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Vanishing points detection using combination of fast hough transform and deep learning

机译:快速霍夫变换和深度学习相结合的消失点检测

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In this paper we propose a novel method for vanishing points detection based on convolutional neural network (CNN) approach and Fast Hough transform algorithm. We show how to determine Fast Hough Transform neural network layer and how to use it in order to increase usability of the neural network approach to the vanishing point detection task. Our algorithm includes CNN with consequence of convolutional and fast Hough transform layers. We are building estimator for distribution of possible vanishing points in the image. This distribution can be used to find candidates of vanishing point. We provide experimental results from tests of suggested method using images collected from videos of a road trips. Our approach shows stable result on test images with different projective distortions and noise. Described approach can be effectively implemented for mobile GPU and CPU.
机译:在本文中,我们提出了一种基于卷积神经网络(CNN)方法和快速霍夫变换算法的消失点检测新方法。我们展示了如何确定快速霍夫变换神经网络层,以及如何使用它来增加消失点检测任务的神经网络方法的可用性。我们的算法包括具有卷积和快速霍夫变换层的结果的CNN。我们正在建立估算器,以分配图像中可能消失的点。此分布可用于查找消失点的候选项。我们使用从公路旅行视频中收集的图像,从建议方法的测试中提供实验结果。我们的方法在具有不同投影失真和噪声的测试图像上显示稳定的结果。所描述的方法可以有效地用于移动GPU和CPU。

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