CN112734771A - Image segmentation method and system - Google Patents
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Abstract
The invention provides an image segmentation method and system, and belongs to the technical field of artificial intelligence. The image segmentation method comprises the following steps: determining a distance function from the pixel point to an initial clustering center according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center and the initial membership degree of the field point corresponding to the pixel point to the initial clustering center; determining the intermediate membership degree of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center; and determining the target membership degree according to the intermediate membership degree, obtaining a target clustering center according to the target membership degree, and segmenting the image through the target clustering center and the target membership degree to extract image information. The invention can realize effective segmentation of the image, thereby extracting accurate image information.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an image segmentation method and an image segmentation system.
Background
In the prior art, image segmentation and information extraction are realized through image spatial information and an image segmentation algorithm with a function of correcting an offset field, and effective segmentation of detail components cannot be realized, so that accurate image information is difficult to obtain.
Disclosure of Invention
The embodiment of the invention mainly aims to provide an image segmentation method and an image segmentation system so as to realize effective segmentation of an image and further extract accurate image information.
In order to achieve the above object, an embodiment of the present invention provides an image segmentation method, including:
determining a distance function from the pixel point to an initial clustering center according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center and the initial membership degree of the field point corresponding to the pixel point to the initial clustering center;
determining the intermediate membership degree of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center;
and determining the target membership degree according to the intermediate membership degree, obtaining a target clustering center according to the target membership degree, and segmenting the image through the target clustering center and the target membership degree to extract image information.
The embodiment of the present invention further provides an image segmentation system, including:
the distance function determining unit is used for determining a distance function from the pixel point to the initial clustering center according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center and the initial membership degree of the field point corresponding to the pixel point relative to the initial clustering center;
the intermediate membership unit is used for determining the intermediate membership of the pixel point relative to the initial clustering center according to a distance function from the pixel point to the initial clustering center;
and the image segmentation unit is used for determining the target membership degree according to the intermediate membership degree, acquiring a target clustering center according to the target membership degree, and segmenting the image through the target clustering center and the target membership degree to extract image information.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the image segmentation method when executing the computer program.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of the image segmentation method.
The image segmentation method and the image segmentation system of the embodiment of the invention firstly determine a distance function according to the pixel points of the image, the initial offset of the pixel points, the initial clustering center and the initial membership of the field points corresponding to the pixel points relative to the initial clustering center, then determine the intermediate membership according to the distance function, then determine the target membership according to the intermediate membership, obtain the target clustering center according to the target membership, and finally segment the image through the target clustering center and the target membership to extract the image information, so that the effective segmentation of the image can be realized, and the accurate image information can be extracted.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments will be briefly introduced below, 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 that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of an image segmentation method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method of image segmentation in another embodiment of the present invention;
FIG. 3 is a flow chart of determining a target offset in one embodiment of the present invention;
FIG. 4 is an image of a bill number not affected by a field of illumination inhomogeneities;
FIG. 5 is an image of a bill number subject to a field of illumination inhomogeneities;
FIG. 6 is a ticket number extracted by the image segmentation method of the present invention;
FIG. 7 is a block diagram of an image segmentation system in an embodiment of the invention;
fig. 8 is a block diagram of a computer device in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be embodied as a system, apparatus, device, method, or computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
In view of the fact that effective segmentation of detail components cannot be achieved in the prior art and accurate image information is difficult to obtain, the embodiment of the invention provides an image segmentation method which can achieve effective segmentation of images and further extract accurate image information. The present invention will be described in detail below with reference to the accompanying drawings.
FIG. 1 is a flowchart of an image segmentation method according to an embodiment of the present invention. FIG. 2 is a flow chart of an image segmentation method according to another embodiment of the present invention. As shown in fig. 1 and 2, the image segmentation method includes:
s101: and determining a distance function from the pixel point to the initial clustering center according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center and the initial membership degree of the field point corresponding to the pixel point to the initial clustering center.
The initial offset of the pixel point may be a smaller value, for example, 0.01.
In one embodiment, before performing S101, the method further includes:
acquiring an initial image; and removing the background area of the initial image to obtain an image.
In specific implementation, a morphological method may be used to remove the background region and the region not of interest of the initial image, so as to obtain an image.
