CN111462144A - Image segmentation method for rapidly inhibiting image fuzzy boundary based on rough set - Google Patents
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Abstract
Translated fromChinese
Description
Translated fromChinese技术领域technical field
本发明涉及图像处理技术领域，具体为一种基于粗糙集的快速抑制图像模糊边界的图像分割方法。The invention relates to the technical field of image processing, in particular to a rough set-based image segmentation method for rapidly suppressing blurred boundaries of images.
背景技术Background technique
图像分割是图像处理与计算机视觉领域低层次视觉中最为基础和重要的领域之一，它是模式识别和目标检测的前提，具有重要的实际价值。但在图像中目标边界模糊的情况下，目标与背景之间的灰度差异并不大，增加了目标提取的难度，影响了后续任务的处理，在实际应用中貝有一定的难度。Image segmentation is one of the most basic and important fields of low-level vision in the field of image processing and computer vision. It is the premise of pattern recognition and target detection, and has important practical value. However, when the target boundary in the image is blurred, the grayscale difference between the target and the background is not large, which increases the difficulty of target extraction and affects the processing of subsequent tasks, which is difficult in practical applications.
图像分割是图像处理的主要问题，是后续处理的重要步骤，属于计算机视觉领域低层次视觉中的问题目前，对于图像的分割，已经有相当多的成果和结论，但是至今都没有一个通用的方法适用于所有图像。图像分割的方法种类繁多，我们根据大多数研究者的研究，把图像分割方法分为如下几类國值分割方法、基于聚类的分割方法、基于区域的分割方法、基于边缘的分割方法、基于形态学分水岭的分割方法以及其他类型的图像分割方法。Image segmentation is the main problem of image processing and an important step in subsequent processing. It belongs to the problem of low-level vision in the field of computer vision. At present, there have been quite a lot of achievements and conclusions for image segmentation, but so far there is no general method. Works with all images. There are many kinds of image segmentation methods. According to the research of most researchers, we divide image segmentation methods into the following categories: threshold segmentation methods, cluster-based segmentation methods, region-based segmentation methods, edge-based segmentation methods, and segmentation methods based on edges. Segmentation methods for morphological watersheds and other types of image segmentation.
现有的方法中对图像进行分割时，产生的不确定性因素会被强制的归纳到摸个产生的指令集中，由于该指令集的数据范围在操作前已经被设定，导致在操作时产生的不确定因素会影响图像的分割，造成现有技术对图像进行分割并不适用于所有的图像，造成使用的范围受限。In the existing method, when the image is segmented, the generated uncertainty factors will be forcibly summarized into the generated instruction set. Since the data range of the instruction set has been set before the operation, it will be generated during the operation. The uncertain factors will affect the segmentation of images, resulting in that the prior art segmentation of images is not applicable to all images, resulting in limited scope of use.
发明内容SUMMARY OF THE INVENTION
本部分的目的在于概述本发明的实施方式的一些方面以及简要介绍一些较佳实施方式。在本部分以及本申请的说明书摘要和发明名称中可能会做些简化或省略以避免使本部分、说明书摘要和发明名称的目的模糊，而这种简化或省略不能用于限制本发明的范围。The purpose of this section is to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the application to avoid obscuring the purpose of this section, abstract and title, and such simplifications or omissions may not be used to limit the scope of the invention.
鉴于上述和/或现有图像分割方法中存在的问题，提出了本发明。In view of the above and/or problems existing in existing image segmentation methods, the present invention is proposed.
因此，本发明的目的是提供一种基于粗糙集的快速抑制图像模糊边界的图像分割方法，能够基于粗糙集对图像进行分割，对产生的不确定因素进行有效的处理，再将不确定性因素代入到已经确定的数据中，往复的计算，直至将产生的不确定性因素处理完毕，再通过已知算法对整个图像进行常规的分割，达到快速抑制图像模糊边界的目的。Therefore, the purpose of the present invention is to provide an image segmentation method based on rough sets for quickly suppressing blurred boundaries of images, which can segment images based on rough sets, effectively process the generated uncertain factors, and then divide the uncertain factors into Substitute into the determined data, and perform back-and-forth calculations until the generated uncertainties are processed, and then routinely segment the entire image through a known algorithm to quickly suppress the blurred boundary of the image.
