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For this purpose, various material classifications based on nature, chemical composition, physical properties, and other parameters have been described [ 4].
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Material classification performance experiments were also performed.
Figure 6 Material classification of a bare printed-circuit board.
Cross flow system was therefore proposed for material classification.
Figure 4b depicts the ground truth of material classification by segmenting manually the image.
The associated band gap, magnetic moment, phase, and material classification are also indicated.
The results obtained demonstrate the promising potential of ICAMM for material classification.
Those previous methods were useful for material classification of printed circuit boards.
ICAMM gathers relevant characteristics that have general interest for material classification.
These measures, referred as shape transformers, can classify shapes in a way similar to material classification.
Table 3 displays the material classification results for metal for GoogLeNet, AlexNet, and VGG-16 trained on the MINC dataset.
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