CN100580694C - A method for fast multi-threshold segmentation of grayscale images - Google Patents

Abstract

The invention relates to the technical field of digital image processing, and discloses a method for fast multi-threshold segmentation of grayscale images. The invention firstly reads the grayscale image and calculates its grayscale histogram. Smooth the obtained histogram to obtain a smoothed histogram, select several maximum peak points in the smoothed histogram, and obtain their corresponding gray value arrays. The two ends of the gray value array are extended, so that the addition of two adjacent elements in the extended array and dividing by 2 can obtain the threshold value required for multi-threshold segmentation, and then realize multi-threshold segmentation.

Description

A method for fast multi-threshold segmentation of grayscale images

technical field

The invention relates to a digital image processing technology, in particular to a method for fast multi-threshold segmentation of grayscale images.

Background technique

Segmentation technology is the basis of image pattern recognition, has a wide range of uses, and is a hot research topic in scientific research and production. Multi-threshold segmentation can be used to identify objects with different colors or gray levels or to identify objects whose gray levels vary within a narrow range. For example, in the side-scan sonar target recognition task, the shadow can be used as a feature of the target, but because the echo intensity of the seabed background is similar to the shadow area, it is generally difficult for the binary segmentation algorithm to extract the shadow area. However, many current multi-threshold segmentation techniques are difficult to meet the real-time requirements because of the large amount of calculation.

Contents of the invention

The invention provides a method for rapid multi-threshold segmentation of gray-scale images, which can realize multi-threshold segmentation of gray-scale images and is easy to meet real-time requirements.

The purpose of the present invention is achieved like this:

(1) Read the grayscale image to be segmented and store it in a two-dimensional image array A, the grayscale value of each pixel is in the range of 0 to 255;

(2) traverse the image array A to obtain the grayscale histogram of the image;

(3) Make the grayscale histogram pass through a low-pass filter to obtain a smooth histogram;

(4) Open up two arrays, the peak value array and the gray scale array, and initialize both arrays to 0. The peak value array and the gray scale array respectively store the peak value of the histogram and the gray scale corresponding to the peak value, and the subscripts of the peak value array and the gray scale array The same elements are in one-to-one correspondence;

(5) Traversing the smooth histogram, storing the newly obtained peak value and the gray value corresponding to the peak value into the end of the peak value array and the gray value array respectively, and then sorting the peak value array and the gray value array;

(6) utilize the grayscale array that step (5) obtains to determine each threshold of multi-threshold segmentation;

(7) traverse the image array A, each element in A is compared with each threshold obtained in step (6), obtain a label, store in the corresponding position of the two-dimensional label array B, realize the fast multi-threshold segmentation of the grayscale image.

The present invention can also include such features:

1. The size of the peak array and grayscale array described in step (4) is equal to the preset threshold number n.

2. In the sorting described in step (5), the peak array is sorted by the direct method, and the grayscale array is changed according to the change of the peak array, so that the grayscale value corresponding to the peak value remains unchanged.

3. The method for determining each threshold of the multi-threshold segmentation described in step (6) is to arrange the grayscale array in descending order, then take out the first n-1 elements in the grayscale array, and expand the left and right sides of the grayscale array. The extended element of is 0, the extended element on the right is 255, and a new one-dimensional array C is obtained. Starting from the first element on the left of C, the element is added to the adjacent element on the right and divided by 2 to obtain the required threshold. Array D, this process continues until the nth element of array C, and each element in array D is the required n thresholds.

The present invention is based on the multi-threshold segmentation of the gray histogram. Due to the existence of multiple targets, the grayscale histogram has multiple peaks, so the grayscale corresponding to the midpoint of two adjacent peaks is a reasonable estimate as the threshold value, which is exactly the method for fast multi-threshold segmentation of grayscale images. core idea.

Experiments show that, for a grayscale image with a size of 330×244, under the condition that a CPU is AMD1600+, the operating system is Windows2000, and the programming environment is VC6.0, 2-threshold segmentation, 3-threshold segmentation, 4-threshold Segmentation and 5-threshold segmentation take 10 milliseconds in total, which can fully meet the real-time requirements. At present, other multi-threshold algorithms do not provide data on the execution time of the algorithm.

Description of drawings

Fig. 1 is a flowchart of the present invention.

Fig. 2 is the array C described in step 6 of the present invention.

