Image segmentation method and system

Abstract

The present invention provides an image segmentation method and system, belonging to the field of artificial intelligence technology. The image segmentation method comprises: determining the distance function from the pixel point to the initial cluster center according to the pixel point of the original image, the initial offset of the pixel point, the initial cluster center and the initial membership of the domain point corresponding to the pixel point with respect to the initial cluster center; determining the intermediate membership of the pixel point with respect to the initial cluster center according to the distance function from the pixel point to the initial cluster center; determining the target membership according to the intermediate membership, obtaining the target cluster center according to the target membership, and segmenting the image through the target cluster center and the target membership to extract image information. The present invention can realize the effective segmentation of the image, and then extract accurate image information.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an image segmentation method and system. Background Art

[0002] The existing technology realizes image segmentation and information extraction through image spatial information and an image segmentation algorithm with a function of correcting offset fields, but cannot realize effective segmentation of detail components, so it is difficult to obtain accurate image information. Summary of the invention

[0003] The main purpose of the embodiments of the present invention is to provide an image segmentation method and system to achieve effective image segmentation and further extract accurate image information.

[0004] In order to achieve the above object, an embodiment of the present invention provides an image segmentation method, comprising:

[0005] Determine the distance function from the pixel point to the initial cluster center according to the pixel point of the original image, the initial offset of the pixel point, the initial cluster center and the initial membership of the domain point corresponding to the pixel point with respect to the initial cluster center;

[0006] Determine the intermediate membership of the pixel point with respect to the initial cluster center according to the distance function from the pixel point to the initial cluster center;

[0007] The target membership is determined according to the intermediate membership, the target cluster center is obtained according to the target membership, and the image is segmented by the target cluster center and the target membership to extract image information.

[0008] The embodiment of the present invention also provides an image segmentation system, including:

[0009] A distance function determination unit, used to determine the distance function from the pixel point to the initial cluster center according to the pixel point of the original image, the initial offset of the pixel point, the initial cluster center and the initial membership of the domain point corresponding to the pixel point with respect to the initial cluster center;

[0010] An intermediate membership unit, used to determine the intermediate membership of the pixel point with respect to the initial cluster center according to the distance function from the pixel point to the initial cluster center;

[0011] The image segmentation unit is used to determine the target membership according to the intermediate membership, obtain the target cluster center according to the target membership, and segment the image through the target cluster center and the target membership to extract image information.

[0012] An embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the steps of the image segmentation method are implemented when the processor executes the computer program.

[0013] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the image segmentation method are implemented.

[0014] The image segmentation method and system of the embodiment of the present invention first determine the distance function according to the pixel points of the image, the initial offset of the pixel points, the initial cluster center and the initial membership of the domain points corresponding to the pixel points with respect to the initial cluster center, then determine the intermediate membership according to the distance function, and then determine the target membership according to the intermediate membership, obtain the target cluster center according to the target membership, and finally segment the image through the target cluster center and the target membership to extract image information, so as to achieve effective segmentation of the image and further extract accurate image information. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 is a flow chart of an image segmentation method in one embodiment of the present invention;

[0017] Figure 2 is a flow chart of an image segmentation method in another embodiment of the present invention;

[0018] Figure 3 is a flow chart of determining a target offset in one embodiment of the present invention;

[0019] Figure 4 It is the bill number image that is not affected by the uneven illumination field;

[0020] Figure 5 It is the bill number image affected by the non-uniform illumination field;

[0021] Figure 6 The bill number is extracted by the image segmentation method of the present invention;

[0022] Figure 7 is a structural block diagram of an image segmentation system in an embodiment of the present invention;

[0023] Figure 8 It is a structural block diagram of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, device, apparatus, method or computer program product. Therefore, the present disclosure may be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0026] Since the prior art cannot effectively segment detail components and it is difficult to obtain accurate image information, an embodiment of the present invention provides an image segmentation method that can effectively segment an image and extract accurate image information. The present invention is described in detail below with reference to the accompanying drawings.

[0027] Figure 1 is a flow chart of an image segmentation method in one embodiment of the present invention. Figure 2 FIG. 1 is a flow chart of an image segmentation method in another embodiment of the present invention. Figure 1 and Figure 2 As shown, the image segmentation method includes:

[0028] S101: determining a distance function from a pixel point to an initial cluster center according to a pixel point of an original image, an initial offset of the pixel point, an initial cluster center, and an initial membership of a neighborhood point corresponding to the pixel point with respect to the initial cluster center.

