A control method, apparatus, device, and storage medium

Abstract

This invention discloses an image processing method, apparatus, device, and storage medium. The method includes: determining the local similarity density corresponding to each pixel based on the target similarity between each pixel and other pixels in a target image; filtering pixels in the target image based on the local similarity density corresponding to each pixel to obtain target cluster centers; and segmenting the target image based on the target cluster centers. The technical solution of this invention can improve the accuracy of image segmentation.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a control method, device, equipment and storage medium. Background Technology

[0002] Intelligent connected vehicle technology has been widely used to ensure driving safety, simplify travel, and reduce energy consumption and emissions, making it one of the hottest research topics this year. Meanwhile, computer science and technology have permeated the automotive industry to improve performance. Among these technologies, computer vision plays a crucial role in the driver's perception and understanding of their surroundings. Its objective and accurate prompts greatly assist drivers in making correct decisions and rational plans. To achieve a comprehensive understanding of the surrounding environment and target posture, there is a significant need for accurate extraction of the driving environment.

[0003] Due to external natural factors, the grayscale distribution on the surface of the same object is uneven. Furthermore, real-world image acquisition is often affected by electrical current during transmission, resulting in noise. All of these issues contribute to imperfect object segmentation. Traditional image processing methods require manual determination of cluster centers. Since the segmentation result is strongly correlated with the cluster centers, manually determining the cluster centers severely impacts the accuracy of the segmentation product. Summary of the Invention

[0004] This invention provides an image processing method, apparatus, device, and storage medium that can improve the accuracy of image segmentation.

[0005] According to one aspect of the present invention, an image processing method is provided, comprising:

[0006] The local similarity density of each pixel is determined based on the target similarity between each pixel and other pixels in the target image.

[0007] The target cluster centers are obtained by filtering the pixels in the target image based on the local similarity density corresponding to each pixel.

[0008] The target image is segmented based on the target cluster center.

[0009] According to another aspect of the present invention, an image processing apparatus is provided, the image processing apparatus comprising:

[0010] The local similarity density determination module is used to determine the local similarity density corresponding to each pixel based on the target similarity between each pixel and other pixels in the target image.

[0011] The pixel filtering module is used to filter pixels in the target image based on the local similarity density corresponding to each pixel to obtain the target cluster center;

[0012] The image segmentation processing module is used to segment the target image based on the target cluster center.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the image processing method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the image processing method according to any embodiment of the present invention.

[0018] This invention improves image segmentation accuracy by determining the local similarity density of each pixel based on the target similarity between each pixel and other pixels in the target image; filtering pixels in the target image based on the local similarity density of each pixel to obtain target cluster centers; and performing segmentation processing on the target image based on the target cluster centers.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of an image processing method according to an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of the structure of an image processing device according to an embodiment of the present invention;

[0023] Figure 3This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0027] Example 1

[0028] Figure 1 This is a flowchart illustrating an image processing method provided in an embodiment of the present invention. This embodiment is applicable to image processing situations. The method can be executed by an image processing device in this embodiment, which can be implemented in software and / or hardware, such as... Figure 1 As shown, the method specifically includes the following steps:

[0029] S110, determine the local similarity density corresponding to each pixel based on the target similarity between each pixel and other pixels in the target image.

[0030] Specifically, determining the local similarity density of each pixel based on the target similarity between each pixel and other pixels in the target image can be achieved by: obtaining the distance similarity and grayscale similarity between each pixel and other pixels in the target image; determining the target similarity between each pixel and other pixels based on the distance similarity and grayscale similarity; and determining the local similarity density of each pixel based on the target similarity between each pixel and other pixels in the target image. Alternatively, determining the local similarity density of each pixel based on the target similarity between each pixel and other pixels in the target image can also be achieved by: obtaining calibration parameters; and determining the local similarity density of each pixel based on the calibration parameters and the target similarity between each pixel and other pixels in the target image.

