CN107945202A - Image partition method, device and computing device based on adaptive threshold - Google Patents
Image partition method, device and computing device based on adaptive threshold Download PDFInfo
- Publication number
- CN107945202A CN107945202A CN201711377314.3A CN201711377314A CN107945202A CN 107945202 A CN107945202 A CN 107945202A CN 201711377314 A CN201711377314 A CN 201711377314A CN 107945202 A CN107945202 A CN 107945202A
- Authority
- CN
- China
- Prior art keywords
- image
- probabilistic information
- foreground
- prospect
- prospect probabilistic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20212—Image combination
- G06T2207/20221—Image fusion; Image merging
Abstract
The invention discloses a kind of image partition method based on adaptive threshold, device, computing device and computer-readable storage medium, this method includes：Obtain the pending image for including special object；Scene cut processing is carried out to pending image, obtains the prospect probabilistic information for special object；According to prospect probabilistic information, foreground area accounting is determined；According to foreground area accounting, mapping processing is carried out to prospect probabilistic information, obtains image segmentation result.Technical solution provided by the invention carries out mapping processing according to foreground area accounting to prospect probabilistic information, realize the self organizing maps to prospect probabilistic information, prospect probabilistic information after being handled using mapping can quickly, accurately obtain the corresponding image segmentation result of pending image, be effectively improved the segmentation precision and treatment effeciency of image scene segmentation.
Description
Technical field
The present invention relates to technical field of image processing, and in particular to a kind of image partition method based on adaptive threshold,
Device, computing device and computer-readable storage medium.
Background technology
In the prior art, when user needs to carry out pending image replacement background, addition special efficacy when personalisation process,
Image partition method is often used to carry out scene cut processing to pending image, wherein, using based on deep learning
Image partition method can reach the segmentation effect of pixel scale.But existing image partition method is being carried out at scene cut
During reason, it is not intended that foreground image proportion in pending image, therefore it is shared in pending image to work as foreground image
When ratio is smaller, it is easy to the pixel for actually belonging to foreground image edge being divided into using existing image partition method
Background image, the segmentation precision of obtained image segmentation result is relatively low, segmentation effect is poor.Therefore, figure of the prior art
As partitioning scheme there is the segmentation precision of image scene segmentation it is low the problem of.
The content of the invention
In view of the above problems, it is proposed that the present invention overcomes the above problem in order to provide one kind or solves at least in part
State the image partition method based on adaptive threshold, device, computing device and the computer-readable storage medium of problem.
According to an aspect of the invention, there is provided a kind of image partition method based on adaptive threshold, this method bag
Include：
Obtain the pending image for including special object；
Scene cut processing is carried out to pending image, obtains the prospect probabilistic information for special object；
According to prospect probabilistic information, foreground area accounting is determined；
According to foreground area accounting, mapping processing is carried out to prospect probabilistic information, obtains image segmentation result.
Further, prospect probabilistic information have recorded for reflecting that each pixel belongs to foreground image in pending image
Probability.
Further, according to prospect probabilistic information, determine that foreground area accounting further comprises：
According to prospect probabilistic information, the pixel for belonging to foreground image is determined；
Ratio of the pixel for belonging to foreground image in pending image in all pixels point is calculated, is by ratio-dependent
Foreground area accounting.
Further, according to prospect probabilistic information, determine that the pixel for belonging to foreground image further comprises：
Probability in prospect probabilistic information is determined to belong to the pixel of foreground image higher than the pixel of predetermined probabilities threshold value
Point.
Further, according to foreground area accounting, mapping processing is carried out to prospect probabilistic information, obtains image segmentation result
Further comprise：
Foundation foreground area accounting, adjusts the parameter of mapping function；
Mapping processing is carried out to prospect probabilistic information using the mapping function after adjustment, obtains mapping result；
According to mapping result, image segmentation result is obtained.
Further, slope of the mapping function in default interval of definition is more than default slope threshold value.
Further, after image segmentation result is obtained, this method further includes：
According to image segmentation result, the foreground image after processing is determined；
Foreground image after processing and default background image are subjected to fusion treatment, the image after being handled.
