CN107945202B - Image segmentation method and device based on adaptive threshold value and computing equipment - Google Patents
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Abstract
The invention discloses an image segmentation method, an image segmentation device, a computing device and a computer storage medium based on an adaptive threshold, wherein the method comprises the following steps: acquiring an image to be processed containing a specific object; carrying out scene segmentation processing on an image to be processed to obtain foreground probability information aiming at a specific object; determining the foreground area ratio according to the foreground probability information; and mapping the foreground probability information according to the foreground area ratio to obtain an image segmentation result. According to the technical scheme provided by the invention, the foreground probability information is mapped according to the foreground area ratio, the self-adaptive mapping of the foreground probability information is realized, the image segmentation result corresponding to the image to be processed can be quickly and accurately obtained by using the mapped foreground probability information, and the segmentation precision and the processing efficiency of the image scene segmentation are effectively improved.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an image segmentation method and device based on an adaptive threshold, computing equipment and a computer storage medium.
Background
In the prior art, when a user needs to perform personalized processing such as background replacement, special effect addition and the like on an image to be processed, an image segmentation method is often used for performing scene segmentation processing on the image to be processed, wherein a pixel-level segmentation effect can be achieved by using the image segmentation method based on deep learning. However, when the existing image segmentation method is used for scene segmentation processing, the proportion of the foreground image in the image to be processed is not considered, so when the proportion of the foreground image in the image to be processed is small, the existing image segmentation method is used for easily dividing pixel points which actually belong to the edge of the foreground image into background images, and the obtained image segmentation result is low in segmentation precision and poor in segmentation effect. Therefore, the image segmentation method in the prior art has the problem of low segmentation precision of image scene segmentation.
Disclosure of Invention
In view of the above, the present invention has been developed to provide an adaptive threshold based image segmentation method, apparatus, computing device and computer storage medium that overcome or at least partially address the above-identified problems.
According to an aspect of the present invention, there is provided an adaptive threshold based image segmentation method, comprising:
acquiring an image to be processed containing a specific object;
carrying out scene segmentation processing on an image to be processed to obtain foreground probability information aiming at a specific object;
determining the foreground area ratio according to the foreground probability information;
and mapping the foreground probability information according to the foreground area ratio to obtain an image segmentation result.
Further, the foreground probability information records the probability that each pixel point in the image to be processed belongs to the foreground image.
Further, according to the foreground probability information, determining the foreground region proportion further includes:
determining pixel points belonging to the foreground image according to the foreground probability information;
and calculating the proportion of the pixel points belonging to the foreground image in all the pixel points in the image to be processed, and determining the proportion as the foreground area ratio.
Further, according to the foreground probability information, determining pixel points belonging to the foreground image further includes:
and determining the pixel points with the probability higher than a preset probability threshold in the foreground probability information as the pixel points belonging to the foreground image.
Further, mapping the foreground probability information according to the foreground region ratio to obtain an image segmentation result, further comprising:
adjusting parameters of the mapping function according to the ratio of the foreground area;
mapping the foreground probability information by using the adjusted mapping function to obtain a mapping result;
and obtaining an image segmentation result according to the mapping result.
Further, the slope of the mapping function in the preset defined interval is greater than a preset slope threshold.
Further, after obtaining the image segmentation result, the method further comprises:
determining a processed foreground image according to an image segmentation result;
and carrying out fusion processing on the processed foreground image and a preset background image to obtain a processed image.
According to another aspect of the present invention, there is provided an adaptive threshold-based image segmentation apparatus, comprising:
the acquisition module is suitable for acquiring an image to be processed containing a specific object;
the segmentation module is suitable for carrying out scene segmentation processing on an image to be processed to obtain foreground probability information aiming at a specific object;
the first determining module is suitable for determining the foreground area ratio according to the foreground probability information;
and the mapping processing module is suitable for mapping the foreground probability information according to the foreground area ratio to obtain an image segmentation result.
Further, the foreground probability information records the probability that each pixel point in the image to be processed belongs to the foreground image.
