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These methods are very efficient because they deal with the very specific classes of shapes.
Three different classes of shapes, namely: ligaments, droplets, and large, generally irregular objects are examined.
We design a novel method to learn the relationship between two classes of shapes, which are related by certain operations or transformation.
We fabricated modules with two different classes of shapes: (i) square and clover-leaf cross-sections, to demonstrate that our technique can generate modules with sharp corners and with various shapes; and (ii) circular cross-sections to allow easy comparison of our technique to the original process of McGuigan and Sefton, which also produced cylindrical modules [23], [24].
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The possible classes of shape, viewed as hierarchical structures, are incorporated into the shape model.
The result of the visual reasoning is the symbolic name that refers to the possible classes of shape.
In this paper, the optimal contact shape is studied by formulating several classes of shape optimization problems, namely minimization of generalized wear volume rate, friction dissipation power and wear dissipation rate occurring in two bodies.
In contrast, the PCA separates components according to the structure of the shape tangent space, and can therefore identify more classes of shape changes according to their symmetries.
These methods are very efficient because they deal with a very specific class of shapes.
A Hough transform for natural shapes (HTNS) was subsequently proposed, but necessitates the supervised learning of the class of shapes.
This enables us to seek for the optimum in a class of functions and their derivatives instead of a class of shapes.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com