Image segmentation method based on random walk combining local structure information around pixel points

Abstract

The present invention provides an image segmentation method based on random walk combining local structure information around pixel points. The method is good in image segmentation effect and high inaccuracy, and has high accuracy special for the segmentation problem of images with complex local structures. The image segmentation method based on random walk combining local structure information around pixel points belongs to fields of image processing and image segmentation algorithm. The segmentation misalignment problem of a general image segmentation method caused by complex local information is solved. The image segmentation method based on random walk combining local structure information around pixel points is suitable for segmentation of general images with clear contours and obvious structures, is suitable for segmentation of images with complex local structure information, and has a good effect on a segmentation object with an elongated portion.

Description

technical field [0001] The invention specifically relates to an image segmentation method based on random walk and local structure information around pixels. Background technique [0002] The deficiency that existing technology exists is: [0003] (1) The existing image segmentation algorithms have large segmentation limitations, limited scope of application, and poor segmentation results for different objects. For example, the existing random walk image segmentation algorithm is only suitable for segmenting images with obvious objects and clear outlines, but it is not effective for image segmentation with messy, complex, and complex edge structure and texture information. [0004] (2) When the existing random walk image segmentation algorithm is used to segment images with rich local structure information, images with unobvious object boundary features and images with complex texture information, the segmentation effect of the image segmentation is not good, and the segmen...

AI Technical Summary

Problems solved by technology

[0003] (1) The existing image segmentation algorithm has large segmentation limitations, limited scope of application, and poor segmentation effect on different objects

For example, the existing random walk image segmentation algorithm is only suitable for segmenting images with obvious objects and clear outlines, but the segmentation effect is not good for images with messy, complex, and complex edge structure texture information.

[0004] (2) When the existing random walk image segmentation algorithm is used to segment images with rich local structure information, images with unobvious object boundary features and images with complex texture information, the segmentation effect of the image segmentation is not good, and the segmentation results are often Inaccurate

[0005] (3) The existing image segmentation algorithm, the segmentation result of the image of the object to be segmented that contains slender and pointed parts in the image to be segmented is inaccurate, and cannot satisfy the image segmentation that contains slender parts.

The existing random walk image segmentation algorithm does not consider adding auxiliary labels, so when calculating the probability of each pixel reaching the label point, the basis for the classification label of the pixel is not sufficient, resulting in poor image segmentation effect and low robustness

Especially when segmenting images containing complex and elongated segmentation objects, the traditional random walk algorithm is prone to under-segmentation

[0008] (2) The existing image segmentation technology only emphasizes and considers the color and grayscale information of the pixels of the image in the graph theory, but does not consider the local structure information around the pixels in the image, which leads to the segmentation containing Segmentation does not work well with textured images rich in structural information

Especially for segmentation objects with complex local structure at the contour edge, the segmentation results are often inaccurate

The texture image segmentation results obtained by the existing random walk image segmentation technology are not good enough

[0009] (3) When the segmentation object contains slender parts, the existing random walk image segmentation technology segmentation algorithm seed label setting fails to meet its precise segmentation requirements

[0012] 1. The traditional random walk image segmentation algorithm originally set the classification label seed label in a single form, and the basis for the classification label of the pixel is not sufficient, resulting in inaccurate image segmentation results, and the specific performance cannot meet the requirements for segmentation objects containing slender parts. Accurate segmentation requirements;

[0013] 2. When the existing image segmentation technology describes the similarity between pixel nodes in graph theory, it is only based on a single pixel color or grayscale information, and does not fully consider the structural information around the pixel point. Therefore, the existing random walk Image segmentation methods cannot accurately segment images with complex edge structures

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