According to another aspect of the present invention, there is provided a kind of image segmenting device based on adaptive threshold, the device bag
Include：
Acquisition module, suitable for obtaining the pending image for including special object；
Split module, suitable for carrying out scene cut processing to pending image, obtain the prospect probability for special object
Information；
First determining module, suitable for according to prospect probabilistic information, determining foreground area accounting；
Processing module is mapped, suitable for according to foreground area accounting, carrying out mapping processing to prospect probabilistic information, obtaining image
Segmentation result.
Further, prospect probabilistic information have recorded for reflecting that each pixel belongs to foreground image in pending image
Probability.
Further, the first determining module is further adapted for：
According to prospect probabilistic information, the pixel for belonging to foreground image is determined；
Ratio of the pixel for belonging to foreground image in pending image in all pixels point is calculated, is by ratio-dependent
Foreground area accounting.
Further, the first determining module is further adapted for：
Probability in prospect probabilistic information is determined to belong to the pixel of foreground image higher than the pixel of predetermined probabilities threshold value
Point.
Further, mapping processing module includes：Adjustment unit, map unit and generation unit；
Adjustment unit is suitable for：Foundation foreground area accounting, adjusts the parameter of mapping function；
Map unit is suitable for：Mapping processing is carried out to prospect probabilistic information using the mapping function after adjustment, is mapped
As a result；
Generation unit is suitable for：According to mapping result, image segmentation result is obtained.
Further, slope of the mapping function in default interval of definition is more than default slope threshold value.
Further, which further includes：
Second determining module, suitable for according to image segmentation result, determining the foreground image after processing；
Fusion treatment module, suitable for the foreground image after processing and default background image are carried out fusion treatment, obtains everywhere
Image after reason.
According to another aspect of the invention, there is provided a kind of computing device, including：Processor, memory, communication interface and
Communication bus, processor, memory and communication interface complete mutual communication by communication bus；
Memory is used to store an at least executable instruction, and executable instruction makes processor execution is above-mentioned to be based on adaptive thresholding
The corresponding operation of image partition method of value.
In accordance with a further aspect of the present invention, there is provided a kind of computer-readable storage medium, is stored with least one in storage medium
Executable instruction, executable instruction make processor perform such as the corresponding behaviour of the above-mentioned image partition method based on adaptive threshold
Make.
The technical solution provided according to the present invention, obtains the pending image for including special object, then to pending
Image carries out scene cut processing, obtains the prospect probabilistic information for special object, then according to prospect probabilistic information, determines
Foreground area accounting, it is last according to foreground area accounting, mapping processing is carried out to prospect probabilistic information, obtains image segmentation knot
Fruit.Technical solution provided by the invention carries out mapping processing according to foreground area accounting to prospect probabilistic information, realizes to preceding
The self organizing maps of scape probabilistic information, the prospect probabilistic information after being handled using mapping can quickly, accurately obtain pending
The corresponding image segmentation result of image, is effectively improved the segmentation precision and treatment effeciency of image scene segmentation, optimizes
Image scene segmentation processing mode.
Described above is only the general introduction of technical solution of the present invention, in order to better understand the technological means of the present invention,
And can be practiced according to the content of specification, and in order to allow above and other objects of the present invention, feature and advantage can
Become apparent, below especially exemplified by the embodiment of the present invention.
Brief description of the drawings
By reading the detailed description of hereafter preferred embodiment, it is various other the advantages of and benefit it is common for this area
Technical staff will be clear understanding.Attached drawing is only used for showing the purpose of preferred embodiment, and is not considered as to the present invention
Limitation.And in whole attached drawing, identical component is denoted by the same reference numerals.In the accompanying drawings：
Fig. 1 shows the flow signal of the image partition method according to an embodiment of the invention based on adaptive threshold
Figure；
Fig. 2 shows that the flow of the image partition method in accordance with another embodiment of the present invention based on adaptive threshold is shown
It is intended to；
Fig. 3 shows the structural frames of the image segmenting device according to an embodiment of the invention based on adaptive threshold
Figure；
Fig. 4 shows a kind of structure diagram of computing device according to embodiments of the present invention.
Embodiment
The exemplary embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although the disclosure is shown in attached drawing
Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here
Limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure
Completely it is communicated to those skilled in the art.
