A straight line detection method based on self-adaptation multi-scale fast discrete Beamlet transform

Abstract

The present invention belongs to the technical field of image processing, and particularly relates to a straight line detection method based on self-adaptation multi-scale fast discrete Beamlet transform. Technical features of the method rest with following details: an edge detection image E is obtained by carrying out edge detection on an image I using a Canny operator, gradient direction angles of edge pixel points on the edge detection image E are calculated and the edge contour is divided into various linearity regions based on a gradient direction angle of two edge points, and fast discrete Beamlet transform is carried out on a current self-adaptation partition subblock through linearity region screening to detect straight lines. The method reduces calculation amount by a certain degree because the scale of an AMFDBT algorithm initial blockette is in a self-adaptation set by the region where a target point is] in the image. At the same time, the initial Beamlet transform can easily cause straight lines to be truncated while the self-adaptive change in partition sub-block makes result of the detected straight line more complete with no truncation of straight lines.

Description

technical field [0001] The invention belongs to image processing, in particular to a straight line detection method based on adaptive multi-scale fast discrete Beamlet transformation. Background technique [0002] The extraction of line features has always been a research hotspot in the field of image processing and pattern recognition. Although traditional line detection algorithms can detect straight lines quickly, they all fail because they do not provide the length information of the line segment and the starting point and end point information of the line segment. Precise positioning of line segments cannot be achieved. [0003] Beamlet transform is one of the effective tools for multi-scale geometric analysis. It is used to restore straight lines, curves and blocky areas from noisy images. It is easy to achieve accurate positioning of line segments and multi-scale approximation of line segments. It is used in the detection of noisy line features. It has obvious advant...

AI Technical Summary

Problems solved by technology

However, the traditional Beamlet transformation requires a lot of calculations, and many improved fast algorithms waste a lot of calculations due to a lot of repetitive work.

Moreover, since the block size of the traditional Beamlet transformation is a binary square, it is relatively tedious to apply it to a general rectangular image.

At the same time, since the algorithm is implemented on the basis of binary recursion, it is easy to cause the straight line to be truncated

[0004] To sum up, the traditional Beamlet transformation has the disadvantages of large amount of calculation and incomplete detection results.

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