[0029] The initial offset of the pixel point may be a smaller value, such as 0.01.

[0030] In one embodiment, before executing S101, the method further includes:

[0031] Obtain an initial image; remove the background area of ​​the initial image to obtain an image.

[0032] In specific implementation, a morphological method can be used to remove the background area and the area of ​​no interest of the initial image to obtain an image.

[0033] In one embodiment, assume that a finite set Y = {y 1 ,y 2 ,y 3 ,……,y N} is a set of N pixels, c is the number of predetermined categories, y i is the i-th pixel, v j is the jth cluster center, uj (y i ) is the membership of the i-th pixel to the j-th cluster center, then the clustering criterion function is defined by the membership function as:

[0034]

[0035] Among them, J(U,V) is the clustering criterion function, ||y i -v j || is y i to v j (j=1,2,3,...,c) square of the Euclidean distance; b is the fuzzy weighted power index, which is a parameter that can control the fuzziness of the clustering result; U is the fuzzy matrix of Y, and satisfies u j (y i )∈[0,1] and V is the set of cluster centers of Y.

[0036] The present invention improves the FCM algorithm and incorporates spatial relationship constraints: the purpose of the FCM clustering algorithm is to obtain V and U that minimize the criterion function. The algorithm defines each pixel y i About cluster center v j The membership degree u j (y i )for:

[0037]

[0038] Among them, v m is the mth cluster center.

[0039] according to and The following formula can be obtained to determine the distance function from the pixel to the initial cluster center:

[0040]

[0041]

[0042] Among them, d 2 (y i ,v j ) is the i-th pixel y i To the jth initial cluster center v j The distance function, y i is the i-th pixel, β i is the initial offset of the i-th pixel, v j is the jth initial cluster center, λ is the first adjustment space constraint relationship coefficient, λ∈[0,1], ξ is the second adjustment space constraint relationship coefficient, ξ∈[0,1], uj (y k ) is the kth area point y corresponding to the i-th pixel k Regarding the initial membership of the jth initial cluster center, G ik is the absolute value of the grayscale difference between the i-th pixel and the corresponding k-th field point, P ik is the neighborhood distance between the ith pixel and the corresponding kth neighborhood point, s is the value of y i The center point and y i The corresponding field point y k The number of 2 (y i ,v m ) is the i-th pixel y i To the mth initial cluster center v m The distance function, v m is the mth initial cluster center, u m (y k ) is the kth area point y corresponding to the i-th pixel k The initial membership of the mth initial cluster center.

[0043] It can be seen from this that the clustering fuzzy matrix is ​​not only determined by the grayscale of the pixel itself, but also affected by pixels in other areas.

[0044] S102: Determine the intermediate membership of the pixel point with respect to the initial cluster center according to the distance function from the pixel point to the initial cluster center.

[0045] In one embodiment, the intermediate membership degree may be determined by the following formula:

[0046]

[0047] Among them, u j (y i ) is the i-th pixel y i Regarding the intermediate membership of the jth initial cluster center, b is the fuzzy weighted power exponent.

[0048] S103: determining a target membership according to the intermediate membership, obtaining a target cluster center according to the target membership, and segmenting the image by the target cluster center and the target membership to extract image information.

[0049] Figure 3 FIG. 1 is a flow chart of determining target membership in one embodiment of the present invention. Figure 3 As shown, determining the target membership according to the intermediate membership also includes:

[0050] S201: Determine the intermediate offset of the pixel point according to the intermediate membership and the initial cluster center.

[0051] In one embodiment, the middle offset of the pixel point can be determined by the following formula:

[0052]

[0053] Among them, β' i is the middle offset of the i-th pixel.

[0054] S202: Determine an intermediate cluster center according to the intermediate offset and the intermediate membership.

[0055] In one embodiment, the intermediate cluster center may be determined by the following formula:

[0056]

[0057] Among them, v' j is the jth intermediate cluster center, (u(y i )) max is the i-th pixel y i The maximum value of the intermediate membership about the initial cluster centers.

[0058] S203: Determine the target membership through the intermediate membership, the intermediate offset and the intermediate cluster center.