Fig. 3 is the array D described in step 6 of the present invention.

Figure 4 is a grayscale image with a size of 330×244 to be segmented.

Fig. 5 is the binary segmentation result of Fig. 4 by Otsu method.

Fig. 6 is the segmentation result of the four thresholds in Fig. 4 by using the present invention.

Detailed ways

The present invention will be further described below with examples in conjunction with the accompanying drawings.

1. Read the grayscale image to be segmented (Figure 4), and store it in a two-dimensional image array A, the grayscale value of each pixel is in the range of 0-255.

2. Traverse the image array A to get the grayscale histogram of the image.

3. Pass the grayscale histogram through a low-pass filter to obtain a smooth histogram.

4. Create two arrays—the peak array and the grayscale array, and initialize both arrays to 0. The peak value array and the gray level array respectively store the peak value of the histogram and the gray level corresponding to the peak value. The elements with the same subscript of the peak value array and the gray level array are in one-to-one correspondence. The size of the peak value array and the gray level array should be equal to the preset threshold number n.

5. Traversing the smoothing histogram, storing the newly obtained peak value and the corresponding gray value to the end of the peak value array and the gray value array respectively, and then sorting the peak value array and the gray value array. The peak array is sorted by the direct method, and the grayscale array is changed according to the change of the peak array, so that the grayscale value corresponding to the peak value remains unchanged.

6. Arrange the grayscale array in descending order, then take out the first n-1 elements in the grayscale array, and expand the left and right sides of the n-1 elements, the left expansion element is 0, and the right expansion element is 255, Get a new one-dimensional array C (as shown in Figure 2), which contains n+1 elements, starting from the first element on the left, adding the element to the adjacent element on the right and dividing by 2 to obtain the requirement The threshold array D (as shown in Figure 3), this process continues until the nth element of the array C. The array D contains n elements in total, and these n elements are the required n thresholds.

7. The image array A is traversed, and each element in A is compared with each threshold in D to obtain a label, which is stored in the corresponding position of the two-dimensional label array B, thereby realizing fast multi-threshold segmentation of grayscale images. Among them, each element in D is D[0],...D[n-1], when the element in A is less than or equal to D[k] (k=0, 1...n-1), then in B The corresponding position is given the label k, and when the element in A is greater than D[n-1], the corresponding position in B is given the label n, so as to realize the multi-threshold segmentation of the image.

Claims (3)

1. one kind is used for the method that the gray level image rapid multi-threshold value is cut apart, and it is characterized in that comprising the steps:

(1) read gray level image to be split, and it is deposited among the two dimensional image array A, each gray values of pixel points is all in 0～255 scope;

(2) traversing graph obtains the grey level histogram of image as array A;

(3) make grey level histogram pass through a low-pass filter, obtain smoothed histogram;

(4) open up two arrays of peak value array and gray scale array, and two arrays all are initialized as 0, peak value array and grey group are preserved the gray scale of histogram peak and peak value correspondence respectively, and the element that the peak value array is identical with the gray scale array index is one to one;

(5) traversal smoothed histogram deposits the gray scale that newly obtains peak value and peak value correspondence in peak value array and gray scale array last respectively, then with peak value array and gray scale array sort;

(6) the gray scale array is carried out descending sort, take out preceding n-1 element in the gray scale array then, this n-1 element the right and left expanded, the extensible element on the left side is 0, the extensible element on the right is 255, obtain a new one-dimension array C, this array comprises n+1 element, from the first left element, the adjacent element addition of this element and its right just obtains the threshold value array D that requires divided by 2 again, this process lasts till n the element of array C always, comprises n element among the array D altogether, and this n element is exactly a n threshold value of requirement;

(7) traversing graph is as array A, and each threshold ratio that obtains in each element and the step (6) among the A obtains a label, deposits the corresponding position of two-dimentional label array B in, realizes that the rapid multi-threshold value of gray level image is cut apart.

2. according to claim 1ly be used for the method that the gray level image rapid multi-threshold value is cut apart, it is characterized in that: peak value array described in the step (4) and gray scale array size equal the preset threshold value number n.

3. according to claim 1 and 2ly be used for the method that the gray level image rapid multi-threshold value is cut apart, it is characterized in that: the ordering described in the step (5), the peak value array is by the direct method ordering, and the gray scale array is made corresponding the change according to the variation of peak value array, make peak value corresponding gray scale value constant.

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