Fig. 1 shows the flow signal of the image partition method according to an embodiment of the invention based on adaptive threshold
Figure, as shown in Figure 1, this method comprises the following steps：
Step S100, obtains the pending image for including special object.
Wherein, pending image is wanted to carry out the image of scene cut for user, and pending image can be arbitrary image,
For example, pending image can be the image in the image of user oneself shooting or website, other users are can also be
The image shared, does not limit herein.Wherein, special object is included in pending image, special object can be human body etc..This
Field technology personnel can be configured special object according to being actually needed, and not limit herein.When user is wanted to pending
When image carries out scene cut, then obtain pending image in the step s 100.
Step S101, carries out scene cut processing to pending image, obtains the prospect probabilistic information for special object.
Wherein, when carrying out scene cut processing to pending image, deep learning method can be utilized.Deep learning is
It is a kind of based on the method that data are carried out with representative learning in machine learning.Observation (such as piece image) can use a variety of sides
Formula represents, such as vector of each pixel intensity value, or is more abstractively expressed as a series of sides, the region etc. of given shape.
And some specific method for expressing are used to be easier from example learning task.Place is treated using the dividing method of deep learning
Manage image and carry out scene cut processing, obtain the prospect probabilistic information that pending image is directed to special object.Specifically, can profit
Scene cut network obtained with deep learning method etc. carries out scene cut processing to pending image, obtains pending image
The prospect probabilistic information of special object is directed to, wherein, prospect probabilistic information have recorded each in pending image for reflecting
Pixel belongs to the probability of foreground image, specifically, each pixel belong to the probability of foreground image value range can be [0,
1]。
In the present invention, foreground image can only include special object, and background image is to remove foreground picture in pending image
Image as outside.It can distinguish which pixel in pending image belongs to foreground image according to prospect probabilistic information, which
Pixel belongs to background image, which pixel, which may both belong to foreground image, may also belong to background image.If for example, some
The corresponding prospect probabilistic information of pixel then illustrates that the pixel belongs to background image close to 0；Before if some pixel is corresponding
Scape probabilistic information then illustrates that the pixel belongs to foreground image close to 1；If the corresponding prospect probabilistic information of some pixel approaches
0.5, then illustrate that the pixel may both belong to foreground image and may also belong to background image.
Step S102, according to prospect probabilistic information, determines foreground area accounting.
Wherein, foreground area accounting is the ratio for reflecting foreground image occupied area in pending image.Due to
Prospect probabilistic information have recorded for reflecting that each pixel in pending image belongs to the probability of foreground image, therefore before
Scape probabilistic information is capable of determining that in pending image which pixel belongs to foreground image, accounts for so that it is determined that going out foreground area
Than.
Step S103, according to foreground area accounting, mapping processing is carried out to prospect probabilistic information, obtains image segmentation knot
Fruit.
After foreground area accounting has been obtained, according to foreground area accounting, adaptability is carried out to prospect probabilistic information
Mapping is handled, for example, when foreground area accounting is smaller, for example foreground area accounting is 0.2, illustrates foreground image pending
Shared area is smaller in image, then mapping processing can be carried out to prospect probabilistic information, will be less in prospect probabilistic information
Probability is adaptively mapped as larger probability, and probability larger in prospect probabilistic information is adaptively mapped as more
Smooth probability；And for example, when foreground area accounting is larger, for example foreground area accounting is 0.8, illustrates that foreground image is being waited to locate
It is larger to manage area shared in image, then mapping processing can be carried out to prospect probabilistic information, will be general in prospect probabilistic information
Rate is adaptively mapped as more smooth probability.After mapping processing is carried out to prospect probabilistic information, after being handled according to mapping
Prospect probabilistic information obtain image segmentation result, compared with prior art, this processing mode provided by the invention can have
Improve the segmentation precision of image scene segmentation in effect ground so that segmenting edge is more smooth.
According to the image partition method provided in this embodiment based on adaptive threshold, acquisition includes treating for special object
Image is handled, scene cut processing then is carried out to pending image, obtains the prospect probabilistic information for special object, then
According to prospect probabilistic information, foreground area accounting is determined, it is last according to foreground area accounting, prospect probabilistic information is mapped
Processing, obtains image segmentation result.Technical solution provided by the invention carries out prospect probabilistic information according to foreground area accounting
Mapping is handled, and realizes the self organizing maps to prospect probabilistic information, the prospect probabilistic information after being handled using mapping can be fast
Speed, accurately obtain the corresponding image segmentation result of pending image, is effectively improved the segmentation precision of image scene segmentation
And treatment effeciency, optimize image scene segmentation processing mode.