Further, the first determination module is further adapted to:
determining pixel points belonging to the foreground image according to the foreground probability information;
and calculating the proportion of the pixel points belonging to the foreground image in all the pixel points in the image to be processed, and determining the proportion as the foreground area ratio.
Further, the first determination module is further adapted to:
and determining the pixel points with the probability higher than a preset probability threshold in the foreground probability information as the pixel points belonging to the foreground image.
Further, the mapping processing module comprises: an adjusting unit, a mapping unit and a generating unit;
the adjustment unit is adapted to: adjusting parameters of the mapping function according to the ratio of the foreground area;
the mapping unit is adapted to: mapping the foreground probability information by using the adjusted mapping function to obtain a mapping result;
the generation unit is adapted to: and obtaining an image segmentation result according to the mapping result.
Further, the slope of the mapping function in the preset defined interval is greater than a preset slope threshold.
Further, the apparatus further comprises:
the second determining module is suitable for determining the processed foreground image according to the image segmentation result;
and the fusion processing module is suitable for fusing the processed foreground image and the preset background image to obtain a processed image.
According to yet another aspect of the present invention, there is provided a computing device comprising: the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the image segmentation method based on the adaptive threshold.
According to yet another aspect of the present invention, a computer storage medium is provided, wherein at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to perform operations corresponding to the adaptive threshold based image segmentation method as described above.
According to the technical scheme provided by the invention, the image to be processed containing the specific object is obtained, then the image to be processed is subjected to scene segmentation processing to obtain the foreground probability information aiming at the specific object, then the foreground region proportion is determined according to the foreground probability information, and finally the foreground probability information is subjected to mapping processing according to the foreground region proportion to obtain the image segmentation result. According to the technical scheme provided by the invention, the foreground probability information is mapped according to the foreground area ratio, the self-adaptive mapping of the foreground probability information is realized, the image segmentation result corresponding to the image to be processed can be quickly and accurately obtained by using the mapped foreground probability information, the segmentation precision and the processing efficiency of the image scene segmentation are effectively improved, and the image scene segmentation processing mode is optimized.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 shows a flow diagram of an adaptive threshold based image segmentation method according to an embodiment of the invention;
FIG. 2 shows a schematic flow diagram of an adaptive threshold based image segmentation method according to another embodiment of the present invention;
FIG. 3 is a block diagram illustrating an adaptive threshold based image segmentation apparatus according to an embodiment of the present invention;
FIG. 4 shows a schematic structural diagram of a computing device according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Fig. 1 shows a flow chart of an adaptive threshold based image segmentation method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S100, acquiring an image to be processed including a specific object.
The image to be processed is an image that the user wants to perform scene segmentation, and the image to be processed may be any image, for example, the image to be processed may be an image shot by the user, an image in a website, or an image shared by other users, which is not limited herein. The image to be processed contains a specific object, and the specific object can be a human body and the like. The specific object can be set by those skilled in the art according to actual needs, and is not limited herein. When the user wants to perform scene segmentation on the image to be processed, then the image to be processed is acquired in step S100.
Step S101, carrying out scene segmentation processing on an image to be processed to obtain foreground probability information aiming at a specific object.
When the image to be processed is subjected to scene segmentation processing, a deep learning method can be utilized. Deep learning is a method based on characterization learning of data in machine learning. An observation (e.g., an image) may be represented using a number of ways, such as a vector of intensity values for each pixel, or more abstractly as a series of edges, a specially shaped region, etc. And tasks are easier to learn from the examples using some specific representation methods. The scene segmentation processing can be carried out on the image to be processed by utilizing a segmentation method of deep learning, and foreground probability information of the image to be processed aiming at a specific object is obtained. Specifically, a scene segmentation network or the like obtained by using a deep learning method may be used to perform scene segmentation processing on the image to be processed, so as to obtain foreground probability information of the image to be processed for a specific object, where the foreground probability information records a probability that each pixel in the image to be processed belongs to the foreground image, and specifically, a value range of the probability that each pixel belongs to the foreground image may be [0, 1 ].
