<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>The article proposes a solution for object classification using multiple views generated from 3D data rendering and convolutional neural networks. For presentation purposes and easier verification of the solution, an application was developed to create views of 3D objects, classify them using the selected CNN, and evaluate the performance of the CNN. The evaluation is based on metrics and characteristics described in the article. Seven testing objects were used to verify the proposed solution; five CNNs were tested for each.</p>
</abstract>

The article proposes a solution for object classification using multiple views generated from 3D data rendering and convolutional neural networks. For presentation purposes and easier verification of the solution, an application was developed to create views of 3D objects, classify them using the selected CNN, and evaluate the performance of the CNN. The evaluation is based on metrics and characteristics described in the article. Seven testing objects were used to verify the proposed solution; five CNNs were tested for each.

Object classification with aggregating multiple spatial views using a machine-learning approach

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The article proposes a solution for object classification using multiple views generated from 3D data rendering and convolutional neural networks. For...

Object classification with aggregating multiple spatial views using a machine-learning approach

Publié en ligne: 04 avr. 2024

Pages: 137 - 150

Reçu: 14 févr. 2024

DOI: https://doi.org/10.2478/jee-2024-0017

Mots clés
3D, OpenGL, CNN recycling, classification, classification statistics aggregation, 3D model labeling, reprojection

© 2024 Šimon Grác et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Object classification with aggregating multiple spatial views using a machine-learning approach

Publié en ligne: 04 avr. 2024

Pages: 137 - 150

Reçu: 14 févr. 2024

DOI: https://doi.org/10.2478/jee-2024-0017

Mots clés3D, OpenGL, CNN recycling, classification, classification statistics aggregation, 3D model labeling, reprojection

© 2024 Šimon Grác et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
3D, OpenGL, CNN recycling, classification, classification statistics aggregation, 3D model labeling, reprojection