Method and system for processing adaptive images

Abstract

The invention relates to a method for processing adaptive images. The method includes following steps: step A, obtaining interested area images according to original gray scale images; step B, calculating integral images of the interested area images and the original gray scale images; step C, adaptively adjusting size of optimization windows of each pixel point in an interested area according to the integral images of the interested area images and the original gray scale images; step D, calculating gray scale thresholds of each pixel point in the interested area and binarizing by using the local threshold method according to the windows; and step E, outputting binarized images. The invention further relates to a system for processing the adaptive images. By the method and the system for processing the adaptive images, the size of the windows of each pixel point in the interested area can be adaptively adjusted, and obtaining of gray scale thresholds with different sizes and with prospects having obvious differences in brightness / contrast in the interested area can be realized, so that the local threshold method is expanded to be suitable for the field of processing of any other images except for text images.

Description

【Technical field】 [0001] The invention relates to the field of image analysis, in particular to an adaptive image processing method and an adaptive image processing system. 【Background technique】 [0002] One of the most straightforward and effective methods for image classification or segmentation is the image grayscale thresholding method. This method compares the relationship between the gray level g(x, y) at each pixel point (x, y) and the pixel gray level threshold T(x, y). For the dark foreground, the thresholding is: if g(x, y)<T(x, y), then the pixel (x, y) is set as the foreground, otherwise it is set as the background; for the bright foreground, the thresholding is : If g(x, y)>T(x, y), the pixel (x, y) is set as the foreground, otherwise it is set as the background. If the threshold T(x, y) does not depend on (x, y), it is called global thresholding, otherwise it is called local thresholding. [0003] The local threshold method was proposed by Niblack in ...

AI Technical Summary

Problems solved by technology

But neither window can detect both areas at the same time

[0013] Therefore, the local gray threshold method with a fixed window size cannot detect foregrounds with large size changes (for example, there are larger objects in the foreground and smaller objects), which is why local thresholds are currently mostly used for text image segmentation. , because the size of the foreground of the text image is basically the same

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