Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Vanishing Point Detection with Direct and Transposed Fast Hough
  Transform inside the neural network   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.

35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an