In one embodiment, a finite set of Y ═ Y is assumed1,y2,y3,……,yNIs a set of N pixels, c is a predetermined number of classes, yiIs the ith pixel point, vjIs the jth cluster center, uj(yi) If the membership degree of the ith pixel point relative to the jth clustering center is adopted, the clustering criterion function is defined by the membership function as follows:
wherein J (U, V) is a clustering criterion function, | | yi-vj| is yiTo vjA square of the euclidean distance of (j ═ 1,2, 3.., c); b is fuzzy weighted power exponent which is a parameter capable of controlling the fuzzy degree of the clustering result; u is the fuzzification submatrix of Y while satisfying Uj(yi)∈[0,1]And
v is the set of cluster centers for Y.
The present invention improves the FCM algorithm and incorporates spatial relationship constraints: the objective of the FCM clustering algorithm is to obtain V and U that minimize the criteria function. The algorithm defines each pixel yiAbout the cluster center vjDegree of membership u ofj(yi) Comprises the following steps:
wherein v ismIs the mth cluster center.
According to
And
the following formula can be obtained to determine the distance function from the pixel point to the initial clustering center:
wherein d is2(yi,vj) Is the ith pixel point yiTo the jth initial clustering center vjDistance function of yiIs the ith pixel point, betaiIs the initial offset, v, of the ith pixel pointjIs the jth initial clustering center, lambda is the first adjustment space constraint relation coefficient, and lambda belongs to [0,1 ]]Xi is a second adjustment space constraint relation coefficient, and xi belongs to [0,1 ]]，uj(yk) K field point y corresponding to i pixel pointkInitial degree of membership, G, for the jth initial cluster centerikIs the absolute value of the gray difference between the ith pixel point and the corresponding kth field point, PikIs the block distance between the ith pixel point and the corresponding kth domain point, and s is yiIs a central point, and yiCorresponding domain point ykThe number of (2); d2(yi,vm) Is the ith pixel point yiTo the m-th initial clustering center vmV is a distance function ofmFor the m-th initial cluster center, um(yk) K field point y corresponding to i pixel pointkInitial membership for the mth initial cluster center.
Therefore, the clustering ambiguity matrix is not only determined by the gray level of the pixel, but also influenced by the pixel points in other fields.
S102: and determining the intermediate membership degree of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center.
In one embodiment, the intermediate degree of membership may be determined by the following formula:
wherein u isj(yi) Is the ith pixel point yiWith respect to the intermediate degree of membership of the jth initial cluster center, b is a fuzzy weighted power exponent.
S103: and determining the target membership degree according to the intermediate membership degree, obtaining a target clustering center according to the target membership degree, and segmenting the image through the target clustering center and the target membership degree to extract image information.
FIG. 3 is a flow chart of determining target membership in one embodiment of the present invention. As shown in fig. 3, determining the target membership based on the intermediate membership further comprises:
s201: and determining the intermediate offset of the pixel point according to the intermediate membership and the initial clustering center.
In one embodiment, the intermediate offset of the pixel point can be determined by the following formula:
wherein is beta'iThe intermediate offset of the ith pixel point.
S202: and determining a middle clustering center according to the middle offset and the middle membership.
In one embodiment, the intermediate cluster center may be determined by the following formula:
wherein, v'jFor the jth intermediate cluster center, (u (y)i))maxIs the ith pixel point yiMaximum value of intermediate degree of membership with respect to the initial cluster center.
S203: and determining the target membership degree through the intermediate membership degree, the intermediate offset and the intermediate clustering center.
In one embodiment, S203 further includes:
1. respectively replacing the initial membership degree, the initial offset and the initial clustering center by the intermediate membership degree, the intermediate offset and the intermediate clustering center, and then carrying out corresponding iterative computation;
the domain point corresponding to the pixel point is also one of the pixel points of the original image, so that the initial membership degree of the domain point corresponding to the pixel point relative to the initial clustering center can be replaced according to the intermediate membership degree of the pixel point relative to the initial clustering center.
2. And when the current iteration times are equal to the preset iteration times, taking the intermediate membership obtained by current iteration calculation as a target membership.
The method for obtaining the target clustering center according to the target membership comprises the following steps: determining the target offset of the pixel point according to the target membership and the intermediate clustering center in the last iteration; and determining a target clustering center according to the target offset and the target membership. Namely, when the current iteration number is equal to the preset iteration number, the intermediate offset obtained by the current iteration calculation is used as the target offset of the pixel point, and the target clustering center is determined according to the target offset and the target membership degree, which is equivalent to the intermediate clustering center obtained by the current iteration calculation being used as the target clustering center.