[0059] In one embodiment, S203 further includes:

[0060] 1. Replace the initial membership, initial offset and initial cluster center with the intermediate membership, intermediate offset and intermediate cluster center respectively, and then perform corresponding iterative calculations;

[0061] Among them, the domain point corresponding to the pixel point is also one of the pixel points of the original image. Therefore, the initial membership of the domain point corresponding to the pixel point with respect to the initial cluster center can be replaced according to the intermediate membership of the pixel point with respect to the initial cluster center.

[0062] 2. When the current number of iterations is equal to the preset number of iterations, the intermediate membership obtained by the current iteration calculation is used as the target membership.

[0063] Wherein, obtaining the target cluster center according to the target membership includes: determining the target offset of the pixel point according to the target membership and the intermediate cluster center in the previous iteration; and determining the target cluster center according to the target offset and the target membership. That is, 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 cluster center is determined according to the target offset and the target membership, which is equivalent to using the intermediate cluster center obtained by the current iteration calculation as the target cluster center.

[0064] Figure 1The execution subject of the image segmentation method shown in the figure can be a computer. Figure 1 As can be seen from the process shown, the image segmentation method of the embodiment of the present invention first determines the distance function based on the pixel points of the image, the initial offset of the pixel points, the initial cluster center and the initial membership of the domain points corresponding to the pixel points with respect to the initial cluster center, and then determines the intermediate membership based on the distance function, and then determines the target membership based on the intermediate membership, obtains the target cluster center based on the target membership, and finally segments the image through the target cluster center and the target membership to extract image information, which can achieve effective image segmentation and further extract accurate image information.

[0065] In addition, in image acquisition scenarios, images are often destroyed by non-uniform fields containing slowly changing grayscale. The non-uniform field will cause the grayscale of the same part to change, thereby affecting the clarity and accuracy of the picture. Therefore, the image information extracted by the existing technology through the spatial information of the image and the algorithm with the function of correcting the offset field contains high-frequency components and cannot obtain clear and accurate images.

[0066] An image subjected to inhomogeneous illumination can be equivalent to an image that is not actually contaminated by the inhomogeneous field multiplied by a slowly varying inhomogeneous field in the spatial domain:

[0067] Z i =X i G i , i∈{i,2,3,...,N};

[0068] In the formula, Z i is the observed value of the image at the i-th point, X i is the value of the image at the i-th point that is not contaminated by the uniform field, G i The value of the multiplicative inhomogeneous field at the i-th point, N is the total number of pixels in the image. For ease of calculation, take the logarithm on both sides of the above formula to obtain:

[0069] z i =x i +β i , i∈{i,2,3,...,N};

[0070] In the formula, z i is the logarithmic value of the observed image at the i-th point, x i is the logarithmic value of the image at the i-th point that is not contaminated by the uniform field, β i is the logarithm of the multiplicative inhomogeneous field at the i-th point. The purpose of grayscale image correction and restoration is to correct the grayscale image from z i Lieutenant General x i Recover.

[0071] For example, when collecting images of counter document information, invoice contract information, and physical bills at a bank, the collected images are unclear due to factors such as uneven lighting and reflection. The present invention can also effectively remove the influence of uneven lighting on the image through the optimized FCM (mean clustering) algorithm, achieve the purpose of image restoration, and display the image more clearly, thereby better segmenting the restored image and extracting accurate image information (image target object).

[0072] In one embodiment, the image segmentation method further includes:

[0073] When the current number of iterations is equal to the preset number of iterations, the intermediate offset calculated by the current iteration is used as the target offset of the pixel point; and the image is repaired according to the target offset of the pixel point.

[0074] In a specific implementation, the corresponding pixel points of the original image can be restored according to the target offset (non-uniform field) of the pixel points to remove the illumination non-uniform field and repair the image.

[0075] At this time, segmenting the image by the target cluster center and the target membership to extract the image information includes: segmenting the restored image by the target cluster center and the target membership to extract the image information.

[0076] Therefore, the present invention can also effectively remove high-frequency components in an image to extract clear and accurate image information.

[0077] The specific process of the embodiment of the present invention is as follows:

[0078] 1. Obtain an initial image; remove the background area of ​​the initial image to obtain an image.

[0079] 2. Determine the distance function from the pixel to the initial cluster center according to the pixel of the image, the initial offset of the pixel, the initial cluster center and the initial membership of the domain point corresponding to the pixel with respect to the initial cluster center.