[0031] S120: Filter the pixels in the target image according to the local similarity density corresponding to each pixel to obtain the target cluster center.

[0032] The target pixel is a pixel in the target image.

[0033] Specifically, the method of filtering pixels in the target image based on the local similarity density of each pixel to obtain the target cluster center can be as follows: The pixel with the highest local similarity density in the target image is identified as the target pixel; a set of pixels is obtained, comprising all pixels in the target image except for the pixel with the highest local similarity density; the set of pixels is filtered based on the distance between each pixel and other pixels and the local similarity density of each pixel in the set to obtain the target pixel; and the target cluster center is determined based on the target pixel. Alternatively, the method of filtering pixels in the target image based on the local similarity density of each pixel to obtain the target cluster center can be as follows: an initial cluster center is determined based on the local similarity density of each pixel; the target similarity between the initial cluster center and other pixels is obtained; the intuitionistic fuzzy membership degree from other pixels in the target image (excluding the initial cluster center) to each pixel in the initial cluster center is obtained; and the initial cluster center is updated based on the intuitionistic fuzzy membership degree to obtain the target cluster center. Another method for obtaining target cluster centers by filtering pixels in the target image based on the local similarity density of each pixel is as follows: The pixel with the highest local similarity density in the target image is determined as the initial pixel; a set of pixels is obtained, comprising all pixels in the target image except for the pixel with the highest local similarity density; the set of pixels is filtered based on the distance between each pixel and other pixels and the local similarity density of each pixel to obtain the initial pixels; the initial cluster centers are determined based on the initial pixels, and the target similarity between the initial cluster centers and other pixels is obtained; the intuitionistic fuzzy membership degrees of other pixels in the target image (excluding the initial cluster centers) to the initial cluster centers are obtained; the initial cluster centers are updated based on the intuitionistic fuzzy membership degrees to obtain the target cluster centers.

[0034] S130, the target image is segmented according to the target cluster center.

[0035] Specifically, the method for segmenting the target image based on the target cluster center can be as follows: obtain the intuitionistic fuzzy membership degree, and segment the target image based on the target cluster center and the intuitionistic fuzzy membership degree.

[0036] Optionally, before determining the local similarity density corresponding to each pixel based on the target similarity between each pixel and other pixels in the target image, the method further includes:

[0037] Obtain the distance similarity and grayscale similarity between each pixel in the target image and other pixels;

[0038] The target similarity between each pixel and other pixels is determined based on the distance similarity and grayscale similarity between each pixel and other pixels.

[0039] Specifically, the method for obtaining the distance similarity between each pixel in the target image and other pixels can be as follows: determine the distance similarity between each pixel in the target image and other pixels based on the distance between each pixel in the target image and other pixels. For example, it can be to obtain a first adaptive parameter and determine the distance similarity between each pixel in the target image and other pixels based on the first adaptive parameter and the distance between each pixel in the target image and other pixels.

[0040] Specifically, the method for obtaining the grayscale similarity between each pixel in the target image and other pixels can be as follows: obtain the difference in grayscale values ​​between each pixel in the target image and other pixels, and determine the grayscale similarity between each pixel in the target image and other pixels based on the difference in grayscale values ​​between each pixel in the target image and other pixels. For example, it can be to obtain a second adaptive parameter, and determine the grayscale similarity between each pixel in the target image and other pixels based on the second adaptive parameter and the difference in grayscale values ​​between each pixel in the target image and other pixels.

[0041] Specifically, the target similarity between each pixel and other pixels can be determined by multiplying the distance similarity and grayscale similarity between each pixel and other pixels.

[0042] Optionally, the local similarity density corresponding to each pixel is determined based on the target similarity between each pixel and other pixels in the target image, including:

[0043] Obtain calibration parameters;

[0044] The local similarity density corresponding to each pixel is determined based on the calibration parameters and the target similarity between each pixel and other pixels in the target image.

