CN111462144A - Image segmentation method for rapidly inhibiting image fuzzy boundary based on rough set - Google Patents
Image segmentation method for rapidly inhibiting image fuzzy boundary based on rough set Download PDFInfo
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Abstract
The invention belongs to the technical field of image processing, and particularly relates to an image segmentation method for rapidly inhibiting an image fuzzy boundary based on a rough set, which comprises the following steps: the method comprises the following steps: setting an image segmentation threshold value; step two: setting the threshold values of the retained data among the image threshold values for the second time; step three: refining rough set data generated after segmentation; step four: carrying out check type segmentation on the rough set data brought into the segmentation completion data by an initial segmentation method, and searching for data errors; step five: continuously substituting and dividing according to the method of the step four; step six: after the segmentation is finished, data which is substituted from the rough set is extracted from the segmented data, the image is segmented through various methods, the generated rough set data is subjected to substitution processing, boundary blurring is reduced through similar processing operation, and the processed image is extracted and returned, so that the purpose of rapidly segmenting the image is achieved, and the restraining speed of the fuzzy boundary of the image is improved.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an image segmentation method for rapidly inhibiting fuzzy boundaries of an image based on a rough set.
Background
The image segmentation is one of the most basic and important fields in the image processing and low-level vision in the computer vision field, is a premise of pattern recognition and target detection, and has important practical value. However, under the condition that the boundary of the target in the image is fuzzy, the gray difference between the target and the background is not large, the difficulty of target extraction is increased, the processing of subsequent tasks is influenced, and has certain difficulty in practical application.
Image segmentation is a main problem of image processing, is an important step of subsequent processing, belongs to the problem of low-level vision in the field of computer vision, and currently, for image segmentation, a lot of achievements and conclusions exist, but no universal method is suitable for all images so far. Image segmentation methods are various, and according to the research of most researchers, image segmentation methods are divided into the following types of GUO value segmentation methods, clustering-based segmentation methods, region-based segmentation methods, edge-based segmentation methods, morphological watershed-based segmentation methods and other types of image segmentation methods.
In the prior art, when an image is segmented, the generated uncertain factors can be forcibly summarized into an instruction set generated by a user, and because the data range of the instruction set is set before operation, the uncertain factors generated during operation can influence the segmentation of the image, so that the segmentation of the image in the prior art is not suitable for all images, and the use range is limited.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above and/or other problems occurring in the conventional image segmentation methods.
Therefore, the present invention aims to provide an image segmentation method for rapidly suppressing an image blurred boundary based on a rough set, which can segment an image based on the rough set, effectively process the generated uncertain factors, substitute the uncertain factors into the determined data, perform reciprocal calculation until the generated uncertain factors are processed, and perform conventional segmentation on the whole image through a known algorithm, so as to achieve the purpose of rapidly suppressing the image blurred boundary.
To solve the above technical problem, according to an aspect of the present invention, the present invention provides the following technical solutions:
an image segmentation method for rapidly inhibiting image fuzzy boundaries based on a rough set comprises the following steps:
the method comprises the following steps: setting an image segmentation threshold, roughly segmenting data in the threshold, and reserving marginalized images;
step two: performing secondary threshold value setting on data between reserved image threshold values, and performing refined data segmentation through the set image segmentation threshold values;
step three: refining rough set data generated after segmentation, and substituting the data set into the segmented data;
step four: carrying out check type segmentation on the rough set data brought into the segmentation completion data by an initial segmentation method, and searching for data errors;
step five: continuously performing substitution segmentation according to the method of the fourth step until the rough set data generated in the third step cannot be substituted or the substitution is finished;
step six: and after the segmentation is finished, extracting the data substituted from the rough set from the segmented data, and combining the data again to obtain the segmented image with the fuzzy boundary of the image.
As a preferable solution of the image segmentation method for rapidly suppressing the blurred boundary of the image based on the rough set, according to the present invention, wherein: the segmentation method in the first step is a threshold segmentation method, the marginalized image in the first step is specifically an image within a threshold set point of an image fuzzy boundary, and the retained data of the image is data which does not affect normal threshold segmentation.
As a preferable solution of the image segmentation method for rapidly suppressing the blurred boundary of the image based on the rough set, according to the present invention, wherein: and setting a secondary threshold in the second step based on a region growing segmentation method, wherein the refined data segmentation in the second step is specifically a residual region generated after the region growing data is segmented, and the region is characterized in that the image cannot be segmented through a region segmentation algorithm.
