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Vanishing point detection with direct and transposed fast Hough transform inside the neural network
A. Sheshkus 4,6, A. Chirvonaya 2,6, D. Matveev 5,6, D. Nikolaev 1,6, V.L. Arlazarov 3,4

Institute for Information Transmission Problems (Kharkevich Institute) RAS, Moscow, Russia,
National University of Science and Technology "MISIS",
Moscow Institute for Physics and Technology, Moscow, Russia,
Institute for Systems Analysis, Federal Research Center "Computer Science and Control"
of Russian Academy of Sciences, Moscow, Russia,
Lomonosov Moscow State University, Moscow, Russia,
Smart Engines Service LLC, Moscow, Russia

 PDF, 1074 kB

DOI: 10.18287/2412-6179-CO-676

Pages: 737-745.

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.

fast Hough transform, vanishing points, deep learning, convolutional neural networks.

Sheshkus A, Chirvonaya A, Matveev D, Nikolaev D, Arlazarov VL. Vanishing point detection with direct and transposed fast Hough transform inside the neural network. Computer Optics 2020; 44(5): 737-745. DOI: 10.18287/2412-6179-CO-676.

This work was supported by the Russian Foundation for Basic Research (projects 18-29-26027 and 17-29-03161).


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