[0080] 3. Determine the intermediate membership of the pixel point with respect to the initial cluster center based on the distance function from the pixel point to the initial cluster center.

[0081] 4. Determine the intermediate offset of the pixel point based on the intermediate membership and the initial cluster center.

[0082] 5. Determine the intermediate cluster center based on the intermediate offset and intermediate membership.

[0083] 6. Determine whether the current number of iterations is equal to the preset number of iterations.

[0084] 7. When the current number of iterations is equal to the preset number of iterations, the intermediate membership obtained by the current iteration calculation is used as the target membership of the pixel point, the intermediate offset obtained by the current iteration calculation is used as the target offset of the pixel point, and the intermediate cluster center obtained by the current iteration calculation is used as the target cluster center of the pixel point. Otherwise, the initial membership is replaced by the intermediate membership, the initial offset is replaced by the intermediate offset, and the initial cluster center is replaced by the intermediate cluster center, and return to step 2.

[0085] 8. Repair the image based on the target offset of the pixels.

[0086] 9. Segment the image through the target cluster center and the target membership of the pixel point to extract image information.

[0087] Figure 4 It is the bill number image that is not affected by the uneven illumination field. Figure 5 It is the bill number image affected by the uneven illumination field. Figure 6 The ticket number is extracted by the image segmentation method of the present invention. Figure 4-Figure 6 As shown, the present invention can accurately segment images, laying a foundation for subsequent image information extraction (including digital extraction, graphic extraction, and text extraction, etc.).

[0088] In summary, the present invention proposes 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 the FCM clustering criterion function based on grayscale information. It can accurately repair the original image, realize effective image segmentation, and extract accurate image information.

[0089] Based on the same inventive concept, an embodiment of the present invention further provides an image segmentation system. Since the principle of solving the problem by 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 the repeated parts will not be repeated.

[0090] Figure 7 is a structural block diagram of the image segmentation system in an embodiment of the present invention. Figure 7 As shown, the image segmentation system includes:

[0091] A distance function determination unit, used to determine the distance function from the pixel point to the initial cluster center according to the pixel point of the original image, the initial offset of the pixel point, the initial cluster center and the initial membership of the domain point corresponding to the pixel point with respect to the initial cluster center;

[0092] An intermediate membership determination unit, used to determine the intermediate membership of the pixel point with respect to the initial cluster center according to a distance function from the pixel point to the initial cluster center;

[0093] The image segmentation unit is used to determine the target membership according to the intermediate membership, obtain the target cluster center according to the target membership, and segment the image through the target cluster center and the target membership to extract image information.

[0094] In one embodiment, the image segmentation unit is further configured to:

[0095] Determine the intermediate offset of the pixel point according to the intermediate membership and the initial cluster center;

[0096] Determine the intermediate cluster center according to the intermediate offset and intermediate membership;

[0097] The target membership is determined by the intermediate membership, intermediate offset and intermediate cluster center.

[0098] In one embodiment, the image segmentation unit is further configured to:

[0099] After replacing the initial membership, initial offset and initial cluster center respectively with the intermediate membership, intermediate offset and intermediate cluster center, corresponding iterative calculation is performed;

[0100] When the current number of iterations is equal to the preset number of iterations, the intermediate membership obtained by the current iteration calculation is used as the target membership.

[0101] In one embodiment, the invention further comprises: an image restoration unit, configured to:

[0102] When the current number of iterations is equal to the preset number of iterations, the intermediate offset calculated by the current iteration is used as the target offset of the pixel point;

[0103] Repair the image according to the target offset of the pixel points;

[0104] The image segmentation unit is specifically used for:

[0105] The restored image is segmented by target cluster center and target membership to extract image information.

[0106] In summary, the image segmentation system of the embodiment of the present invention first determines the distance function based on the image pixels, the initial offset of the pixels, the initial cluster center and the initial membership of the domain points corresponding to the pixels with respect to the initial cluster center, and then determines the intermediate membership based on the distance function, and then determines the target membership based on the intermediate membership, obtains the target cluster center based on the target membership, and finally segments the image through the target cluster center and the target membership to extract image information, which can achieve effective image segmentation and further extract accurate image information.