As a preferable solution of the image segmentation method for rapidly suppressing the blurred boundary of the image based on the rough set, according to the present invention, wherein: the specific method for substituting the data set in the step three into the segmented data is as follows:
the method comprises the following steps: carrying out region division on the generated rough set data;
step two: finding out the image data which is divided according to the divided region, and substituting the rough set data in the region into the image data which is divided;
step three: and C, performing segmentation again according to the segmentation method in the step II, and optimizing the segmentation data.
As a preferable solution of the image segmentation method for rapidly suppressing the blurred boundary of the image based on the rough set, according to the present invention, wherein: the specific method of the detection method and the optimization error reporting in the fourth step and the fifth step is to monitor whether the optimized data is error reported, the generated error reporting information integrally transfers the whole substituted rough set information to the similar image data which is segmented, and the secondary substitution and optimization are carried out until no error is reported.
As a preferable solution of the image segmentation method for rapidly suppressing the blurred boundary of the image based on the rough set, according to the present invention, wherein: and the combination method in the sixth step is a specific data extraction method, specifically, a data set is selected, extraction is carried out according to the key words, and then the extracted data is restored according to the same steps.
Compared with the prior art: in the prior art, the image is segmented by a plurality of methods, generated rough set data is subjected to substitution processing, boundary blurring is reduced by similar processing operation, and the processed image is extracted and returned, so that the purpose of rapidly segmenting the image is achieved, and the inhibition speed of the fuzzy boundary of the image is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail with reference to the accompanying drawings and detailed embodiments, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise. Wherein:
fig. 1 is a schematic flow structure diagram of an image segmentation method for rapidly suppressing an image blurred boundary based on a rough set according to the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described herein, and it will be apparent to those of ordinary skill in the art that the present invention may be practiced without departing from the spirit and scope of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Next, the present invention will be described in detail with reference to the drawings, wherein for convenience of illustration, the cross-sectional view of the device structure is not enlarged partially according to the general scale, and the drawings are only examples, which should not limit the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The invention provides an image segmentation method for rapidly inhibiting image fuzzy boundaries based on a rough set, referring to fig. 1, the image segmentation steps are as follows:
the method comprises the following steps: setting an image segmentation threshold, roughly segmenting data in the threshold, and reserving marginalized images;
step two: performing secondary threshold value setting on data between reserved image threshold values, and performing refined data segmentation through the set image segmentation threshold values;
step three: refining rough set data generated after segmentation, and substituting the data set into the segmented data;
step four: carrying out check type segmentation on the rough set data brought into the segmentation completion data by an initial segmentation method, and searching for data errors;
step five: continuously performing substitution segmentation according to the method of the fourth step until the rough set data generated in the third step cannot be substituted or the substitution is finished;
step six: and after the segmentation is finished, extracting the data substituted from the rough set from the segmented data, and combining the data again to obtain the segmented image with the fuzzy boundary of the image.
Referring again to fig. 1, the segmentation method in the first step is a threshold segmentation method, the marginalized image in the first step is specifically an image within a threshold set point of an image blur boundary, the remaining data of the image is data that does not affect normal threshold segmentation, and the gray threshold segmentation method is one of the most commonly used parallel region techniques, and is the most applied one in image segmentation. The thresholding method is actually the following transformation of the input image f to the output image g:
wherein T is a threshold value; for an image element of an object, g (i, j) is 1, and for an image element of a background, g (i, j) is 0.
It can be seen that the key to the threshold segmentation algorithm is to determine the threshold value, and if a suitable threshold value can be determined, the image can be accurately segmented. After the threshold is determined, the threshold is compared with the gray value of the pixel point and pixel segmentation can be carried out on each pixel in parallel, and the segmentation result is directly given to an image area.
Referring to fig. 1 again, the secondary threshold setting in the second step is based on a region growing segmentation method, the refined data segmentation in the second step is specifically a residual region generated after the region growing data is divided, the region is characterized in that the image cannot be segmented by a region segmentation algorithm, the region growing and splitting combination method is two typical serial region techniques, and the processing of the subsequent steps of the segmentation process is determined by judging according to the result of the previous step.
(1) Region growing
The basic idea of region growing is to group pixels with similar properties together to form a region. Specifically, a seed pixel is found for each region to be segmented as a starting point for growth, and then pixels (determined according to a certain predetermined growth or similarity criterion) with the same or similar properties as the seed pixels in the neighborhood around the seed pixels are merged into the region where the seed pixels are located. The above process continues with these new pixels as new seed pixels until no more pixels that satisfy the condition can be included. Such a region grows.
