A multi-angle edge detection method based on Gaussian wavelet one-dimensional peak recognition

Abstract

The invention relates to a multi-angle edge detection method based on Gauss wavelet one-dimensional peak value identification. The multi-angle edge detection method comprises the following steps: histogram analysis is performed on a to-be-processed image to obtain the gray value of a target and a background of the target, and the obtained gray value is adopted to perform assignment on 18 given template images; multiple groups of edge detection parameters are preset, and the preset multiple groups of edge detection parameters are adopted to perform edge detection on the 18 given template images subjected to assignment, the edge detection results having the highest degree of approximation with the template images are found out, the edge detection parameters used by the edge detection results serve as the optimal parameters, the obtained optimal parameters are adopted to carry out the following items on the to-be-processed image: image segmentation, constructing multiple one-dimensional vectors, carrying out convolution operation and calculating the absolute value, determining the local maximum value, assigning gray value to the local maximum value, substituting pixels on the same position of an original image and performing binarization treatment on the edges of the images subjected to multiple superposition, so that the image subjected to edge detection can be obtained.

Description

technical field [0001] The invention relates to an image processing method, in particular to a multi-angle edge detection method based on Gaussian wavelet one-dimensional peak recognition. Background technique [0002] Images bring humans an image of the thinking world, which is an important way for humans to understand the world. The abrupt and discontinuous structures existing in the image are called edges. Edges often carry rich image information. These edge points constitute the outline of the object, and these outlines are often the places of interest to researchers. It embodies the characteristics of the research target and plays an extremely important role in the subsequent image segmentation, image matching, target recognition, and computer vision. Therefore, How to refine the structurally unstable edges in the actual image into structurally stable edges has become a direction that people have been intensively studying for many years. In decades of research, peopl...

AI Technical Summary

Problems solved by technology

It is suitable for most application environments, but it is difficult to find a suitable decision strategy and evaluate its effectiveness

There is no uniform standard for the selection of edge detection algorithm parameters. Basically, the algorithm parameters are determined based on whether the application meets the criteria.

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