[0045] The calibration parameters can be pre-set constants, for example, the local similarity density corresponding to each pixel can be determined based on the following formula:

[0046]

[0047] Where, ρ i Sim||x represents the local similarity density for each pixel. i ,xj || represents the target similarity between each pixel in the target image and other pixels. c These are calibration parameters.

[0048] Optionally, pixels in the target image are filtered based on the local similarity density corresponding to each pixel to obtain target cluster centers, including:

[0049] The pixel with the highest local similarity density in the target image is identified as the target pixel.

[0050] Obtain a set of pixels, wherein the set of pixels includes all pixels in the target image except for the pixel with the highest local similarity density;

[0051] The target pixels are obtained by filtering the pixel set based on the distance between each pixel and other pixels and the local similarity density of each pixel in the pixel set;

[0052] The target cluster center is determined based on the target pixel.

[0053] Specifically, the method for determining the pixel with the highest local similarity density in the target image as the target pixel can be as follows: sort the pixels in the target image in descending order of local similarity density to obtain a pixel list, and determine the first pixel in the pixel list as the target pixel.

[0054] Specifically, the method for obtaining the pixel set can be as follows: obtain all pixels in the target image except for the pixels with the highest local similarity density, and generate a pixel set based on all pixels in the target image except for the pixels with the highest local similarity density.

[0055] Specifically, the pixel set is filtered based on the distance between each pixel and other pixels and the local similarity density of each pixel in the pixel set. The method to obtain the target pixel is as follows: the pixel in the pixel set whose local similarity density is greater than the density threshold and whose distance from other pixels is greater than the distance threshold is determined as the target pixel.

[0056] Specifically, the method for determining the target cluster center based on the target pixels can be as follows: generate the target cluster center based on the target pixels. Alternatively, the method can be as follows: determine an initial cluster center based on the target pixels, obtain the target similarity between the initial cluster center and other pixels; obtain the intuitionistic fuzzy membership degree of each pixel in the initial cluster center from all pixels in the target image except the initial cluster center; update the initial cluster center based on the intuitionistic fuzzy membership degree and the target similarity to obtain the target cluster center.

[0057] Optionally, pixels in the target image are filtered based on the local similarity density corresponding to each pixel to obtain target cluster centers, including:

[0058] The initial cluster centers are determined based on the local similarity density corresponding to each pixel.

[0059] Obtain the target similarity between the initial cluster center and other pixels;

[0060] Obtain the intuitive fuzzy membership degree of all pixels in the target image except the initial cluster center to the initial cluster center;

[0061] The initial cluster centers are updated based on the intuitionistic fuzzy membership degree and the target similarity to obtain the target cluster centers.

[0062] Specifically, the method for determining the initial cluster center based on the local similarity density corresponding to each pixel can be as follows: the pixel with the highest local similarity density in the target image is determined as the initial pixel; a set of pixels is obtained, wherein the set of pixels includes all pixels in the target image except for the pixel with the highest local similarity density; the set of pixels is filtered according to the distance between each pixel and other pixels and the local similarity density corresponding to each pixel in the set to obtain the initial pixel; the initial cluster center is determined based on the initial pixel.

[0063] Specifically, the method for obtaining the target similarity between the initial cluster center and other pixels can be as follows: obtain the distance similarity and grayscale similarity between the initial cluster center and other pixels; determine the target similarity between each pixel in the initial cluster center set and other pixels in the target image based on the distance similarity and grayscale similarity between each pixel in the initial cluster center set and other pixels in the target image.

[0064] Specifically, the method for updating the initial cluster centers based on the intuitionistic fuzzy membership degree and the target similarity to obtain the target cluster centers can be as follows: determine the objective function based on the intuitionistic fuzzy membership degree and the target similarity; update the intuitionistic fuzzy membership degree based on the objective function; update the initial cluster centers based on the updated intuitionistic fuzzy membership degree to obtain the target cluster centers.