Fig. 2 shows that the flow of the image partition method in accordance with another embodiment of the present invention based on adaptive threshold is shown
It is intended to, as shown in Fig. 2, this method comprises the following steps：
Step S200, obtains the pending image for including special object.
Alternatively, in step s 200, can the special object that includes that catches of real-time image acquisition collecting device wait to locate
Manage image.Specifically, image capture device can be mobile terminal etc., by taking image capture device is mobile terminal as an example, obtain in real time
The pending image that mobile terminal camera captures is taken, wherein, special object is included in pending image, special object can
For human body etc..
Step S201, carries out scene cut processing to pending image, obtains the prospect probabilistic information for special object.
Step S202, according to prospect probabilistic information, determines the pixel for belonging to foreground image.
Wherein, prospect probabilistic information have recorded that each pixel belongs to the general of foreground image in pending image for reflecting
Rate, the value range that each pixel belongs to the probability of foreground image can be [0,1].Specifically, can be by prospect probabilistic information
Probability is determined to belong to the pixel of foreground image higher than the pixel of predetermined probabilities threshold value, and those skilled in the art can be according to reality
Border needs to be configured predetermined probabilities threshold value, does not limit herein.Such as when predetermined probabilities threshold value is 0.7, then can incite somebody to action
Pixel of the prospect probabilistic information higher than 0.7 is determined to belong to the pixel of foreground image.
Step S203, calculates ratio of the pixel for belonging to foreground image in pending image in all pixels point, will
Ratio-dependent is foreground area accounting.
Specifically, the quantity for the pixel for belonging to foreground image and the number of all pixels point in pending image can be calculated
Amount, the ratio for belonging to the quantity and the quantity of all pixels point of the pixel of foreground image is foreground area accounting.
Step S204, according to foreground area accounting, adjusts the parameter of mapping function.
Wherein, mapping processing is carried out to prospect probabilistic information using mapping function, those skilled in the art can be according to reality
Border needs to set mapping function, does not limit herein.For example, mapping function can be piecewise linear transform function or nonlinear transformation
Function.For different foreground area accountings, the parameter of corresponding mapping function is different.
Specifically, when foreground area accounting is smaller, illustrate that foreground image area shared in pending image is smaller,
According to foreground area accounting so in step S204, the parameter of mapping function is adjusted so that utilize reflecting after adjustment
Penetrate function pair prospect probabilistic information carry out mapping processing when, less probability in prospect probabilistic information can adaptively be mapped
For larger probability, probability larger in prospect probabilistic information is adaptively mapped as to more smooth probability；Currently
When scene area accounting is larger, illustrate that foreground image area shared in pending image is larger, then in step S204 according to
According to foreground area accounting, the parameter of mapping function is adjusted so that prospect probability is believed using the mapping function after adjustment
When breath carries out mapping processing, the probability in prospect probabilistic information can be adaptively mapped as to more smooth probability.
Wherein, slope of the mapping function in default interval of definition is more than default slope threshold value.Those skilled in the art can
Default interval of definition and default slope threshold value are set according to being actually needed, do not limited herein, for example, when default interval of definition is
(0,0.5), when default slope threshold value is 1, slope of the mapping function in interval of definition (0,0.5) is more than 1, so as to by before
Less probability is adaptively mapped as larger probability in scape probabilistic information, for example, being mapped as 0.3 by 0.1.
By taking mapping function is non-linear transform function as an example, in a specific embodiment, its specific formula can be such as
Lower formula：
Y=1/ (1+exp (- (k*x-a)))
Wherein, k is the first parameter, and a is the second parameter, specifically, the first parameter for need according to foreground area accounting into
The parameter of row adjustment, the second parameter are default preset parameter, and those skilled in the art can be according to actual needs to specific adjustment side
Formula and default preset parameter are configured, and are not limited herein.Assuming that foreground area accounting is represented with parameter r, then Ke Yishe
K=2/r, a=4 are put, hence for different foreground area accountings, the value of corresponding k also can be different.