为解决上述技术问题，根据本发明的一个方面，本发明提供了如下技术方案：In order to solve the above-mentioned technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:
一种基于粗糙集的快速抑制图像模糊边界的图像分割方法，该图像分割步骤如下：An image segmentation method based on rough set to quickly suppress the blurred boundary of the image. The image segmentation steps are as follows:
步骤一：设定图像分割阀值，对阀值内的数据进行大致分割，对边缘化的图像进行保留；Step 1: Set the image segmentation threshold, roughly segment the data within the threshold, and retain the marginalized images;
步骤二：对保留的图像阀值之间数据进行二次的阀值设定，再通过设定的图像分割阀值进行细化的数据分割；Step 2: Perform a second threshold setting for the data between the reserved image thresholds, and then perform refined data segmentation through the set image segmentation threshold;
步骤三：细化分割后产生的粗糙集数据，将该数据集代入到分割的数据中；Step 3: Refine the rough set data generated after segmentation, and substitute the data set into the segmented data;
步骤四：带入到分割完成数据中的粗糙集数据通过初始分割方法进行检验式分割，查找数据报错；Step 4: The rough set data brought into the segmented data is segmented by inspection through the initial segmentation method to find data errors;
步骤五：按照步骤四的方法不断的进行代入分割，直至步骤三中产生的粗糙集数据不能代入或代入完毕为止；Step 5: Substitute and segment continuously according to the method of Step 4, until the rough set data generated in Step 3 cannot be substituted or the substitution is completed;
步骤六：分割完毕后将从粗糙集充产生代入的数据从分割完成的数据中抽出，并再次进行组合，即可得到图像模糊边界的分割图像。Step 6: After the segmentation is completed, the data substituted from the rough set is extracted from the segmented data, and combined again to obtain a segmented image with a fuzzy boundary of the image.
作为本发明所述的一种基于粗糙集的快速抑制图像模糊边界的图像分割方法的一种优选方案，其中：所述步骤一中的分割方法为阀值分割法，所述步骤一中的边缘化图像具体为图像模糊边界的阀值设定点内的图像，该图像的保留的数据为不影响正常阀值分割的数据。As a preferred solution of the rough set-based image segmentation method for quickly suppressing blurred boundaries of images in the present invention, the segmentation method in the first step is a threshold segmentation method, and the edge segmentation method in the first step The transformed image is specifically an image within the threshold set point of the image blur boundary, and the retained data of the image is the data that does not affect the normal threshold segmentation.
作为本发明所述的一种基于粗糙集的快速抑制图像模糊边界的图像分割方法的一种优选方案，其中：所述步骤二中二次阀值设定基于区域生长分割方法，所述步骤二中的细化的数据分割具体为区域生长数据划分后产生的剩余区域，该区域的特征为不能通过区域分割算法对图像进行分割。As a preferred solution of the rough set-based image segmentation method for quickly suppressing blurred boundaries of images, wherein: in the second step, the secondary threshold setting is based on the region growing segmentation method, and the second step The refined data segmentation in is specifically the remaining region generated after the region growing data is divided, and the feature of this region is that the image cannot be segmented by the region segmentation algorithm.
作为本发明所述的一种基于粗糙集的快速抑制图像模糊边界的图像分割方法的一种优选方案，其中：所述步骤三中数据集代入到分割的数据中的具体方法如下：As a preferred solution of the rough set-based image segmentation method for quickly suppressing the blurred boundary of the image, wherein: the specific method for substituting the data set into the segmented data in the step 3 is as follows:
步骤一：对产生的粗糙集数据进行区域划分；Step 1: Divide the generated rough set data into regions;
步骤二：根据划分区域，找出已经分割完成的图像数据，将该区域内的粗糙集数据代入到该分割完成的图像数据中；Step 2: According to the divided area, find out the image data that has been segmented, and substitute the rough set data in the area into the segmented image data;
步骤三：根据步骤二的分割方法进行再次分割，对分割数据优化即可。Step 3: Perform the segmentation again according to the segmentation method in Step 2, and optimize the segmentation data.
