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In this paper, we experimentally compare some of the most significant methods for merging line segments in maps by applying them to publicly available data sets.
Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation method.
Some existence results are obtained for convex as well as non-convex multivalued maps by applying standard fixed point theorems for such maps.
Open image in new window Fig. 1 I The decomposed dictionary components from the fMRI data during one single task and II the 14 corresponding reference weight maps by applying the HAFNI method to the whole-brain fMRI signals.
We converted these continuous predictions to binary presence-absence maps by applying a threshold before drawing the pseudo-absences.
The background level was set to be the same in all calculated difference maps by applying the same absolute plotting parameters as used for the control difference maps.
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We analyze this map by applying the method of multiple scales upto first order.
The NBS map used in this study was derived from the NBS LC map by applying to each stratum its respective average biomass density reported in Drichi [21].
Finally, we had achieved an acceptable accuracy of landslide susceptibility map by applying the PFR model when we used a high-resolution of DEM.
We obtain the following convergence result for solving split feasibility problem (3.1) and the fixed point problem for a k-strictly pseudocontractive mapping by applying Theorem 2.4.
The RSBERS first applies entropy rate superpixel segmentation method to split the image into compact, homogeneous and similar-sized regions, and gets the saliency map by applying saliency estimation methods in each superpixel regions.
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