Step S205, carries out mapping processing to prospect probabilistic information using the mapping function after adjustment, obtains mapping result.
After it have adjusted mapping function, can using prospect probabilistic information as adjustment after mapping function independent variable, that
Obtained functional value is mapping result.
Step S206, according to mapping result, obtains image segmentation result.
After mapping result has been obtained, so that it may obtain image segmentation result according to mapping result.Compared with prior art,
The present invention has the segmentation precision of higher according to the obtained image segmentation result of mapping result, and segmenting edge is more smooth.
Step S207, according to image segmentation result, determines the foreground image after processing.
It can be determined clearly out which pixel in pending image belongs to foreground image according to image segmentation result, which
Pixel belongs to background image, so that it is determined that going out the foreground image after processing.
Step S208, fusion treatment, the figure after being handled are carried out by the foreground image after processing and default background image
Picture.
After the foreground image after being handled, the foreground image after processing and default background image can be melted
Conjunction is handled, and is obtained and the image after the corresponding processing of pending image.Those skilled in the art can be set according to being actually needed
Default background image, does not limit herein.Default background image can be two-dimensional scene background image, can also the three-dimensional scenic back of the body
Scape image, such as the three-dimensional scenic background image such as three-dimensional seabed scene background image, three-dimensional volcano scene background image.
, can be according to foreground area accounting pair according to the image partition method provided in this embodiment based on adaptive threshold
The parameter of mapping function is adjusted so that when foreground area accounting is different, the parameter of corresponding mapping function is different, realizes
Self organizing maps according to foreground area accounting to prospect probabilistic information；And can quickly, accurately it be obtained using mapping result
To the corresponding image segmentation result of pending image, the segmentation precision and processing for being effectively improved image scene segmentation are imitated
Rate so that segmenting edge is more smooth, helps to improve the display effect of the image after fusion treatment, makes its more natural true.
Fig. 3 shows the structural frames of the image segmenting device according to an embodiment of the invention based on adaptive threshold
Figure, as shown in figure 3, the device includes：Acquisition module 310, segmentation module 320, the first determining module 330 and mapping processing module
340。
Acquisition module 310 is suitable for：Obtain the pending image for including special object.
Segmentation module 320 is suitable for：Scene cut processing is carried out to pending image, the prospect for obtaining being directed to special object is general
Rate information.
Wherein, prospect probabilistic information have recorded that each pixel belongs to the general of foreground image in pending image for reflecting
Rate.
First determining module 330 is suitable for：According to prospect probabilistic information, foreground area accounting is determined.
Wherein, the first determining module 330 is further adapted for：According to prospect probabilistic information, the picture for belonging to foreground image is determined
Vegetarian refreshments；Ratio of the pixel for belonging to foreground image in pending image in all pixels point is calculated, before being by ratio-dependent
Scene area accounting.Specifically, probability in prospect probabilistic information is higher than the pixel of predetermined probabilities threshold value by the first determining module 330
It is determined to belong to the pixel of foreground image.
Mapping processing module 340 is suitable for：According to foreground area accounting, mapping processing is carried out to prospect probabilistic information, is obtained
Image segmentation result.
In a specific embodiment, mapping processing module 340 may include：Adjustment unit 341, map unit 342 and life
Into unit 343.
Adjustment unit 341 is suitable for：Foundation foreground area accounting, adjusts the parameter of mapping function.Wherein, mapping function is pre-
If the slope in interval of definition is more than default slope threshold value.
Map unit 342 is suitable for：Mapping processing is carried out to prospect probabilistic information using the mapping function after adjustment, is reflected
Penetrate result.
Generation unit 343 is suitable for：According to mapping result, image segmentation result is obtained.
The device may also include：Second determining module 350 and fusion treatment module 360.Wherein, the second determining module 350
It is suitable for：According to image segmentation result, the foreground image after processing is determined.Fusion treatment module 360 is suitable for：By the prospect after processing
Image carries out fusion treatment, the image after being handled with default background image.
