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The final model was used to evaluate 20 randomly-selected test images from each age by computing their selected image features, applying the corresponding weights, plotting the result in the weighted subspace, and measuring Euclidean distances to the model's class centroids.
Finally, all individual results are combined to the final output via ensemble weighted averaging, with probabilities measuring the corresponding weights.
Regarding the processing architecture, such techniques can be grouped into two main classes indirect, which adopt the basic structure of a weighted mean filter and use fuzzy models to evaluate the corresponding weights, and direct approaches, which adopt special fuzzy systems that directly yield the result.
After collection of all MRs of the participated frames, we divide them into α clusters using a clustering algorithm such as fuzzy C-means[37, 38] or K-means [39] to cluster them into α classes based on the gravitational center (GC) [22] of MRs. The GC of an MR is a weighted average of all coordinates of non-zero errors where the corresponding weights are their absolute error values.
At the other end was a whiteboard listing numbers of textbooks and the corresponding weights.
Where QAF, QBF are edge preservation values and WA, WB are the corresponding weights.
Then, the binarization values are multiplied by the corresponding weights according to their positions.
Afterwards, the potential outliers are detected based on the initial matching result, and the corresponding weights are adjusted to zero.
The first MAC unit section multiplies the outputs of the first DRRC with the corresponding weights and is accumulated.
Designing two weights based on these inputs, we produce an adjusted illumination by fusing the derived inputs with the corresponding weights in a multi-scale fashion.
Shape changes can be represented by altering both spatial location of control points and the corresponding weights towards the optimal design.
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