Fast Hough transform analysis: pattern deviation from line segment

Ershov, Egor; Terekhin, Arseniy P.; Nikolaev, Dmitry P.; Postnikov, Vassili V.; Karpenko, Simon M.

doi:10.1117/12.2228852

Cited by 7 publications

(11 citation statements)

References 3 publications

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“…In (Ershov et al 2015b) it was shown that maximal possible dyadic line deviation from its geometrical counterpart grows with image size as 1 6 log 2 n. Thus for an image size 1024 × 1024 the maximal deviation would be less than two pixels, which is good enough for all practical purposes.…”

Section: Dyadic Pattern: Construction and Accuracymentioning

confidence: 95%

Fast 3D Hough Transform Computation

Ershov¹,

Terekhin²,

Karpenko³

et al. 2016

ECMS 2016 Proceedings Edited by Thorsten Claus, Frank Herrmann, Michael Manitz, Oliver Rose

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We present a three-dimensional generalization of linear Hough transform allowing fast calculating of sums along all planes in discretized space. The main idea of this method is multiple calculation of two-dimensional fast Hough transforms combined with a specific method for plane parametrization. Compared to the direct summation, the method achieves significant acceleration O(n 3 log n) vs O(n 5) .

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Section: Dyadic Pattern: Construction and Accuracymentioning

confidence: 95%

Fast 3D Hough Transform Computation

Ershov¹,

Terekhin²,

Karpenko³

et al. 2016

ECMS 2016 Proceedings Edited by Thorsten Claus, Frank Herrmann, Michael Manitz, Oliver Rose

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“…This version of the algorithm is faster due to the use of self-similar patterns for integration along straight lines with the calculation of partial sums. It is important to note that such recursive patterns approximate real lines in images with an accuracy sufficient for most problems [22,23]. The computational complexity of the fast Hough transform is O (log (n) n 2 ).…”

Section: H S Ixymentioning

confidence: 99%

“…Proof of this lemma was given in paper [24], but it was based on another definition of the pattern. In the paper [23] the author uses the same definition as was given earlier, but he uses this statement without proof.…”

Section: Fig 4 Matrix Of Patterns Indentationsmentioning

confidence: 99%

Vanishing point detection with direct and transposed fast Hough transform inside the neural network

et al. 2020

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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.

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“…Less sensitive to the difference in shooting conditions are methods based on combining extended linear objects such as roads, house walls, rectilinear power lines and so on [ 18 ]. The application of analysis of linear objects gives rise to the usage of fast Hough transform [ 35 , 36 ].…”

Section: General Approach To the Data Fusion Model Of Uav Navigatimentioning

confidence: 99%

New Approaches to the Integration of Navigation Systems for Autonomous Unmanned Vehicles (UAV)

Konovalenko

Kuznetsova

Miller

et al. 2018

Sensors

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The article presents an overview of the theoretical and experimental work related to unmanned aerial vehicles (UAVs) motion parameters estimation based on the integration of video measurements obtained by the on-board optoelectronic camera and data from the UAV’s own inertial navigation system (INS). The use of various approaches described in the literature which show good characteristics in computer simulations or in fairly simple conditions close to laboratory ones demonstrates the sufficient complexity of the problems associated with adaption of camera parameters to the changing conditions of a real flight. In our experiments, we used computer simulation methods applying them to the real images and processing methods of videos obtained during real flights. For example, it was noted that the use of images that are very different in scale and in the aspect angle from the observed images in flight makes it very difficult to use the methodology of singular points. At the same time, the matching of the observed and reference images using rectilinear segments, such as images of road sections and the walls of the buildings look quite promising. In addition, in our experiments we used the projective transformation matrix computation from frame to frame, which together with the filtering estimates for the coordinate and angular velocities provides additional possibilities for estimating the UAV position. Data on the UAV position determining based on the methods of video navigation obtained during real flights are presented. New approaches to video navigation obtained using the methods of conjugation rectilinear segments, characteristic curvilinear elements and segmentation of textured and colored regions are demonstrated. Also the application of the method of calculating projective transformations from frame-to-frame is shown which gives estimates of the displacements and rotations of the apparatus and thereby serves to the UAV position estimation by filtering. Thus, the aim of the work was to analyze various approaches to UAV navigation using video data as an additional source of information about the position and velocity of the vehicle.

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Fast Hough transform analysis: pattern deviation from line segment

Cited by 7 publications

References 3 publications

Fast 3D Hough Transform Computation

Fast 3D Hough Transform Computation

Vanishing point detection with direct and transposed fast Hough transform inside the neural network

New Approaches to the Integration of Navigation Systems for Autonomous Unmanned Vehicles (UAV)

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