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The series were analyzed using a multiresolution analysis based on the Haar-wavelet.
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In the broad-scale analysis, all of the wheat fields were identified in the imagery using a multiresolution hierarchical segmentation based on two scales.
As a result, several necessary criterion estimations have to be performed which is inefficient when using a multiresolution strategy.
The authors of [10] try to deal with this problem by using a multiresolution approach to edge detection.
The first block in Figure 1 implements a multiresolution analysis (MRA), using the stationary wavelet transform (SWT), providing two types of wavelet coefficients: approximation coefficients used for modeling the traffic overall tendency and detail coefficients used for modeling the variability around the overall tendency of traffic.
A multiresolution analysis technique allows us to analyse a signal for multiple frequency resolutions while maintaining time resolution unlike a normal frequency transform.
The search for a discrete transform that is well localized in both spaces leads to a multiresolution analysis.
The size of the histogram in a multiresolution analysis, in time domain, increases linearly with R t.
Two numerical characteristics for estimation of translation invariance of a multiresolution analysis (MRA) of L2(R) are proposed.
It presents a multiresolution analysis which exploits the properties of the non-linear operators of mathematical morphology.
In order to develop a multiresolution analysis, a scaling function is needed, together with the dilated and translated version of it,.
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