In the present invention, the foreground image may only contain the specific object, and the background image is an image other than the foreground image in the image to be processed. According to the foreground probability information, which pixel points in the image to be processed belong to the foreground image, which pixel points belong to the background image, and which pixel points may belong to both the foreground image and the background image. For example, if the foreground probability information corresponding to a certain pixel point is close to 0, it is indicated that the pixel point belongs to a background image; if the foreground probability information corresponding to a certain pixel point is close to 1, the pixel point is indicated to belong to a foreground image; if the foreground probability information corresponding to a certain pixel point is close to 0.5, it is indicated that the pixel point may belong to both the foreground image and the background image.
And S102, determining the foreground area ratio according to the foreground probability information.
The foreground area ratio is used for reflecting the proportion of the occupied area of the foreground image in the image to be processed. Because the foreground probability information records the probability for reflecting that each pixel point in the image to be processed belongs to the foreground image, which pixel points in the image to be processed belong to the foreground image can be determined according to the foreground probability information, and therefore the foreground area ratio is determined.
And step S103, mapping the foreground probability information according to the foreground area ratio to obtain an image segmentation result.
After the foreground region occupation ratio is obtained, performing adaptive mapping processing on the foreground probability information according to the foreground region occupation ratio, for example, when the foreground region occupation ratio is smaller, for example, the foreground region occupation ratio is 0.2, which indicates that the area occupied by the foreground image in the image to be processed is smaller, performing mapping processing on the foreground probability information, adaptively mapping the smaller probability in the foreground probability information to a larger probability, and adaptively mapping the larger probability in the foreground probability information to a smoother probability; for another example, when the foreground region accounts for a relatively large area, for example, the foreground region accounts for 0.8, which indicates that the area of the foreground image in the image to be processed is relatively large, the foreground probability information may be mapped, and the probability in the foreground probability information is adaptively mapped to a relatively smooth probability. Compared with the prior art, the processing method provided by the invention can effectively improve the segmentation precision of the image scene segmentation and enable the segmentation edge to be smoother.
According to the image segmentation method based on the adaptive threshold provided by the embodiment, an image to be processed containing a specific object is obtained, then scene segmentation processing is performed on the image to be processed to obtain foreground probability information for the specific object, then a foreground region proportion is determined according to the foreground probability information, and finally mapping processing is performed on the foreground probability information according to the foreground region proportion to obtain an image segmentation result. According to the technical scheme provided by the invention, the foreground probability information is mapped according to the foreground area ratio, the self-adaptive mapping of the foreground probability information is realized, the image segmentation result corresponding to the image to be processed can be quickly and accurately obtained by using the mapped foreground probability information, the segmentation precision and the processing efficiency of the image scene segmentation are effectively improved, and the image scene segmentation processing mode is optimized.
Fig. 2 shows a flow chart of an adaptive threshold based image segmentation method according to another embodiment of the present invention, as shown in fig. 2, the method comprises the following steps:
step S200, acquiring an image to be processed containing a specific object.
Alternatively, in step S200, the image to be processed including the specific object captured by the image capturing device may be acquired in real time. Specifically, the image acquisition device may be a mobile terminal or the like, and taking the image acquisition device as the mobile terminal as an example, the image to be processed captured by the camera of the mobile terminal is acquired in real time, where the image to be processed includes a specific object, and the specific object may be a human body or the like.
Step S201, performing scene segmentation processing on the image to be processed to obtain foreground probability information for the specific object.
Step S202, determining pixel points belonging to the foreground image according to the foreground probability information.
The foreground probability information records the probability for reflecting that each pixel point in the image to be processed belongs to the foreground image, and the value range of the probability for each pixel point to belong to the foreground image can be [0, 1 ]. Specifically, the pixel points with the probability higher than the preset probability threshold in the foreground probability information can be determined as the pixel points belonging to the foreground image, and a person skilled in the art can set the preset probability threshold according to actual needs, which is not limited here. For example, when the preset probability threshold is 0.7, the pixel point with foreground probability information higher than 0.7 may be determined as the pixel point belonging to the foreground image.