作为本发明所述的一种基于粗糙集的快速抑制图像模糊边界的图像分割方法的一种优选方案，其中：所述步骤四和步骤五中检验方法和优化报错体现的具体方法为监测优化后的数据是否报错，产生的报错信息将整个代入的粗糙集信息整体的转移到相近的完成分割的图像数据中，进行二次代入并优化，直至无报错即可。As a preferred solution of the rough set-based image segmentation method for quickly suppressing blurred boundaries of images in the present invention, wherein: the specific methods for the inspection method and optimization error reporting in the fourth and fifth steps are the monitoring and optimization Whether the data is wrong or not, the generated error message will transfer the entire substituted rough set information as a whole to the similar segmented image data, and perform secondary substitution and optimization until no error is reported.
作为本发明所述的一种基于粗糙集的快速抑制图像模糊边界的图像分割方法的一种优选方案，其中：所述步骤六中组合方法为特定数据抽取方法，具体为选择数据集，根据关键词进行抽取，再将抽取的数据按照同样的步骤还原即可。As a preferred solution of the rough set-based image segmentation method for quickly suppressing image blurred boundaries, wherein: the combination method in step 6 is a specific data extraction method, specifically selecting a data set, according to the key Words are extracted, and then the extracted data can be restored according to the same steps.
与现有技术相比：现有的方法中对图像进行分割时，产生的不确定性因素会被强制的归纳到摸个产生的指令集中，由于该指令集的数据范围在操作前已经被设定，导致在操作时产生的不确定因素会影响图像的分割，造成现有技术对图像进行分割并不适用于所有的图像，造成使用的范围受限，本申请文件中，通过多种方法对图像进行分割，产生的粗糙集数据进行代入式处理，通过相似处理的操作，减少边界模糊，再将处理后的图像抽出归位，达到图像快速分割的目的，提高图像模糊边界的抑制速度。Compared with the prior art: when the image is segmented in the existing method, the generated uncertainty factors will be forcibly summarized into the generated instruction set, because the data range of the instruction set has been set before the operation. It is determined that the uncertain factors generated during operation will affect the segmentation of images, resulting in that the prior art segmentation of images is not applicable to all images, resulting in limited scope of use. The image is segmented, and the generated rough set data is subjected to substitution processing. Through similar processing operations, the boundary blur is reduced, and then the processed image is extracted and returned to the original position, so as to achieve the purpose of rapid image segmentation and improve the suppression speed of image blur boundary.
附图说明Description of drawings
为了更清楚地说明本发明实施方式的技术方案，下面将结合附图和详细实施方式对本发明进行详细说明，显而易见地，下面描述中的附图仅仅是本发明的一些实施方式，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其它的附图。其中：In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and detailed embodiments. As far as technical personnel are concerned, other drawings can also be obtained based on these drawings without paying creative labor. in:
图1为本发明一种基于粗糙集的快速抑制图像模糊边界的图像分割方法的流程结构示意图。FIG. 1 is a schematic flowchart of a rough set-based image segmentation method for quickly suppressing blurred boundaries of images according to the present invention.
具体实施方式Detailed ways
为使本发明的上述目的、特征和优点能够更加明显易懂，下面结合附图对本发明的具体实施方式做详细的说明。In order to make the above objects, features and advantages of the present invention more clearly understood, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
在下面的描述中阐述了很多具体细节以便于充分理解本发明，但是本发明还可以采用其他不同于在此描述的其它方式来实施，本领域技术人员可以在不违背本发明内涵的情况下做类似推广，因此本发明不受下面公开的具体实施方式的限制。Many specific details are set forth in the following description to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein, and those skilled in the art can do so without departing from the connotation of the present invention. Similar promotion, therefore, the present invention is not limited by the specific embodiments disclosed below.
其次，本发明结合示意图进行详细描述，在详述本发明实施方式时，为便于说明，表示器件结构的剖面图会不依一般比例作局部放大，而且所述示意图只是示例，其在此不应限制本发明保护的范围。此外，在实际制作中应包含长度、宽度及深度的三维空间尺寸。Next, the present invention is described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the cross-sectional views showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not be limited here. The scope of protection of the present invention. In addition, the three-dimensional spatial dimensions of length, width and depth should be included in the actual production.
为使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明的实施方式作进一步地详细描述。In order to make the objectives, technical solutions and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.