[0107] The embodiment of the present invention also provides a specific implementation of a computer device capable of implementing all the steps in the image segmentation method in the above embodiment. Figure 8 is a block diagram of a computer device in an embodiment of the present invention, see Figure 8 , the computer equipment specifically includes the following contents:

[0108] Processor (processor) 801 and memory (memory) 802.

[0109] The processor 801 is used to call the computer program in the memory 802. When the processor executes the computer program, all the steps in the image segmentation method in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0110] Determine the distance function from the pixel point to the initial cluster center according to the pixel point of the original image, the initial offset of the pixel point, the initial cluster center and the initial membership of the domain point corresponding to the pixel point with respect to the initial cluster center;

[0111] Determine the intermediate membership of the pixel point with respect to the initial cluster center according to the distance function from the pixel point to the initial cluster center;

[0112] The target membership is determined according to the intermediate membership, the target cluster center is obtained according to the target membership, and the image is segmented by the target cluster center and the target membership to extract image information.

[0113] In summary, the computer device of the embodiment of the present invention first determines the distance function based on the pixel points of the image, the initial offset of the pixel points, the initial cluster center and the initial membership of the domain points corresponding to the pixel points with respect to the initial cluster center, and then determines the intermediate membership based on the distance function, and then determines the target membership based on the intermediate membership, obtains the target cluster center based on the target membership, and finally segments the image through the target cluster center and the target membership to extract image information, which can achieve effective segmentation of the image and then extract accurate image information.

[0114] The embodiment of the present invention also provides a computer-readable storage medium capable of implementing all the steps of the image segmentation method in the above embodiment. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps of the image segmentation method in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0115] Determine the distance function from the pixel point to the initial cluster center according to the pixel point of the original image, the initial offset of the pixel point, the initial cluster center and the initial membership of the domain point corresponding to the pixel point with respect to the initial cluster center;

[0116] Determine the intermediate membership of the pixel point with respect to the initial cluster center according to the distance function from the pixel point to the initial cluster center;

[0117] The target membership is determined according to the intermediate membership, the target cluster center is obtained according to the target membership, and the image is segmented by the target cluster center and the target membership to extract image information.

[0118] In summary, the computer-readable storage medium of an embodiment of the present invention first determines a distance function based on the pixel points of the image, the initial offset of the pixel points, the initial cluster center, and the initial membership of the domain points corresponding to the pixel points with respect to the initial cluster center, and then determines the intermediate membership based on the distance function, and then determines the target membership based on the intermediate membership, obtains the target cluster center based on the target membership, and finally segments the image through the target cluster center and the target membership to extract image information, thereby achieving effective segmentation of the image and extracting accurate image information.

[0119] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0120] Those skilled in the art may also understand that the various illustrative logical blocks, units, and steps listed in the embodiments of the present invention may be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly demonstrate the interchangeability of hardware and software, the various illustrative components, units, and steps described above have generally described their functions. Whether such functions are implemented by hardware or software depends on the specific application and the design requirements of the entire system. Those skilled in the art may use various methods to implement the described functions for each specific application, but such implementation should not be understood as exceeding the scope of protection of the embodiments of the present invention.

[0121] The various illustrative logic blocks, or units, or devices described in the embodiments of the present invention can be implemented or operated by a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field programmable gate array or other programmable logic device, a discrete gate or transistor logic, a discrete hardware component, or any combination of the above. The general-purpose processor can be a microprocessor, and optionally, the general-purpose processor can also be any conventional processor, controller, microcontroller or state machine. The processor can also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration.

[0122] The steps of the method or algorithm described in the embodiments of the present invention can be directly embedded in hardware, a software module executed by a processor, or a combination of the two. The software module can be stored in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or other storage media of any form in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium can be arranged in an ASIC, and the ASIC can be arranged in a user terminal. Optionally, the processor and the storage medium can also be arranged in different components in the user terminal.

[0123] In one or more exemplary designs, the above functions described in the embodiments of the present invention can be implemented in hardware, software, firmware or any combination of the three. If implemented in software, these functions can be stored on a computer-readable medium, or transmitted in the form of one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media that facilitate the transfer of computer programs from one place to another. The storage medium can be any available medium that can be accessed by any general or special computer. For example, such computer-readable media can include but are not limited to RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store program codes in the form of instructions or data structures and other forms that can be read by general or special computers, or general or special processors. In addition, any connection can be appropriately defined as a computer-readable medium, for example, if the software is transmitted from a website site, server or other remote resource through a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wirelessly, such as infrared, wireless and microwave, it is also included in the defined computer-readable medium. The disk and disc include compact disk, laser disk, optical disk, DVD, floppy disk and blue-ray disk. Disks usually copy data magnetically, while discs usually copy data optically with lasers. The above combination can also be included in computer readable media.