The execution subject of the image segmentation method shown in fig. 1 may be a computer. As can be seen from the flow shown in fig. 1, the image segmentation method according to the embodiment of the present invention determines a distance function according to the pixel point of the image, the initial offset of the pixel point, the initial clustering center, and the initial membership of the field point corresponding to the pixel point with respect to the initial clustering center, determines an intermediate membership according to the distance function, determines a target membership according to the intermediate membership, obtains a target clustering center according to the target membership, and segments the image through the target clustering center and the target membership to extract image information, so that effective segmentation of the image can be achieved, and accurate image information can be extracted.
In addition, in an image acquisition scene, an image is often damaged by a slowly-changing gray scale uneven field, and the uneven field can cause the change of the gray scale of the same part, so that the definition and the accuracy of the image are influenced.
An image that is unevenly illuminated may be equivalent to an image that is not actually contaminated by an uneven field multiplied by an uneven field that varies slowly in the spatial domain:
Zi＝XiGi，i∈{i,2,3,...,N}；
in the formula, ZiFor observing the value of the resulting image at the ith point, XiValue at the i-th point, G, for an image not contaminated by a uniform fieldiThe value of the multiplicative inhomogeneous field at the ith point, and N is the total pixel point of the image. For convenience of calculation, taking logarithm on two sides of the formula to obtain:
zi＝xi+βi，i∈{i,2,3,...,N}；
in the formula, ziLogarithmic value, x, at point i for the observed imageiLogarithmic value, beta, at the i-th point for an image not contaminated by a uniform fieldiFor the logarithmic value of the multiplicative uneven field at the ith point, the aim of the gray scale image correction and restoration is to correct the value from ziIn (1) xiAnd recovering.
For example, when a bank collects images such as counter certificate information, invoice contract information, real bill objects and the like, the collected images are not clearly influenced by factors such as uneven illumination, light reflection and the like; according to the invention, the influence of uneven illumination on the image can be effectively removed through an optimized FCM (mean value clustering) algorithm, the purpose of image restoration is achieved, the image is more clearly displayed, the restored image can be better segmented, and accurate image information (image target object) is extracted.
In one embodiment, the image segmentation method further comprises:
when the current iteration times are equal to the preset iteration times, taking the intermediate offset obtained by current iteration calculation as the target offset of the pixel point; and restoring the image according to the target offset of the pixel point.
In specific implementation, the corresponding pixel points of the original image can be restored according to the target offset (uneven field) of the pixel points to remove the uneven field of illumination and restore the image.
The step of segmenting the image through the target clustering center and the target membership degree to extract image information comprises the following steps: and segmenting the repaired image through the target clustering center and the target membership degree to extract image information.
Therefore, the invention can effectively remove high-frequency components in the image to extract clear and accurate image information.
The specific process of the embodiment of the invention is as follows:
1. acquiring an initial image; and removing the background area of the initial image to obtain an image.
2. And determining a distance function from the pixel point to the initial clustering center according to the pixel point of the image, the initial offset of the pixel point, the initial clustering center and the initial membership degree of the field point corresponding to the pixel point to the initial clustering center.
3. And determining the intermediate membership degree of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center.
4. And determining the intermediate offset of the pixel point according to the intermediate membership and the initial clustering center.
5. And determining a middle clustering center according to the middle offset and the middle membership.
6. And judging whether the current iteration times are equal to the preset iteration times or not.
7. And when the current iteration times are equal to the preset iteration times, taking the intermediate membership degree obtained by the current iteration calculation as the target membership degree of the pixel point, taking the intermediate offset obtained by the current iteration calculation as the target offset of the pixel point, taking the intermediate clustering center obtained by the current iteration calculation as the target clustering center of the pixel point, otherwise, replacing the initial membership degree according to the intermediate membership degree, replacing the initial offset according to the intermediate offset, replacing the initial clustering center according to the intermediate clustering center, and returning to the step 2.
8. And restoring the image according to the target offset of the pixel point.
9. And segmenting the image through the target clustering center and the target membership degree of the pixel point to extract image information.
FIG. 4 is an image of a bill number that is not affected by a field of illumination inhomogeneities. FIG. 5 is an image of a ticket number affected by a field of uneven illumination. Fig. 6 is a bill number extracted by the image segmentation method of the present invention. As shown in fig. 4-6, the present invention can accurately segment images, and lay the foundation for subsequent image information extraction (including digital extraction, graphic extraction, and text extraction).
In summary, the invention provides a method for segmenting an image with uneven illumination, which not only introduces an offset field function, but also introduces the spatial domain information of the image into an FCM clustering criterion function based on gray information, so that the original image can be accurately restored, the effective segmentation of the image is realized, and accurate image information is extracted.
Based on the same inventive concept, the embodiment of the invention also provides an image segmentation system, and as the problem solving principle of the system is similar to that of the image segmentation method, the implementation of the system can refer to the implementation of the method, and repeated parts are not described again.