[0065] Optionally, obtaining the target similarity between the initial cluster center and other pixels includes:

[0066] Obtain the distance similarity and grayscale similarity between the initial cluster center and other pixels;

[0067] The target similarity between the initial cluster and other pixels is determined based on the distance similarity and grayscale similarity between the initial cluster center and other pixels.

[0068] Specifically, the method for obtaining the distance similarity between the initial cluster center and other pixels can be as follows: determine the distance similarity between the initial cluster center and other pixels based on the distance between the initial cluster center and other pixels. For example, it can be to obtain a first adaptive parameter and determine the distance similarity between the initial cluster center and other pixels based on the first adaptive parameter and the distance between the initial cluster center and other pixels.

[0069] Specifically, the target similarity between the initial cluster center and other pixels can be determined by multiplying the distance similarity and grayscale similarity between the initial cluster center and other pixels.

[0070] Optionally, the initial cluster centers are updated based on the intuitionistic fuzzy membership degree and the target similarity to obtain the target cluster centers, including:

[0071] The objective function is determined based on the intuitive fuzzy membership degree and the target similarity.

[0072] The intuitive fuzzy membership degree is updated based on the objective function;

[0073] The initial cluster centers are updated based on the updated intuitionistic fuzzy membership degrees to obtain the target cluster centers.

[0074] Specifically, the objective function can be determined based on the intuitionistic fuzzy membership degree and the target similarity as follows: determine the target similarity between each initial cluster center and other pixels based on the distance similarity and grayscale similarity between each initial cluster center and other pixels; obtain the intuitionistic fuzzy membership degree from other pixels to the initial cluster center; determine the similarity parameter based on the target similarity between each initial cluster center and other pixels and the intuitionistic fuzzy membership degree from other pixels to the initial cluster center; and determine the objective function based on the intuitionistic fuzzy membership degree from other pixels to the initial cluster center, the similarity parameter, and the degree of hesitation.

[0075] Specifically, updating the intuitionistic fuzzy membership degree based on the objective function can be achieved by: updating the intuitionistic fuzzy membership degree from other pixels to the cluster center according to the objective function, thus obtaining the updated intuitionistic fuzzy membership degree from other pixels to the cluster center. Alternatively, updating the intuitionistic fuzzy membership degree based on the objective function can be achieved by: determining a membership update function based on the objective function, and updating the intuitionistic fuzzy membership degree from other pixels to the cluster center based on the membership update function, thus obtaining the updated intuitionistic fuzzy membership degree from other pixels to the cluster center.

[0076] Specifically, the method to update the initial cluster centers based on the updated intuitionistic fuzzy membership to obtain the target cluster centers can be as follows: determine the cluster center update function based on the objective function, substitute the updated intuitionistic fuzzy membership into the cluster center update function, and obtain the target cluster centers.

[0077] The technical solution of this embodiment determines the local similarity density corresponding to each pixel based on the target similarity between each pixel and other pixels in the target image; filters the pixels in the target image based on the local similarity density corresponding to each pixel to obtain target cluster centers; and performs segmentation processing on the target image based on the target cluster centers, which can improve the accuracy of image segmentation.

[0078] Example 2

[0079] Figure 2 This is a schematic diagram of an image processing apparatus provided in an embodiment of the present invention. This embodiment is applicable to image processing applications. The apparatus can be implemented using software and / or hardware, and can be integrated into any device that provides image processing functionality, such as… Figure 2 As shown, the image processing device specifically includes: a local similarity density determination module 210, a pixel point filtering module 220, and an image segmentation processing module 230.

[0080] The local similarity density determination module is used to determine the local similarity density of each pixel based on the target similarity between each pixel and other pixels in the target image.

[0081] The pixel filtering module is used to filter pixels in the target image based on the local similarity density corresponding to each pixel to obtain the target cluster center;

[0082] The image segmentation processing module is used to segment the target image based on the target cluster center.