Claims

1. An image segmentation method, characterized in that: include: Determine a distance function from the pixel point to the initial cluster center according to the pixel point of the original image, the initial offset of the pixel point, the initial cluster center and the initial membership of the domain point corresponding to the pixel point with respect to the initial cluster center; Determining the intermediate membership of the pixel point with respect to the initial cluster center according to the distance function from the pixel point to the initial cluster center; Determining a target membership according to the intermediate membership, obtaining a target cluster center according to the target membership, and segmenting the image by using the target cluster center and the target membership to extract image information; Determining the target membership according to the intermediate membership also includes: Determining an intermediate offset of a pixel point according to the intermediate membership and the initial cluster center; Determining an intermediate cluster center according to the intermediate offset and the intermediate membership; Determining a target membership by using the intermediate membership, the intermediate offset and the intermediate cluster center; The intermediate cluster center is determined by the following formula: Among them, v' j is the jth intermediate cluster center, (u(y i )) max is the i-th pixel y i The maximum value of the intermediate membership about the initial cluster centers.

2. The image segmentation method according to claim 1, characterized in that: Determining the target membership by the intermediate membership, the intermediate offset and the intermediate cluster center also includes: After replacing the initial membership degree, the initial offset and the initial cluster center respectively with the intermediate membership degree, the intermediate offset and the intermediate cluster center, corresponding iterative calculation is performed; When the current number of iterations is equal to the preset number of iterations, the intermediate membership obtained by the current iteration calculation is used as the target membership.

3. The image segmentation method according to claim 2, characterized in that: Also includes: When the current number of iterations is equal to the preset number of iterations, the intermediate offset calculated by the current iteration is used as the target offset of the pixel point; Repairing the image according to the target offset of the pixel point; Segmenting the image by using the target cluster center and the target membership to extract image information includes: The restored image is segmented by the target cluster center and the target membership to extract image information.

4. An image segmentation system, characterized in that: include: A distance function determination unit, configured to determine a distance function from a pixel point to an initial cluster center according to a pixel point of an original image, an initial offset of the pixel point, an initial cluster center, and an initial membership degree of a domain point corresponding to the pixel point with respect to the initial cluster center; An intermediate membership determination unit, used to determine the intermediate membership of the pixel point with respect to the initial cluster center according to the distance function from the pixel point to the initial cluster center; An image segmentation unit, configured to determine a target membership according to the intermediate membership, obtain a target cluster center according to the target membership, and segment the image by using the target cluster center and the target membership to extract image information; The image segmentation unit is also used for: Determining an intermediate offset of a pixel point according to the intermediate membership and the initial cluster center; Determining an intermediate cluster center according to the intermediate offset and the intermediate membership; Determining a target membership by using the intermediate membership, the intermediate offset and the intermediate cluster center; The intermediate cluster center is determined by the following formula: Among them, v' j is the jth intermediate cluster center, (u(y i )) max is the i-th pixel y i The maximum value of the intermediate membership about the initial cluster centers.

5. The image segmentation system according to claim 4, characterized in that: The image segmentation unit is also used for: After replacing the initial membership degree, the initial offset and the initial cluster center respectively with the intermediate membership degree, the intermediate offset and the intermediate cluster center, corresponding iterative calculation is performed; When the current number of iterations is equal to the preset number of iterations, the intermediate membership obtained by the current iteration calculation is used as the target membership.

6. The image segmentation system according to claim 5, characterized in that: Also includes: Image restoration unit, used to: When the current number of iterations is equal to the preset number of iterations, the intermediate offset calculated by the current iteration is used as the target offset of the pixel point; Repairing the image according to the target offset of the pixel point; The image segmentation unit is specifically used for: The restored image is segmented by the target cluster center and the target membership to extract image information.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the image segmentation method according to any one of claims 1 to 3 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the image segmentation method according to any one of claims 1 to 3 are implemented.

Images