Lines are one of the most informative structure elements in any images. For this reason, objects detection and recognition problem are often reduced to edge detection task. Radon transform and Hough transform are widely used in straight-line detection. However, these methods allow estimating only the straight line parameters (but not line segment). It is proposed to split the image into square fragments (blocks) in which straight-line segments are detected to solve this problem. A multi-agent system is used to combine segments into curves and drop false detections. The use of artificial neural networks (NN) for programming a part of agent behavior in the multi-agent system is the main theme of this work.
Lines are one of the most informative structure elements on any images. For this reason, objects detection and recognition problems are often reduced to edge detection task. One of the most popular approaches to detect lines is based on the Hough transform or Radon transform.
However, using both of transforms allows estimating the infinite lines parameters only. It is necessary to use additional approaches to estimate the ends of the detected lines. Moreover, Radon transform does not allow detecting non-straight curve shapes at all. This work is oriented to solve line detection problem using Radon transform and multi-agent approach. The results of the experimental researches that confirm the effectiveness of the proposed approach are given. The real full HD image sequences are used. The direction of further improvements is proposed.
In the practice of line detection applications based on Radon transform (RT), noise and clutter decrease the sharpness of the RT local peaks, which correspond to linear edges in the original image. We suggest a new approach to line detection on gray-scale images using the so-called weighted Radon transform (WRT). The proposed WRT-based approach exploits gradient direction information in such a way that only the gradient component perpendicular to the line direction is integrated to form a local peak corresponding to the line. Theoretical and experimental studies have shown the effectiveness of the suggested WRT-based line detection method.
One of the most popular approaches to detect lines is based on the Radon transform (RT). But in real-world applications
RT-based approach suffers from the noise and clutter, because they decrease the sharpness of the local maximums.
In this paper we suggest a new approach to computational effective line detection using the Weighted Radon Transform
(WRT). The suggested WRT-based approach uses gradient direction information, so only the differences that are
perpendicular to the line direction are integrated to make a local maximum corresponding to the line.
The theoretical and experimental studies show the effectiveness of the WRT-based line detection. The suggested WRTbased
algorithm can be effectively implemented in real-time systems using parallelization and FFT-based techniques.
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