(2) Region split merging
The region growing starts from a certain or some pixel points, and finally the whole region is obtained, so that the target extraction is realized. Split merging is almost the reverse process of region growing: and starting from the whole image, continuously splitting to obtain each sub-region, and then combining the foreground regions to realize target extraction. The assumption of split merging is that for an image, the foreground region is composed of a number of interconnected pixels, so if an image is split to the pixel level, it can be determined whether the pixel is a foreground pixel. And when all the pixel points or the sub-areas are judged, combining the foreground areas or the pixels to obtain the foreground target.
Referring to fig. 1 again, the specific method for substituting the data set into the segmented data in the third step is as follows:
the method comprises the following steps: carrying out region division on the generated rough set data;
step two: finding out the image data which is divided according to the divided region, and substituting the rough set data in the region into the image data which is divided;
step three: and C, performing segmentation again according to the segmentation method in the step II, and optimizing the segmentation data.
Referring to fig. 1 again, the specific method for implementing the inspection method and the optimization error reporting in the fourth and fifth steps is to monitor whether the optimized data is error-reported, and the generated error-reporting information integrally transfers the whole substituted rough set information to the similar image data which is completely segmented, and then secondary substitution and optimization are performed until no error is reported.
Referring to fig. 1 again, the combination method in the sixth step is a specific data extraction method, specifically, a data set is selected, extraction is performed according to the keywords, and then the extracted data is restored according to the same steps.
While the invention has been described above with reference to an embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the various features of the disclosed embodiments of the invention may be used in any combination, provided that no structural conflict exists, and the combinations are not exhaustively described in this specification merely for the sake of brevity and resource conservation. Therefore, it is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (6)
1. An image segmentation method for rapidly inhibiting image fuzzy boundaries based on a rough set is characterized in that: the image segmentation step is as follows:
the method comprises the following steps: setting an image segmentation threshold, roughly segmenting data in the threshold, and reserving marginalized images;
step two: performing secondary threshold value setting on data between reserved image threshold values, and performing refined data segmentation through the set image segmentation threshold values;
step three: refining rough set data generated after segmentation, and substituting the data set into the segmented data;
step four: carrying out check type segmentation on the rough set data brought into the segmentation completion data by an initial segmentation method, and searching for data errors;
step five: continuously performing substitution segmentation according to the method of the fourth step until the rough set data generated in the third step cannot be substituted or the substitution is finished;
step six: and after the segmentation is finished, extracting the data substituted from the rough set from the segmented data, and combining the data again to obtain the segmented image with the fuzzy boundary of the image.
2. The image segmentation method for rapidly suppressing the blurred boundary of the image based on the rough set as claimed in claim 1, wherein: the segmentation method in the first step is a threshold segmentation method, the marginalized image in the first step is specifically an image within a threshold set point of an image fuzzy boundary, and the retained data of the image is data which does not affect normal threshold segmentation.
3. The image segmentation method for rapidly suppressing the blurred boundary of the image based on the rough set as claimed in claim 1, wherein: and setting a secondary threshold in the second step based on a region growing segmentation method, wherein the refined data segmentation in the second step is specifically a residual region generated after the region growing data is segmented, and the region is characterized in that the image cannot be segmented through a region segmentation algorithm.
4. The image segmentation method for rapidly suppressing the blurred boundary of the image based on the rough set as claimed in claim 1, wherein: the specific method for substituting the data set in the step three into the segmented data is as follows:
the method comprises the following steps: carrying out region division on the generated rough set data;
step two: finding out the image data which is divided according to the divided region, and substituting the rough set data in the region into the image data which is divided;
step three: and C, performing segmentation again according to the segmentation method in the step II, and optimizing the segmentation data.
5. The image segmentation method for rapidly suppressing the blurred boundary of the image based on the rough set as claimed in claim 1, wherein: the specific method of the detection method and the optimization error reporting in the fourth step and the fifth step is to monitor whether the optimized data is error reported, the generated error reporting information integrally transfers the whole substituted rough set information to the similar image data which is segmented, and the secondary substitution and optimization are carried out until no error is reported.
6. The image segmentation method for rapidly suppressing the blurred boundary of the image based on the rough set as claimed in claim 1, wherein: and the combination method in the sixth step is a specific data extraction method, specifically, a data set is selected, extraction is carried out according to the key words, and then the extracted data is restored according to the same steps.
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