Empirical-mode-decomposition-based edge detecting method

Abstract

The invention discloses an empirical-mode-decomposition-based edge detecting method, which mainly solves the problems that a clear and complete image edge cannot be well detected and a great number of false edges exist in a noise environment in the prior art. The method is technically characterized by comprising the following steps of: (1) acquiring the maximum value envelope and the minimum value envelope of an image by solving two partial differential equations during empirical mode decomposition; (2) acquiring the mean envelope and the differential envelope of the image through the maximumvalue envelope and the minimum value envelope of the image; (3) continuously iterating the step (1) and the step (2) until an iteration stopping condition is met so as to acquire the inherent mode function of the image and the residual image; and (4) calculating the gradient and the threshold of the acquired residual image by using two Prewitt operators so as to acquire the edge of the image. Compared with the traditional Prewitt operator and Canny operator, the method has the advantages that: the clearer and more complete image edge can be acquired, and the influence of the false edges and noise on edge detection is reduced simultaneously.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to edge detection. Specifically, the empirical mode decomposition (EMD) is used for image decomposition, and the edge detection operator is used to detect the edge of the remaining image obtained from the decomposition. The method can be used for target recognition . Background technique [0002] The edge is the discontinuous part of the local grayscale change of the image, and it is the sharp change of the grayscale in the image. It mainly exists between the target and the target, the target and the background, and the region and the region. An important basis for image analysis. The first step in image analysis is often edge detection, so the research on edge detection technology is very important. [0003] The edge detection methods can be summarized into three categories. The first category is the classic edge detection methods, such as the Sobel operator, Robert operator, Prewit...

AI Technical Summary

Problems solved by technology

However, because DEMD triangulates the extreme points of the image, it often cannot cover the entire image, so it will produce uncertain values, and it is easy to produce too bright and too dark points or dark spots; while based on the radial basis Although the decomposed image of interpolated EMD is slightly better than DEMD, it requires a huge amount of calculation

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