Step S203, calculating the proportion of the pixel points belonging to the foreground image in all the pixel points in the image to be processed, and determining the proportion as the foreground area proportion.
Specifically, the number of pixels belonging to the foreground image and the number of all pixels in the image to be processed may be calculated, and the ratio of the number of pixels belonging to the foreground image to the number of all pixels is the foreground region occupation ratio.
And step S204, adjusting parameters of the mapping function according to the foreground area ratio.
The mapping function may be used to map the foreground probability information, and a person skilled in the art may set the mapping function according to actual needs, which is not limited herein. For example, the mapping function may be a piecewise linear transformation function or a non-linear transformation function. And for different foreground area ratios, the parameters of the corresponding mapping functions are different.
Specifically, when the foreground region occupies a smaller area, it indicates that the area occupied by the foreground image in the image to be processed is smaller, and then in step S204, the parameters of the mapping function are adjusted according to the foreground region occupation ratio, so that when the foreground probability information is mapped by using the adjusted mapping function, the smaller probability in the foreground probability information can be adaptively mapped to the larger probability, and the larger probability in the foreground probability information can be adaptively mapped to the smoother probability; when the foreground region accounts for a relatively large area, which indicates that the area of the foreground image in the image to be processed is relatively large, in step S204, the parameters of the mapping function are adjusted according to the foreground region accounts, so that when the foreground probability information is mapped by using the adjusted mapping function, the probability in the foreground probability information can be adaptively mapped to a relatively smooth probability.
And the slope of the mapping function in the preset defined interval is greater than a preset slope threshold value. A person skilled in the art may set the preset definition interval and the preset slope threshold according to actual needs, which is not limited herein, for example, when the preset definition interval is (0, 0.5) and the preset slope threshold is 1, the slope of the mapping function in the definition interval (0, 0.5) is greater than 1, so that a smaller probability in the foreground probability information can be adaptively mapped to a larger probability, for example, 0.1 is mapped to 0.3.
Taking the mapping function as a non-linear transformation function as an example, in a specific embodiment, the specific formula may be as follows:
y＝1/(1+exp(-(k*x-a)))
the foreground region proportion is a foreground region proportion, k is a first parameter, a is a second parameter, specifically, the first parameter is a parameter which needs to be adjusted according to the foreground region proportion, and the second parameter is a preset fixed parameter. Assuming that the foreground region occupancy is represented by the parameter r, k may be set to 2/r and a may be set to 4, so that the corresponding value of k may be different for different foreground region occupancies.
And step S205, carrying out mapping processing on the foreground probability information by using the adjusted mapping function to obtain a mapping result.
After the mapping function is adjusted, the foreground probability information can be used as an independent variable of the adjusted mapping function, and the obtained function value is the mapping result.
And step S206, obtaining an image segmentation result according to the mapping result.
After the mapping result is obtained, an image segmentation result can be obtained according to the mapping result. Compared with the prior art, the image segmentation result obtained according to the mapping result has higher segmentation precision and smoother segmentation edge.
And step S207, determining the processed foreground image according to the image segmentation result.
And clearly determining which pixel points in the image to be processed belong to the foreground image and which pixel points belong to the background image according to the image segmentation result, thereby determining the processed foreground image.
And S208, fusing the processed foreground image and a preset background image to obtain a processed image.
After the processed foreground image is obtained, the processed foreground image and the preset background image can be subjected to fusion processing, so that a processed image corresponding to the image to be processed is obtained. The skilled person can set the preset background image according to the actual need, which is not limited herein. The preset background image may be a two-dimensional scene background image, or may be a three-dimensional scene background image, such as a three-dimensional submarine scene background image, a three-dimensional volcanic scene background image, or the like.
According to the image segmentation method based on the adaptive threshold value provided by the embodiment, parameters of the mapping function can be adjusted according to the foreground region occupation ratio, so that the parameters of the corresponding mapping function are different when the foreground region occupation ratio is different, and the adaptive mapping of foreground probability information according to the foreground region occupation ratio is realized; and the mapping result can be used for quickly and accurately obtaining the image segmentation result corresponding to the image to be processed, so that the segmentation precision and the processing efficiency of image scene segmentation are effectively improved, the segmentation edge is smoother, the display effect of the image after fusion processing is improved, and the image is more natural and real.
