Image segmentation method and device for background detection

Abstract

The invention discloses an image segmentation method based on background detection. The method comprises the following steps: acquiring N silicon wafer images shot by X-ray; respectively carrying outcolor image segmentation on the N silicon wafer images, screening out a single-channel image with the maximum contrast ratio from the N silicon wafer images, and performing further graying to obtain Nfirst target images; carrying out global threshold processing on the N first target images, carrying out hole filling until no gap exists in pixels, and obtaining N second target images; constructinga connected domain of the N second target images according to a set pixel adjacency relation, and further screening out a silicon wafer region by utilizing the area characteristics of the silicon wafer to obtain N third target images; and performing morphological opening operation on the N third target images, reducing the definition domain of the N third target images to a set definition domain,and performing matting processing to obtain N target images. According to the method and device, the original image is subjected to series processing, so that the feature number is reduced on the premise of ensuring the image quality, and the target image is quickly segmented from the original image.

Description

technical field [0001] The invention relates to the field of machine vision detection, in particular to an image segmentation method and device for background detection. Background technique [0002] Image segmentation refers to the technology and process of extracting an image into a region of interest. Among them, typical image segmentation methods include threshold method, edge detection method, region method, clustering method and image segmentation methods combined with specific theories, but these methods always have some shortcomings when targeting different images. [0003] For example: the threshold algorithm is the most classic and commonly used method, and its advantages are simple implementation and fast operation speed. There are many image segmentation methods combined with the threshold algorithm, but it is difficult to deal with the situation that contains multiple foreground objects; the current edge detection method cannot detect the edge of the foreground...

AI Technical Summary

Problems solved by technology

There are many image segmentation methods combined with the threshold algorithm, but it is difficult to deal with the situation that contains multiple foreground objects; the current edge detection method cannot detect the edge of the foreground of the image well and make it continuous, and the edge exists between the background and the target. It depends on the edge operator, so it is difficult to be used in image segmentation; the region method mainly includes the region growing method and the split and merge method, and the split and merge method is considered to be a very promising segmentation method. The segmentation effect is better, but the algorithm is more complex, the amount of calculation is large, and the division may destroy the boundary of the region; the clustering method regards image segmentation as a clustering problem, and the clustering algorithm is widely used in image segmentation, but it needs to specify the clustering in advance. Number of classes, area size, and area homogeneity criteria

[0004] Therefore, based on the fact that the pixels of each point in the image background may have a large gap, it is difficult to directly use the threshold method to distinguish whether a point belongs to the background pixel, which will affect the accuracy of image segmentation.

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