According to the image segmenting device provided in this embodiment based on adaptive threshold, acquisition module acquisition includes specific
The pending image of object, segmentation module carry out scene cut processing to pending image, obtain the prospect for special object
Probabilistic information, the first determining module determine foreground area accounting, mapping processing module is according to foreground zone according to prospect probabilistic information
Domain accounting, carries out mapping processing to prospect probabilistic information, obtains image segmentation result.Technical solution provided by the invention is according to before
Scene area accounting carries out mapping processing to prospect probabilistic information, realizes the self organizing maps to prospect probabilistic information, using reflecting
The prospect probabilistic information penetrated after processing can quickly, accurately obtain the corresponding image segmentation result of pending image, effectively
The segmentation precision and treatment effeciency of image scene segmentation are improved, optimizes image scene segmentation processing mode.
Present invention also offers a kind of nonvolatile computer storage media, computer-readable storage medium is stored with least one can
Execute instruction, executable instruction can perform the image partition method based on adaptive threshold in above-mentioned any means embodiment.
Fig. 4 shows a kind of structure diagram of computing device according to embodiments of the present invention, the specific embodiment of the invention
The specific implementation to computing device does not limit.
As shown in figure 4, the computing device can include：Processor (processor) 402, communication interface
(Communications Interface) 404, memory (memory) 406 and communication bus 408.
Wherein：
Processor 402, communication interface 404 and memory 406 complete mutual communication by communication bus 408.
Communication interface 404, for communicating with the network element of miscellaneous equipment such as client or other servers etc..
Processor 402, for executive program 410, can specifically perform the above-mentioned image segmentation side based on adaptive threshold
Correlation step in method embodiment.
Specifically, program 410 can include program code, which includes computer-managed instruction.
Processor 402 is probably central processor CPU, or specific integrated circuit ASIC (Application
Specific Integrated Circuit), or be arranged to implement the embodiment of the present invention one or more integrate electricity
Road.The one or more processors that computing device includes, can be same type of processors, such as one or more CPU；Also may be used
To be different types of processor, such as one or more CPU and one or more ASIC.
Memory 406, for storing program 410.Memory 406 may include high-speed RAM memory, it is also possible to further include
Nonvolatile memory (non-volatile memory), for example, at least a magnetic disk storage.
Program 410 specifically can be used for so that processor 402 perform in above-mentioned any means embodiment based on adaptive
The image partition method of threshold value.The specific implementation of each step may refer to the above-mentioned image based on adaptive threshold in program 410
Split corresponding description in corresponding steps and the unit in embodiment, this will not be repeated here.Those skilled in the art can be clear
Recognize to Chu, for convenience and simplicity of description, the equipment of foregoing description and the specific work process of module, may be referred to foregoing
Corresponding process description in embodiment of the method, details are not described herein.
Algorithm and display be not inherently related to any certain computer, virtual system or miscellaneous equipment provided herein.
Various general-purpose systems can also be used together with teaching based on this.As described above, required by constructing this kind of system
Structure be obvious.In addition, the present invention is not also directed to any certain programmed language.It should be understood that it can utilize various
Programming language realizes the content of invention described herein, and the description done above to language-specific is to disclose this hair
Bright preferred forms.
In the specification that this place provides, numerous specific details are set forth.It is to be appreciated, however, that the implementation of the present invention
Example can be put into practice in the case of these no details.In some instances, known method, structure is not been shown in detail
And technology, so as not to obscure the understanding of this description.
Similarly, it will be appreciated that in order to simplify the disclosure and help to understand one or more of each inventive aspect,
Above in the description to the exemplary embodiment of the present invention, each feature of the invention is grouped together into single implementation sometimes
In example, figure or descriptions thereof.However, the method for the disclosure should be construed to reflect following intention：I.e. required guarantor
The application claims of shield features more more than the feature being expressly recited in each claim.It is more precisely, such as following
Claims reflect as, inventive aspect is all features less than single embodiment disclosed above.Therefore,
Thus the claims for following embodiment are expressly incorporated in the embodiment, wherein each claim is in itself
Separate embodiments all as the present invention.