本发明提供一种基于粗糙集的快速抑制图像模糊边界的图像分割方法，请参阅图1，该图像分割步骤如下：The present invention provides a rough set-based image segmentation method for quickly suppressing blurred boundaries of images. Please refer to FIG. 1. The image segmentation steps are as follows:
步骤一：设定图像分割阀值，对阀值内的数据进行大致分割，对边缘化的图像进行保留；Step 1: Set the image segmentation threshold, roughly segment the data within the threshold, and retain the marginalized images;
步骤二：对保留的图像阀值之间数据进行二次的阀值设定，再通过设定的图像分割阀值进行细化的数据分割；Step 2: Perform a second threshold setting for the data between the reserved image thresholds, and then perform refined data segmentation through the set image segmentation threshold;
步骤三：细化分割后产生的粗糙集数据，将该数据集代入到分割的数据中；Step 3: Refine the rough set data generated after segmentation, and substitute the data set into the segmented data;
步骤四：带入到分割完成数据中的粗糙集数据通过初始分割方法进行检验式分割，查找数据报错；Step 4: The rough set data brought into the segmented data is segmented by inspection through the initial segmentation method to find data errors;
步骤五：按照步骤四的方法不断的进行代入分割，直至步骤三中产生的粗糙集数据不能代入或代入完毕为止；Step 5: Substitute and segment continuously according to the method of Step 4, until the rough set data generated in Step 3 cannot be substituted or the substitution is completed;
步骤六：分割完毕后将从粗糙集充产生代入的数据从分割完成的数据中抽出，并再次进行组合，即可得到图像模糊边界的分割图像。Step 6: After the segmentation is completed, the data substituted from the rough set is extracted from the segmented data, and combined again to obtain a segmented image with a fuzzy boundary of the image.
请再次参阅图1，所述步骤一中的分割方法为阀值分割法，所述步骤一中的边缘化图像具体为图像模糊边界的阀值设定点内的图像，该图像的保留的数据为不影响正常阀值分割的数据，灰度阈值分割法是一种最常用的并行区域技术，它是图像分割中应用数量最多的一类。阈值分割方法实际上是输入图像f到输出图像g的如下变换：Please refer to FIG. 1 again, the segmentation method in the step 1 is the threshold segmentation method, and the marginalized image in the step 1 is specifically the image within the threshold setting point of the blurred boundary of the image, and the reserved data of the image is In order not to affect the data of normal threshold segmentation, gray threshold segmentation method is one of the most commonly used parallel region techniques, and it is the most widely used category in image segmentation. The threshold segmentation method is actually the following transformation of the input image f to the output image g:
其中，T为阈值；对于物体的图像元素，g(i,j)＝1，对于背景的图像元素，g(i,j)＝0。Among them, T is the threshold; for the image element of the object, g(i,j)=1, and for the image element of the background, g(i,j)=0.
由此可见，阈值分割算法的关键是确定阈值，如果能确定一个适合的阈值就可准确地将图像分割开来。阈值确定后，阈值与像素点的灰度值比较和像素分割可对各像素并行地进行，分割的结果直接给出图像区域。It can be seen that the key of the threshold segmentation algorithm is to determine the threshold. If a suitable threshold can be determined, the image can be accurately segmented. After the threshold is determined, the comparison between the threshold and the gray value of the pixel point and the pixel segmentation can be performed in parallel for each pixel, and the segmentation result directly gives the image area.
请再次参阅图1，所述步骤二中二次阀值设定基于区域生长分割方法，所述步骤二中的细化的数据分割具体为区域生长数据划分后产生的剩余区域，该区域的特征为不能通过区域分割算法对图像进行分割，区域生长和分裂合并法是两种典型的串行区域技术，其分割过程后续步骤的处理要根据前面步骤的结果进行判断而确定。Please refer to FIG. 1 again. The secondary threshold setting in the second step is based on the region growing segmentation method. The refined data segmentation in the second step is specifically the remaining region generated after the region growing data is divided. The characteristics of the region are In order to not be able to segment the image by the region segmentation algorithm, region growing and splitting and merging are two typical serial region techniques, and the processing of the subsequent steps of the segmentation process should be determined according to the results of the previous steps.