Fig. 3 is a block diagram illustrating a structure of an adaptive threshold based image segmentation apparatus according to an embodiment of the present invention, as shown in fig. 3, the apparatus including: an acquisition module 310, a segmentation module 320, a first determination module 330, and a mapping processing module 340.
The acquisition module 310 is adapted to: an image to be processed containing a specific object is acquired.
The segmentation module 320 is adapted to: and carrying out scene segmentation processing on the image to be processed to obtain foreground probability information aiming at the specific object.
The foreground probability information records the probability of each pixel point in the image to be processed belonging to the foreground image.
The first determination module 330 is adapted to: and determining the foreground area ratio according to the foreground probability information.
Wherein the first determining module 330 is further adapted to: determining pixel points belonging to the foreground image according to the foreground probability information; and calculating the proportion of the pixel points belonging to the foreground image in all the pixel points in the image to be processed, and determining the proportion as the foreground area ratio. Specifically, the first determining module 330 determines the pixel points with the probability higher than the preset probability threshold in the foreground probability information as the pixel points belonging to the foreground image.
The mapping processing module 340 is adapted to: and mapping the foreground probability information according to the foreground area ratio to obtain an image segmentation result.
In a particular embodiment, the mapping processing module 340 may include: an adjusting unit 341, a mapping unit 342, and a generating unit 343.
The adjusting unit 341 is adapted to: and adjusting parameters of the mapping function according to the foreground area ratio. And the slope of the mapping function in the preset defined interval is greater than a preset slope threshold value.
The mapping unit 342 is adapted to: and mapping the foreground probability information by using the adjusted mapping function to obtain a mapping result.
The generating unit 343 is adapted to: and obtaining an image segmentation result according to the mapping result.
The apparatus may further comprise: a second determination module 350 and a fusion processing module 360. Wherein the second determination module 350 is adapted to: and determining the processed foreground image according to the image segmentation result. The fusion processing module 360 is adapted to: and carrying out fusion processing on the processed foreground image and a preset background image to obtain a processed image.
According to the image segmentation device based on the adaptive threshold provided by the embodiment, the acquisition module acquires an image to be processed containing a specific object, the segmentation module performs scene segmentation processing on the image to be processed to obtain foreground probability information for the specific object, the first determination module determines the foreground region proportion according to the foreground probability information, and the mapping processing module performs mapping processing on the foreground probability information according to the foreground region proportion to obtain an image segmentation result. According to the technical scheme provided by the invention, the foreground probability information is mapped according to the foreground area ratio, the self-adaptive mapping of the foreground probability information is realized, the image segmentation result corresponding to the image to be processed can be quickly and accurately obtained by using the mapped foreground probability information, the segmentation precision and the processing efficiency of the image scene segmentation are effectively improved, and the image scene segmentation processing mode is optimized.
The invention also provides a non-volatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the executable instruction can execute the image segmentation method based on the adaptive threshold value in any method embodiment.
Fig. 4 is a schematic structural diagram of a computing device according to an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the computing device.
As shown in fig. 4, the computing device may include: a processor (processor)402, a Communications Interface 404, a memory 406, and a Communications bus 408.
Wherein:
the processor 402, communication interface 404, and memory 406 communicate with each other via a communication bus 408.
A communication interface 404 for communicating with network elements of other devices, such as clients or other servers.
The processor 402 is configured to execute the program 410, and may specifically execute the relevant steps in the above-described embodiment of the adaptive threshold-based image segmentation method.
In particular, program 410 may include program code comprising computer operating instructions.
