论文标题
神经网络内部直接和转置快速转化的消失点检测
Vanishing Point Detection with Direct and Transposed Fast Hough Transform inside the neural network
论文作者
论文摘要
在本文中，我们建议一种新的神经网络体系结构，用于图像中消失点检测。关键要素是使用具有标准激活函数的卷积层块分隔的直接和转移的快速霍夫变换。它使我们可以在网络输出的输入图像的坐标中获取答案，从而通过简单地选择最大值来计算消失点的坐标。此外，可以证明可以使用直接旋转的快速转换进行计算。整体操作员的使用使神经网络能够依靠图像中的全局直线特征，因此它是检测消失点的理想选择。为了证明所提出的体系结构的有效性，我们使用了来自DVR的一组图像，并显示了其优于现有方法。请注意，此外，提出的神经网络体系结构基本上重复了使用的直接和背部投影的过程，例如计算机断层扫描中。
In this paper, we suggest a new neural network architecture for vanishing point detection in images. The key element is the use of the direct and transposed Fast Hough Transforms separated by convolutional layer blocks with standard activation functions. It allows us to get the answer in the coordinates of the input image at the output of the network and thus to calculate the coordinates of the vanishing point by simply selecting the maximum. Besides, it was proved that calculation of the transposed Fast Hough Transform can be performed using the direct one. The use of integral operators enables the neural network to rely on global rectilinear features in the image, and so it is ideal for detecting vanishing points. To demonstrate the effectiveness of the proposed architecture, we use a set of images from a DVR and show its superiority over existing methods. Note, in addition, that the proposed neural network architecture essentially repeats the process of direct and back projection used, for example, in computed tomography.
