KR-20260066414-A - Image processing method and image processing system

KR20260066414AKR 20260066414 AKR20260066414 AKR 20260066414AKR-20260066414-A

Abstract

An image processing method and image processing system according to one embodiment of the present invention comprises the steps of inputting a reference image and a source image, grouping pixels of the reference image according to gray levels, binarizing the grouped pixels to generate a mask image for each pixel group, extracting pixel location information from the mask image, specifying a histogram of the source image based on a histogram of the reference image, and outputting the source image with the specified histogram as a modified image, wherein the step of specifying the histogram of the source image is performed based on the pixel location information of the mask image.

Inventors

박세혁
박성배
김덕호

Assignees

주식회사 옵츠

Dates

Publication Date: 20260512
Application Date: 20241104

Claims (8)

Step of inputting the reference image and source image; A step of grouping pixels of the above reference image according to gray level; A step of generating a mask image for each pixel group by binarizing grouped pixels; A step of extracting pixel location information from the above mask image; A step of specifying the histogram of the source image based on the histogram of the reference image; and The method includes the step of outputting the source image, for which a histogram is specified, as a modified image; The step of specifying the histogram of the source image above is, An image processing method based on pixel location information of the above mask image.
In paragraph 1, The step of specifying the above histogram is, A step of calculating the histogram of the reference image and the histogram of the source image; A step of calculating a reference cumulative distribution function for the histogram of the reference image and a source cumulative distribution function for the histogram of the source image; A step of generating a specified function by comparing a reference cumulative distribution function and the source cumulative distribution function; and An image processing method comprising the step of converting the gray level of a pixel corresponding to a pixel position of the mask image in the source image based on the above-mentioned specified function.
In paragraph 1, The above pixel location information is, An image processing method comprising absolute coordinates defining the position of a pixel appearing in the above mask image as X1 and Y1.
In paragraph 1, An image processing method further comprising the step of performing a vision inspection based on the above-mentioned deformed image.
An image input section for inputting a reference image and a source image; A grouping unit that groups pixels of the above reference image according to gray level; A mask image generation unit that binarizes grouped pixels to generate a mask image for each pixel group; An extraction unit that extracts pixel location information from the above mask image; A histogram designation unit that designates the histogram of the source image based on the histogram of the reference image; and A modified image output unit that outputs the source image specified in the histogram as a modified image; is included, The above histogram designation unit is, An image processing system that specifies a histogram of a source image based on pixel location information of the mask image.
In paragraph 5, The above histogram designation unit is, An image processing system that calculates a histogram of a reference image and a histogram of a source image, calculates a reference cumulative distribution function for the histogram of the reference image and a source cumulative distribution function for the histogram of the source image, generates a designated function by comparing the reference cumulative distribution function and the source cumulative distribution function, and converts the gray level of a pixel corresponding to a pixel position of the mask image in the source image based on the designated function.
In paragraph 5, The above pixel location information is, An image processing system comprising absolute coordinates defining the position of a pixel appearing in the above mask image as X1 and Y1.
In paragraph 5, An image processing system further comprising a vision inspection unit that performs a vision inspection based on the above-mentioned deformed image.

Description

Image processing method and image processing system The present invention relates to an image processing method and an image processing system. Manufacturing processes in various industrial settings are automated. Products produced through these processes are inspected for defects via visual inspection. However, this type of visual inspection presents a problem in that results are inconsistent depending on the inspector's mental and physical state, consequently leading to reduced product reliability and productivity. Therefore, to overcome the limitations of visual inspection, machine vision technology is being developed to automate defect inspection using mechanical devices. Such machine vision systems can detect product defects by using product images. The aforementioned machine vision technology identifies product defects by referencing a reference image, but errors can occur even due to minute changes in brightness of the image of the product being inspected and input into the inspection system. Therefore, it is desirable for the image of the product being inspected and input into the machine vision system to have the same brightness as the reference image. However, product images are acquired under lighting conditions, and even under the same lighting, it is difficult to maintain a constant amount of light irradiated onto the product due to factors such as changes in the surrounding environment. Consequently, even images of the same product may exhibit subtle differences in brightness; therefore, image processing technology is required to ensure that product images possess a consistent brightness. Histogram specification can be used as an image processing method. Histogram specification is a method that adjusts the gray level distribution of a source image to be identical to the gray level distribution of a reference image. However, unlike continuous signals such as analog images, errors may occur due to quantization errors when performing histogram specification on digital images. Reducing the size or resolution of an image can reduce the error in histogram specification, but reducing the size or resolution of the image may result in the loss of information contained in the image. Therefore, a technology is needed to improve the accuracy of histogram designation by reducing quantization error during the histogram designation process without such information loss. FIG. 1 is a drawing showing an image processing system according to one embodiment of the present invention. FIG. 2 is a flowchart of an image processing method according to one embodiment of the present invention. FIG. 3 is a drawing showing mask images according to gray levels, where FIG. 3(a) shows the first mask image, FIG. 3(b) shows the second mask image, and FIG. 3(c) shows the Nth mask image. FIG. 4 is a flowchart of a partial image generation step according to one embodiment of the present invention. FIG. 5 is a flowchart of a histogram designation step according to one embodiment of the present invention. FIG. 6(a) is a drawing showing a reference image, FIG. 6(b) is a drawing showing a histogram of the image in FIG. 6(a), FIG. 6(c) is a drawing showing a source image, and FIG. 6(d) is a drawing showing a histogram of the image in FIG. 6(c). FIG. 7(a) is a drawing showing a source image corrected through full histogram designation, and FIG. 7(b) is a drawing showing a source image corrected through partial histogram designation. FIG. 8 is a drawing showing an image processing system according to another embodiment of the present invention. FIG. 9 is a flowchart of an image processing method according to another embodiment of the present invention. FIG. 10 is a drawing showing mask images according to gray levels, where FIG. 10(A) is a reference image, FIG. 10(a) is a first mask image, FIG. 10(b) is a second mask image, and FIG. 10(c) is an Nth mask image. FIG. 11 is a flowchart of a histogram designation step according to another embodiment of the present invention. Figure 12 is a diagram showing the histogram of a reference image and the location information of pixels appearing in a mask image. Figure 13 is a diagram showing a reference image, a source image, and histograms for each image. FIG. 14 is a diagram illustrating exemplary modified images of a source image. The objects, specific advantages, and novel features of the present invention will become more apparent from the following detailed description and preferred embodiments in conjunction with the accompanying drawings. It should be noted that in assigning reference numerals to the components of each drawing in this specification, identical components are assigned the same number whenever possible, even if they are shown in different drawings. Furthermore, in describing the present invention, detailed descriptions of related prior art are omitted if it is determined that such detailed descriptions would unnecessarily obscure the essence of the invention. Hereinafter, pref