Edge detection method of arbitrary angle

Abstract

An edge detection method of arbitrary angle comprises the following steps of constructing an edge detection angle interval boundary; constructing an arbitrary angle in an edge detection angle interval; convoluting the first derivative of Gaussian function and some pixel straight lines, and respectively obtaining the absolute value and the local maximum value of the convolutional result; assigningthe local maximum a gray value, and setting the gray value of other non-local maximum pixels to be 0; replacing an image pixel having a gray value with an original image pixel; superposing several images obtained from different edge detection angles in gray scale; and according to the actual requirements of edge images, setting binary thresholds for the gray scale of the images superposed many times, and binarizing the images according to the thresholds to obtain the required edges. The invention provides an algorithm capable of detecting the edge of any angle, and reduces the complexity of the image edge detection algorithm.

Description

technical field [0001] The invention relates to an image processing method, in particular to an edge detection method at any angle. 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 convert an image with unclear outlines into a clear edge image has become a direction that people have been studying deeply for many years. In decades of research, people have continuously introduced mathematical methods to extract and interpret image edges....

AI Technical Summary

Problems solved by technology

However, when the image is rotated and the coordinates are rotated, the image will be interpolated, resulting in a change in the grayscale information of the image. Therefore, after the image is rotated and the coordinates are rotated, the edge recognition of the image cannot guarantee the accuracy of the image edge, and the edge recognition Finally, it is necessary to rotate the edge image back to its original position according to the angle of rotation, which will cause the change of the edge image information again.

In addition, rotating the image and rotating the coordinates will cause the image size to change and cause image boundary problems, which will increase the difficulty of image processing

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