Fig. 7 is a block diagram of an image segmentation system according to an embodiment of the present invention. As shown in fig. 7, the image segmentation system includes:
the distance function determining unit is used for determining a distance function from the pixel point to the initial clustering center according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center and the initial membership degree of the field point corresponding to the pixel point relative to the initial clustering center;
the intermediate membership determining unit is used for determining the intermediate membership of the pixel point relative to the initial clustering center according to a distance function from the pixel point to the initial clustering center;
and the image segmentation unit is used for determining the target membership degree according to the intermediate membership degree, acquiring a target clustering center according to the target membership degree, and segmenting the image through the target clustering center and the target membership degree to extract image information.
In one embodiment, the image segmentation unit is further configured to:
determining the intermediate offset of the pixel point according to the intermediate membership and the initial clustering center;
determining a middle clustering center according to the middle offset and the middle membership;
and determining the target membership degree through the intermediate membership degree, the intermediate offset and the intermediate clustering center.
In one embodiment, the image segmentation unit is further configured to:
respectively replacing the initial membership degree, the initial offset and the initial clustering center by the intermediate membership degree, the intermediate offset and the intermediate clustering center, and then carrying out corresponding iterative computation;
and when the current iteration times are equal to the preset iteration times, taking the intermediate membership obtained by current iteration calculation as a target membership.
In one embodiment, the method further comprises the following steps: an image repair unit to:
when the current iteration times are equal to the preset iteration times, taking the intermediate offset obtained by current iteration calculation as the target offset of the pixel point;
restoring an image according to the target offset of the pixel point;
the image segmentation unit is specifically configured to:
and segmenting the repaired image through the target clustering center and the target membership degree to extract image information.
To sum up, the image segmentation system according to the embodiment of the present invention determines a distance function according to pixel points of an image, an initial offset of the pixel points, an initial clustering center, and an initial membership of a domain point corresponding to the pixel point with respect to the initial clustering center, determines an intermediate membership according to the distance function, determines a target membership according to the intermediate membership, obtains a target clustering center according to the target membership, and segments the image according to the target clustering center and the target membership to extract image information, so that effective segmentation of the image can be achieved, and accurate image information can be extracted.
The embodiment of the present invention further provides a specific implementation manner of a computer device capable of implementing all steps in the image segmentation method in the foregoing embodiment. Fig. 8 is a block diagram of a computer device in an embodiment of the present invention, and referring to fig. 8, the computer device specifically includes the following:
a processor (processor)801 and a memory (memory) 802.
The processor 801 is configured to call a computer program in the memory 802, and the processor implements all the steps in the image segmentation method in the above embodiments when executing the computer program, for example, the processor implements the following steps when executing the computer program:
determining a distance function from the pixel point to an initial clustering center according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center and the initial membership degree of the field point corresponding to the pixel point to the initial clustering center;
determining the intermediate membership degree of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center;
and determining the target membership degree according to the intermediate membership degree, obtaining a target clustering center according to the target membership degree, and segmenting the image through the target clustering center and the target membership degree to extract image information.
To sum up, the computer device of the embodiment of the present invention determines a distance function according to the pixel point of the image, the initial offset of the pixel point, the initial clustering center, and the initial membership of the field point corresponding to the pixel point with respect to the initial clustering center, determines an intermediate membership according to the distance function, determines a target membership according to the intermediate membership, obtains a target clustering center according to the target membership, and segments the image according to the target clustering center and the target membership to extract image information, thereby achieving effective segmentation of the image and further extracting accurate image information.
An embodiment of the present invention further provides a computer-readable storage medium capable of implementing all the steps in the image segmentation method in the foregoing embodiment, where the computer-readable storage medium stores a computer program, and the computer program implements all the steps of the image segmentation method in the foregoing embodiment when being executed by a processor, for example, the processor implements the following steps when executing the computer program:
determining a distance function from the pixel point to an initial clustering center according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center and the initial membership degree of the field point corresponding to the pixel point to the initial clustering center;
determining the intermediate membership degree of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center;
and determining the target membership degree according to the intermediate membership degree, obtaining a target clustering center according to the target membership degree, and segmenting the image through the target clustering center and the target membership degree to extract image information.
