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This method uses hand calculations being, particularly, a useful tool for processes engineers.
The motion-based method uses hand motion in depth space.
This method uses hand movements detected on the touch surface to move an onscreen cursor.
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Third, our method uses hand-crafted features, such as HOG, HOF, and MBH.
Experimental results on widely used Cohn Kanade database show that the classification performance can be improved effectively by considering spatiotemporal layout of facial expressions, and our method outperforms popular methods using hand-designed features.
Fan et al. [4, 5] proposed a highly accurate method that uses hand-crafted editing rules and a human-validated certain region of the table.
The proposed design method uses only hand calculations apart from a simple linear finite element analysis to determine the degree of non-uniformity in the axial stresses.
Whereas, the proposed method uses a hand-coded parser, external lexers or parsers have not been deployed.
On the other hand, the method uses the insight that tangent bifurcation points are precisely the loci where the equilibrium manifold and the reaction simplex become tangent.
First, we briefly review the pioneering methods that use hand-crafted representations and then we delve in detail into the deep learning-based approaches and recently published datasets.
On the other hand, the Delphi method uses the crisp number and a mean to become the evaluation criterion; these shortcomings might distort the experts' opinion.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com