Sentence examples for generating object from inspiring English sources

In order to show that attentional modulation can be applied with benefits to different object detection algorithms, we evaluate the effects of attentional modulation using symmetry (see Sect. " Symmetry-based object detection") for generating object hypotheses.

In processing mode, either symmetry (see Sect. " Symmetry-based object detection") or the appearance-based classifier (see Sect. " The appearance-based classifier") is used for generating object hypotheses but never both at the same time.

Then M ∈ 〈 G ♢ (d ) 〉 and M ∉ 〈 G ♢ (d − 1 ) 〉 if and only if there exists an N ∈ T such that G G d Hom ∗ (M, N ) ≠ 0. An important conceptual point about this perspective is that, by choosing a generating object G, the homotopy category of A has been enhanced to a filtered category.

Those attempts produced things that "just don't look that natural," says Clune. "So we went and stole the secrets that biological evolution took millions of years to discover". According to Clune, generating objects in this way automatically gives them useful properties such as symmetry, and when these objects are printed in 3-D, they usually turn out to be structurally sound.

That planning-related signals in SMG and pMTG are able to 'predict' real tool actions, as shown here, provides an important extension of these previous findings, demonstrating that these areas also play an important and selective role in generating object-directed tool actions.

Coming back to the random testing problem from software engineering, we observe that generating objects of a given class of input data according to a uniform distribution is sufficient for testing the correctness of particular algorithms.

Just as the appearance-based classifier, symmetry-based object detection generates object hypotheses in two steps: first, generation of a multiscale, retinotopic confidence map and second, competitive hypothesis selection (see Sect. " Competitive hypothesis selection") based on the produced maps.

The gap between what we interact with, and how we interact with it, will continue to narrow until there is no discernible distinction between manipulating real world objects and a computer generated object.

Subjects thus had to decide to what extent the mentally generated object was structurally similar to the photo object and to what extent the two objects had comparable positions.

The appearance-based classifier [ 39] generates object hypotheses in two successive steps.

In standard clinical versions, subjects are given 1 min to generate object names from a given category (semantic fluency) or words beginning with a specific letter (phonemic fluency).