We consider the problem of feature detection, in the presence of clutter in spatial point processes. A previous study addresses the issue of the selection of the best nearest neighbour for clutter removal. We outline a simple workflow to automatically estimate the number of nearest neighbours by means of segmented regression models applied to an entropy measure of cluster separation. The method is suitable for a feature with clutter as two superimposed Poisson processes on any twodimensional space, including linear networks. We present simulations to illustrate the method and an application to the problem of seismic fault detection
The task of discriminating between heterogeneity and complete spatial randomness (CSR) for a given p...
We propose a finite mixture model for clustering of the spatial data patterns. The model is based on...
The aim of this work is to detect spatial clusters. We link Erdös graph and Poisson point process. ...
In a spatial point set, clustering patterns (features) are difficult to locate due to the presence o...
When two spatial point processes are overlaid, the one with the higher rate is shown as clustered po...
We consider the problem of detecting features of general shape in spatial point processes in the pre...
In a point process spatio-temporal framework, we consider the problem of features detection in the ...
We consider the problem of detection of features in the presence of clutter for spatio-temporal poin...
We consider the problem of detection of features in the presence of clutter for spatio‐temporal poin...
Abstract – In the analysis of spatial point patterns, complete spatial randomness (CSR) hypothesis, ...
To automatically identify arbitrarily-shaped clusters in point data, a theory of point process decom...
Spatial point pattern analysis usually concerns identifying features in an observation window where ...
Abstract This paper describes methods for randomly thinning two main classes of spatial point proces...
Clustered events are usually deemed as feature when several spatial point processes are overlaid in ...
The problem of features detection under present of clutter in point process on linear networks esta...
The task of discriminating between heterogeneity and complete spatial randomness (CSR) for a given p...
We propose a finite mixture model for clustering of the spatial data patterns. The model is based on...
The aim of this work is to detect spatial clusters. We link Erdös graph and Poisson point process. ...
In a spatial point set, clustering patterns (features) are difficult to locate due to the presence o...
When two spatial point processes are overlaid, the one with the higher rate is shown as clustered po...
We consider the problem of detecting features of general shape in spatial point processes in the pre...
In a point process spatio-temporal framework, we consider the problem of features detection in the ...
We consider the problem of detection of features in the presence of clutter for spatio-temporal poin...
We consider the problem of detection of features in the presence of clutter for spatio‐temporal poin...
Abstract – In the analysis of spatial point patterns, complete spatial randomness (CSR) hypothesis, ...
To automatically identify arbitrarily-shaped clusters in point data, a theory of point process decom...
Spatial point pattern analysis usually concerns identifying features in an observation window where ...
Abstract This paper describes methods for randomly thinning two main classes of spatial point proces...
Clustered events are usually deemed as feature when several spatial point processes are overlaid in ...
The problem of features detection under present of clutter in point process on linear networks esta...
The task of discriminating between heterogeneity and complete spatial randomness (CSR) for a given p...
We propose a finite mixture model for clustering of the spatial data patterns. The model is based on...
The aim of this work is to detect spatial clusters. We link Erdös graph and Poisson point process. ...