Composite second-order fractional order signal processing-based edge detection method

Abstract

The invention provides a composite second-order fractional order signal processing-based edge detection method which belongs to the conventional problem in the mode recognition field. The method is a new algorithm for solving an edge by performing gradient operation on all target pixel points in an image by using fractional order signal processing, and comprises the following steps of: generating a gray level matrix for any image; performing the gradient operation on each pixel point in the matrix by using a detection operator to obtain the gradient amplitude of each pixel point respectively; determining an edge position through an obtained zero-crossing image; and if the gradient amplitude of the target pixel point is greater than a threshold value, determining that the target pixel point is an edge point. By the method, smoothing filter preprocessing of the image is saved and the gradient operation is performed by using a fractional order signal processing-based novel composite derivation algorithm; fractional integral in the algorithm suppresses the interference introduced by a fractional differentiation process; and the method has the characteristics of good signal-to-noise ratio, accurate edge positioning and excellent suppression of false edges. The algorithm can be applied to the fields such as automatic target recognition and the like.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to an edge detection method, in particular to an image edge detection method based on compound second-order fractional order signal processing. Background technique [0002] Image threshold segmentation is a widely used image processing technology, which can usually be used for edge detection and segmentation of images. The current threshold segmentation processing is generally based on gray value calculation. This method first determines a gray value change gradient threshold in the gray value range of the image, and then calculates the gray value change gradient of each pixel in the image. Compared with this threshold, and according to the comparison result, the corresponding pixel points are classified into two categories that are larger than the threshold or smaller than the threshold, and further distinguish whether the pixel is an edge point. [0003] In the classic threshold im...

AI Technical Summary

Problems solved by technology

However, we know that both the edge and the noise are high-frequency signals, and the gradient calculation, that is, the derivation process, will inevitably amplify the interference of the noise on the image signal, thus affecting the accuracy of edge detection

In order to reduce the influence of the derivation process, the Canny algorithm performs Gaussian filter smoothing preprocessing on the image. However, since the noise and the edge are both high-frequency signals, the detection accuracy will be affected while eliminating the noise.

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