code and understand, yet with speed performance compared to the Chamfer 3-5-7 sequential raster and anti-raster algorithm.
Abstract: The paper presents a novel Euclidean distance transform algorithm formulated under the mathematical morphology approach. The distance transform is an erosion by a structuring function dependent on the distance metric used. To achieve high speed performance, the squared Euclidean distance structuring function is decomposed into a family of four one-dimensional two-point structuring functions. The erosion algorithm is based on a propagation scheme which resulted in an overall Euclidean distance transform algorithm very simple to code and understand, yet with speed performance compared to the Chamfer 3-5-7 sequential raster and anti-raster algorithm.

34 citations

Journal ArticleDOI
TL;DR: An algorithm was derived that allows a fully automatic segmentation of transcrystalline microcracks and may facilitate petrographical and stereological studies of rock structures observed under a polarizing microscope.

34 citations

PatentDOI
21 Jan 2003
TL;DR: In this article, the shape of the structuring element is controlled by making it a function of both the scaling factor and the principal curvatures of the intensity surface of the face image.
Abstract: An image classification system uses curvature-based multi-scale morphology to classify an image by its most distinguishing features. The image is recorded in digital form. Curvature features associated with the image are determined. A structuring element is modulated based on the curvature features. The shape of the structuring element is controlled by making it a function of both the scaling factor and the principal curvatures of the intensity surface of the face image. The structuring element modulated with the curvature features is superimposed on the image to determine a feature vector of the image using mathematical morphology. When this Curvature-based Multi-scale Morphology (CMM) technique is applied to face images, a high-dimensional feature vector is obtained. The dimensionality of this feature vector is reduced by using the PCA technique, and the low-dimensional feature vectors are analyzed using an Enhanced FLD Model (EFM) for superior classification performance.

34 citations

Journal ArticleDOI
TL;DR: These techniques were applied to different archaeological sites in Turkmenistan and Iraq, updating archaeological cartography, automatic change detection analysis for the Babylon site, and the objective to provide the archaeological team with new instruments for the orientation and the planning of a remote sensing application.

33 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20236
202214
202112
202019
201929
201824