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In this paper we deal with the topological asymptotic expansion of a class of shape functionals associated with elliptic differential operators of order 2m, m⩾1.
The number of keyframes computed from [12] for each image sequence and target objects are as follows: Case1 has 31 keyframes and different objects for each class of shape, being the most complex case.
Shape memory alloys (SMAs) belong to a class of shape memory materials (SMMs), which have the ability to 'memorise' or retain their previous form when subjected to certain stimulus such as thermomechanical or magnetic variations.
Skeletons are one class of shape descriptors that are used to describe the hierarchies and extent of branching and looping plant networks.
This produced a drop in rat performance (see middle bar in Fig. 3D) that led Gaffan and Simpson to conclude "first, that rats perceived the objects as distinct shapes, not as luminous blobs, and second, that they encoded them in terms of visual features that are common to a class of shape" [147].
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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.
The main novelty of the presented method is that the process of understanding thin objects is related to the visual concept represented as a symbolic name of the possible class of shapes.
Focusing on a class of shapes, the design scheme is applied progressively in four cases in which issues such as a large number of design variables are properly addressed.
This class of shapes generates lines that seem random, but which upon closer and closer inspection reveal inner patterns.
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