Stereo matching with VG-RAM Weightless Neural Networks

L. Veronese, Lauro Jose Lyrio Junior, Filipe Wall Mutz, Jorcy de Oliveira Neto, Vitor Barbirato
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引用次数: 1

Abstract

Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN) is an effective machine learning technique that offers simple implementation and fast training and test. We examined the performance of VG-RAM WNN on binocular dense stereo matching using the Middlebury Stereo Datasets. Our experimental results showed that, even without tackling occlusions and discontinuities in the stereo image pairs examined, our VG-RAM WNN architecture for stereo matching was able to rank at 114th position in the Middlebury Stereo Evaluation system. This result is promising, because the difference in performance among approaches ranked in distinct positions is very small.
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基于VG-RAM失重神经网络的立体匹配
虚拟泛化随机存取记忆无重神经网络(VG-RAM WNN)是一种有效的机器学习技术,具有实现简单、训练和测试快速等特点。我们使用Middlebury立体数据集测试了VG-RAM小波神经网络在双目密集立体匹配中的性能。我们的实验结果表明,即使没有处理所检查的立体图像对中的遮挡和不连续性,我们的用于立体匹配的VG-RAM WNN架构也能够在Middlebury立体评估系统中排名第114位。这个结果是有希望的,因为在不同位置排名的方法之间的性能差异非常小。
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