To sum up, the computer-readable storage medium according to the embodiment of the present invention determines a distance function according to the pixel point of the image, the initial offset of the pixel point, the initial clustering center, and the initial membership of the field point corresponding to the pixel point with respect to the initial clustering center, determines an intermediate membership according to the distance function, determines a target membership according to the intermediate membership, obtains a target clustering center according to the target membership, and segments the image according to the target clustering center and the target membership to extract image information, so that effective segmentation of the image can be achieved, and accurate image information can be extracted.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Those of skill in the art will further appreciate that the various illustrative logical blocks, units, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, elements, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design requirements of the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present embodiments.
The various illustrative logical blocks, or elements, or devices described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be located in a user terminal. In the alternative, the processor and the storage medium may reside in different components in a user terminal.
In one or more exemplary designs, the functions described above in connection with the embodiments of the invention may be implemented in hardware, software, firmware, or any combination of the three. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media that facilitate transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, such computer-readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store program code in the form of instructions or data structures and which can be read by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Additionally, any connection is properly termed a computer-readable medium, and, thus, is included if the software is transmitted from a website, server, or other remote source via a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wirelessly, e.g., infrared, radio, and microwave. Such discs (disk) and disks (disc) include compact disks, laser disks, optical disks, DVDs, floppy disks and blu-ray disks where disks usually reproduce data magnetically, while disks usually reproduce data optically with lasers. Combinations of the above may also be included in the computer-readable medium.
Claims (10)
1. An image segmentation method, comprising:
determining a distance function from a pixel point to an initial clustering center according to the pixel point of an original image, the initial offset of the pixel point, and the initial membership of the initial clustering center and a field point corresponding to the pixel point to the initial clustering center;
determining the intermediate membership degree of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center;
and determining target membership according to the intermediate membership, obtaining a target clustering center according to the target membership, and segmenting the image through the target clustering center and the target membership to extract image information.
2. The image segmentation method of claim 1, wherein determining a target membership based on the intermediate membership further comprises:
determining the intermediate offset of the pixel point according to the intermediate membership and the initial clustering center;
determining a middle clustering center according to the middle offset and the middle membership degree;
and determining the target membership degree through the intermediate membership degree, the intermediate offset and the intermediate clustering center.
3. The image segmentation method of claim 2, wherein determining a target membership by the intermediate membership, the intermediate offset, and the intermediate cluster center further comprises:
respectively replacing the initial membership degree, the initial offset and the initial clustering center by the intermediate membership degree, the intermediate offset and the intermediate clustering center, and then carrying out corresponding iterative computation;
and when the current iteration times are equal to the preset iteration times, taking the intermediate membership obtained by current iteration calculation as a target membership.
4. The image segmentation method according to claim 3, further comprising:
when the current iteration times are equal to the preset iteration times, taking the intermediate offset obtained by current iteration calculation as the target offset of the pixel point;
restoring the image according to the target offset of the pixel point;
segmenting the image by the target cluster center and the target membership to extract image information comprises:
and segmenting the repaired image through the target clustering center and the target membership degree to extract image information.
5. An image segmentation system, comprising:
the distance function determining unit is used for determining a distance function from the pixel point to the initial clustering center according to the pixel point of the original image, the initial offset of the pixel point and the initial membership degree of the initial clustering center and the field point corresponding to the pixel point;
the intermediate membership determining unit is used for determining the intermediate membership of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center;
and the image segmentation unit is used for determining the target membership degree according to the intermediate membership degree, acquiring a target clustering center according to the target membership degree, and segmenting the image through the target clustering center and the target membership degree so as to extract image information.
6. The image segmentation system of claim 5, wherein the image segmentation unit is further configured to:
determining the intermediate offset of the pixel point according to the intermediate membership and the initial clustering center;
determining a middle clustering center according to the middle offset and the middle membership degree;
and determining the target membership degree through the intermediate membership degree, the intermediate offset and the intermediate clustering center.
7. The image segmentation system of claim 6, wherein the image segmentation unit is further configured to:
respectively replacing the initial membership degree, the initial offset and the initial clustering center by the intermediate membership degree, the intermediate offset and the intermediate clustering center, and then carrying out corresponding iterative computation;
and when the current iteration times are equal to the preset iteration times, taking the intermediate membership obtained by current iteration calculation as a target membership.
8. The image segmentation system of claim 7, further comprising: an image repair unit to:
when the current iteration times are equal to the preset iteration times, taking the intermediate offset obtained by current iteration calculation as the target offset of the pixel point;
restoring the image according to the target offset of the pixel point;
the image segmentation unit is specifically configured to:
and segmenting the repaired image through the target clustering center and the target membership degree to extract image information.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and running on the processor, characterized in that the processor implements the steps of the image segmentation method according to any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the image segmentation method according to any one of claims 1 to 4.
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