利用机器学习方法汇总多个空间视图进行物体分类

Šimon Grác, Peter Beno, F. Duchoň, Michal Malý, Martin Dekan
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引用次数: 0

摘要

文章提出了一种利用三维数据渲染和卷积神经网络生成的多视图进行物体分类的解决方案。为便于介绍和验证该解决方案,开发了一个应用程序来创建三维物体视图,使用所选的卷积神经网络对其进行分类,并评估卷积神经网络的性能。评估基于文章中描述的指标和特征。七个测试对象用于验证所提出的解决方案;每个对象测试了五个 CNN。
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Object classification with aggregating multiple spatial views using a machine-learning approach
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.
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