The processor 402 may be a central processing unit CPU or an application Specific Integrated circuit asic or one or more Integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 406 for storing a program 410. Memory 406 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 410 may specifically be adapted to cause the processor 402 to perform an adaptive threshold based image segmentation method in any of the method embodiments described above. For specific implementation of each step in the procedure 410, reference may be made to corresponding descriptions in corresponding steps and units in the foregoing image segmentation embodiment based on adaptive threshold, which is not described herein again. It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described devices and modules may refer to the corresponding process descriptions in the foregoing method embodiments, and are not described herein again.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components in accordance with embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (14)
1. A method of adaptive threshold based image segmentation, the method comprising:
acquiring an image to be processed containing a specific object;
carrying out scene segmentation processing on the image to be processed to obtain foreground probability information aiming at a specific object;
determining the foreground area ratio according to the foreground probability information;
mapping the foreground probability information according to the foreground area ratio to obtain an image segmentation result;
the mapping processing of the foreground probability information according to the foreground region proportion to obtain an image segmentation result further comprises:
adjusting parameters of a mapping function according to the foreground area ratio;
mapping the foreground probability information by using the adjusted mapping function to obtain a mapping result;
and obtaining an image segmentation result according to the mapping result.
2. The method according to claim 1, wherein the foreground probability information records probability for reflecting each pixel point in the image to be processed belonging to a foreground image.
3. The method of claim 1 or 2, wherein said determining a foreground region proportion from said foreground probability information further comprises:
determining pixel points belonging to the foreground image according to the foreground probability information;
and calculating the proportion of the pixel points belonging to the foreground image in all the pixel points in the image to be processed, and determining the proportion as the foreground area ratio.
4. The method of claim 3, wherein said determining pixel points belonging to a foreground image according to the foreground probability information further comprises:
and determining the pixel points with the probability higher than a preset probability threshold in the foreground probability information as the pixel points belonging to the foreground image.
5. The method of claim 1, wherein a slope of the mapping function within a preset defined interval is greater than a preset slope threshold.
6. The method of claim 1, wherein after the obtaining the image segmentation result, the method further comprises:
determining a processed foreground image according to the image segmentation result;
and carrying out fusion processing on the processed foreground image and a preset background image to obtain a processed image.
7. An adaptive threshold-based image segmentation device, the device comprising:
the acquisition module is suitable for acquiring an image to be processed containing a specific object;
the segmentation module is suitable for carrying out scene segmentation processing on the image to be processed to obtain foreground probability information aiming at a specific object;
the first determining module is suitable for determining the foreground area ratio according to the foreground probability information;
the mapping processing module is suitable for mapping the foreground probability information according to the foreground area ratio to obtain an image segmentation result;
the mapping processing module comprises: an adjusting unit, a mapping unit and a generating unit;
the adjustment unit is adapted to: adjusting parameters of a mapping function according to the foreground area ratio;
the mapping unit is adapted to: mapping the foreground probability information by using the adjusted mapping function to obtain a mapping result;
the generation unit is adapted to: and obtaining an image segmentation result according to the mapping result.
8. The apparatus according to claim 7, wherein the foreground probability information records probability for reflecting each pixel point in the image to be processed belongs to foreground image.
9. The apparatus of claim 7 or 8, wherein the first determining module is further adapted to:
determining pixel points belonging to the foreground image according to the foreground probability information;
and calculating the proportion of the pixel points belonging to the foreground image in all the pixel points in the image to be processed, and determining the proportion as the foreground area ratio.
10. The apparatus of claim 9, wherein the first determining module is further adapted to:
and determining the pixel points with the probability higher than a preset probability threshold in the foreground probability information as the pixel points belonging to the foreground image.
11. The apparatus of claim 7, wherein a slope of the mapping function within a preset defined interval is greater than a preset slope threshold.
12. The apparatus of claim 7, wherein the apparatus further comprises:
the second determining module is suitable for determining the processed foreground image according to the image segmentation result;
and the fusion processing module is suitable for carrying out fusion processing on the processed foreground image and a preset background image to obtain a processed image.
13. A computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform operations corresponding to the adaptive threshold-based image segmentation method of any one of claims 1-6.
14. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the adaptive threshold based image segmentation method as claimed in any one of claims 1 to 6.
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