[0083] The above-described products can perform the methods provided in any embodiment of the present invention, and have the corresponding functional modules and beneficial effects for performing the methods.

[0084] Example 3

[0085] Figure 3 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0086] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0087] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0088] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as image processing methods.

[0089] In some embodiments, the image processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the image processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the image processing method by any other suitable means (e.g., by means of firmware).

[0090] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0091] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0092] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0093] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0094] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0095] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0096] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0097] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An image processing method, characterized in that, include: The local similarity density of each pixel is determined based on the target similarity between each pixel and other pixels in the target image. The target cluster centers are obtained by filtering the pixels in the target image based on the local similarity density corresponding to each pixel. The target image is segmented based on the target cluster centers; The step of determining the local similarity density of each pixel based on the target similarity between each pixel and other pixels in the target image includes: The distance similarity between each pixel in the target image and other pixels is determined based on the distance between each pixel and other pixels in the target image. Obtain the difference in grayscale value between each pixel and other pixels in the target image, and determine the grayscale similarity between each pixel and other pixels in the target image based on the difference in grayscale value between each pixel and other pixels in the target image; The product of the distance similarity and grayscale similarity between each pixel and other pixels is determined as the target similarity between each pixel and other pixels; Obtain calibration parameters; The local similarity density corresponding to each pixel is determined based on the calibration parameters and the target similarity between each pixel and other pixels in the target image.

2. The method according to claim 1, characterized in that, Pixels in the target image are filtered based on the local similarity density corresponding to each pixel to obtain target cluster centers, including: The pixel with the highest local similarity density in the target image is identified as the target pixel. Obtain a set of pixels, wherein the set of pixels includes all pixels in the target image except for the pixel with the highest local similarity density; The target pixels are obtained by filtering the pixel set based on the distance between each pixel and other pixels and the local similarity density of each pixel in the pixel set; The target cluster center is determined based on the target pixel.

3. The method according to claim 1, characterized in that, Pixels in the target image are filtered based on the local similarity density corresponding to each pixel to obtain target cluster centers, including: The initial cluster centers are determined based on the local similarity density corresponding to each pixel. Obtain the target similarity between the initial cluster center and other pixels; Obtain the intuitive fuzzy membership degree of all pixels in the target image except the initial cluster center to the initial cluster center; The initial cluster centers are updated based on the intuitionistic fuzzy membership degree and the target similarity to obtain the target cluster centers.

4. The method according to claim 3, characterized in that, The initial cluster centers are updated based on the intuitionistic fuzzy membership degree and the target similarity to obtain the target cluster centers, including: The objective function is determined based on the intuitive fuzzy membership degree and the target similarity. The intuitive fuzzy membership degree is updated based on the objective function; The initial cluster centers are updated based on the updated intuitionistic fuzzy membership degrees to obtain the target cluster centers.

5. An image processing apparatus, characterized in that, include: The local similarity density determination module is used to determine the local similarity density corresponding to each pixel based on the target similarity between each pixel and other pixels in the target image. The pixel filtering module is used to filter pixels in the target image based on the local similarity density corresponding to each pixel to obtain the target cluster center; An image segmentation processing module is used to segment the target image based on the target cluster center; The local similarity density determination module is specifically used for: The distance similarity between each pixel in the target image and other pixels is determined based on the distance between each pixel and other pixels in the target image. Obtain the difference in grayscale value between each pixel and other pixels in the target image, and determine the grayscale similarity between each pixel and other pixels in the target image based on the difference in grayscale value between each pixel and other pixels in the target image; The product of the distance similarity and grayscale similarity between each pixel and other pixels is determined as the target similarity between each pixel and other pixels; Obtain calibration parameters; The local similarity density corresponding to each pixel is determined based on the calibration parameters and the target similarity between each pixel and other pixels in the target image.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the image processing method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the image processing method according to any one of claims 1-4.

Images