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The verification system comprises of two classifiers whose scores are fused using sum rule after normalization.
Then the results were fused using the adaptive weighted linear model to produce the final result.
Second, the INS and GPS measurements are fused using an extended Kalman filter (EKF) approach.
The spectral change information and the similarity index are fused using wavelets.
By analysing the recognition results, the different bands are fused using a feature level fusion scheme to improve recognition accuracy.
Results between various components were fused using a query-class-dependent fusion approach, where the weights applied to each component search method were varied depending upon the type of query.
The resulting HNN per-pixel probability maps are then fused using pooling to reliably produce a 3D bounding box of the pancreas that maximizes the recall.
In the second stage, the outputs of classifiers for test dataset are fused using Sugeno fuzzy integral operator to make better decision for target fold class.
To support very low overlap cases, the CPSH-based alignment is furthermore cast into a probabilistic framework that involves computing the CPSHs on segments of the point clouds and finally fused using an ML estimator.
The base images are fused using the regional weighted sum of pixel energy and gradient energy, and a biologically inspired feedback neural network is used to fuse the detail images.
To avoid the synthesis and storage of separate filters, the images from CCD and IR camera are fused using Daubechies wavelet and then the rotation-invariant WaveMACH filter generated with the fused image.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com