(1)区域生长(1) Regional growth
区域生长的基本思想是将具有相似性质的像素集合起来构成区域。具体先对每个需要分割的区域找一个种子像素作为生长的起点，然后将种子像素周围邻域中与种子像素有相同或相似性质的像素(根据某种事先确定的生长或相似准则来判定)合并到种子像素所在的区域中。将这些新像素当作新的种子像素继续进行上面的过程，直到再没有满足条件的像素可被包括进来。这样一个区域就长成了。The basic idea of region growing is to group pixels with similar properties to form regions. Specifically, first find a seed pixel as the starting point of growth for each area to be segmented, and then select the pixels in the neighborhood around the seed pixel that have the same or similar properties as the seed pixel (determined according to some predetermined growth or similarity criteria) Merge into the area where the seed pixel is located. Continue the above process with these new pixels as new seed pixels until no more pixels that satisfy the condition can be included. Such an area grows.
(2)区域分裂合并(2) Regional splitting and merging
区域生长是从某个或者某些像素点出发，最后得到整个区域，进而实现目标提取。分裂合并差不多是区域生长的逆过程：从整个图像出发，不断分裂得到各个子区域，然后再把前景区域合并，实现目标提取。分裂合并的假设是对于一幅图像，前景区域是由一些相互连通的像素组成的，因此，如果把一幅图像分裂到像素级，那么就可以判定该像素是否为前景像素。当所有像素点或者子区域完成判断以后，把前景区域或者像素合并就可得到前景目标。Region growth starts from one or some pixel points, and finally obtains the entire region, and then realizes target extraction. Splitting and merging is almost the inverse process of region growth: starting from the entire image, continuously splitting to obtain each sub-region, and then merging the foreground regions to achieve target extraction. The assumption of splitting and merging is that for an image, the foreground area is composed of some interconnected pixels. Therefore, if an image is split to the pixel level, it can be determined whether the pixel is a foreground pixel or not. When all pixels or sub-regions are judged, the foreground target can be obtained by combining the foreground regions or pixels.
请再次参阅图1，所述步骤三中数据集代入到分割的数据中的具体方法如下：Please refer to FIG. 1 again. The specific method for substituting the data set into the segmented data in the third step is as follows:
步骤一：对产生的粗糙集数据进行区域划分；Step 1: Divide the generated rough set data into regions;
步骤二：根据划分区域，找出已经分割完成的图像数据，将该区域内的粗糙集数据代入到该分割完成的图像数据中；Step 2: According to the divided area, find out the image data that has been segmented, and substitute the rough set data in the area into the segmented image data;
步骤三：根据步骤二的分割方法进行再次分割，对分割数据优化即可。Step 3: Perform the segmentation again according to the segmentation method in Step 2, and optimize the segmentation data.
请再次参阅图1，所述步骤四和步骤五中检验方法和优化报错体现的具体方法为监测优化后的数据是否报错，产生的报错信息将整个代入的粗糙集信息整体的转移到相近的完成分割的图像数据中，进行二次代入并优化，直至无报错即可。Please refer to Fig. 1 again. The specific method of the inspection method and the optimization error report in the step 4 and step 5 is to monitor whether the optimized data reports an error, and the generated error message transfers the entire substituted rough set information to a similar completion. In the segmented image data, perform secondary substitution and optimization until no error is reported.
请再次参阅图1，所述步骤六中组合方法为特定数据抽取方法，具体为选择数据集，根据关键词进行抽取，再将抽取的数据按照同样的步骤还原即可。Referring to FIG. 1 again, the combination method in the sixth step is a specific data extraction method, specifically selecting a data set, extracting according to keywords, and then restoring the extracted data according to the same steps.
虽然在上文中已经参考实施方式对本发明进行了描述，然而在不脱离本发明的范围的情况下，可以对其进行各种改进并且可以用等效物替换其中的部件。尤其是，只要不存在结构冲突，本发明所披露的实施方式中的各项特征均可通过任意方式相互结合起来使用，在本说明书中未对这些组合的情况进行穷举性的描述仅仅是出于省略篇幅和节约资源的考虑。因此，本发明并不局限于文中公开的特定实施方式，而是包括落入权利要求的范围内的所有技术方案。Although the present invention has been described above with reference to the embodiments, various modifications may be made and equivalents may be substituted for parts thereof without departing from the scope of the invention. In particular, as long as there is no structural conflict, the various features in the disclosed embodiments of the present invention can be combined with each other in any way, and the description of these combinations is not exhaustive in this specification. For the sake of omitting space and saving resources. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.
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