Those skilled in the art, which are appreciated that, to carry out adaptively the module in the equipment in embodiment
Change and they are arranged in one or more equipment different from the embodiment.Can be the module or list in embodiment
Member or component be combined into a module or unit or component, and can be divided into addition multiple submodule or subelement or
Sub-component.In addition at least some in such feature and/or process or unit exclude each other, it can use any
Combination is disclosed to all features disclosed in this specification (including adjoint claim, summary and attached drawing) and so to appoint
Where all processes or unit of method or equipment are combined.Unless expressly stated otherwise, this specification (including adjoint power
Profit requires, summary and attached drawing) disclosed in each feature can be by providing the alternative features of identical, equivalent or similar purpose come generation
Replace.
In addition, it will be appreciated by those of skill in the art that although some embodiments described herein include other embodiments
In included some features rather than further feature, but the combination of the feature of different embodiments means in of the invention
Within the scope of and form different embodiments.For example, in the following claims, embodiment claimed is appointed
One of meaning mode can use in any combination.
The all parts embodiment of the present invention can be with hardware realization, or to be run on one or more processor
Software module realize, or realized with combinations thereof.It will be understood by those of skill in the art that it can use in practice
Microprocessor or digital signal processor (DSP) are come one of some or all components in realizing according to embodiments of the present invention
A little or repertoire.The present invention is also implemented as setting for performing some or all of method as described herein
Standby or program of device (for example, computer program and computer program product).Such program for realizing the present invention can deposit
Storage on a computer-readable medium, or can have the form of one or more signal.Such signal can be from because of spy
Download and obtain on net website, either provide on carrier signal or provided in the form of any other.
It should be noted that the present invention will be described rather than limits the invention for above-described embodiment, and ability
Field technique personnel can design alternative embodiment without departing from the scope of the appended claims.In the claims,
Any reference symbol between bracket should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not
Element or step listed in the claims.Word "a" or "an" before element does not exclude the presence of multiple such
Element.The present invention can be by means of including the hardware of some different elements and being come by means of properly programmed computer real
It is existing.In if the unit claim of equipment for drying is listed, several in these devices can be by same hardware branch
To embody.The use of word first, second, and third does not indicate that any order.These words can be explained and run after fame
Claim.
Claims (10)
1. a kind of image partition method based on adaptive threshold, the described method includes：
Obtain the pending image for including special object；
Scene cut processing is carried out to the pending image, obtains the prospect probabilistic information for special object；
According to the prospect probabilistic information, foreground area accounting is determined；
According to the foreground area accounting, mapping processing is carried out to the prospect probabilistic information, obtains image segmentation result.
2. according to the method described in claim 1, wherein, the prospect probabilistic information have recorded for reflecting the pending figure
Each pixel belongs to the probability of foreground image as in.
3. method according to claim 1 or 2, wherein, it is described according to the prospect probabilistic information, determine that foreground area accounts for
Than further comprising：
According to the prospect probabilistic information, the pixel for belonging to foreground image is determined；
Ratio of the pixel for belonging to foreground image in the pending image in all pixels point is calculated, the ratio is true
It is set to foreground area accounting.
4. according to claim 1-3 any one of them methods, wherein, it is described according to the prospect probabilistic information, determine to belong to
The pixel of foreground image further comprises：
Probability in the prospect probabilistic information is determined to belong to the pixel of foreground image higher than the pixel of predetermined probabilities threshold value
Point.
5. according to claim 1-4 any one of them methods, wherein, it is described according to the foreground area accounting, before described
Scape probabilistic information carries out mapping processing, obtains image segmentation result and further comprises：
According to the foreground area accounting, the parameter of mapping function is adjusted；
Mapping processing is carried out to the prospect probabilistic information using the mapping function after adjustment, obtains mapping result；
According to the mapping result, image segmentation result is obtained.
6. according to claim 1-5 any one of them methods, wherein, slope of the mapping function in default interval of definition
More than default slope threshold value.
7. according to claim 1-6 any one of them methods, wherein, it is described obtain image segmentation result after, the side
Method further includes：
According to described image segmentation result, the foreground image after processing is determined；
Foreground image after the processing and default background image are subjected to fusion treatment, the image after being handled.
8. a kind of image segmenting device based on adaptive threshold, described device include：
Acquisition module, suitable for obtaining the pending image for including special object；
Split module, suitable for carrying out scene cut processing to the pending image, obtain the prospect probability for special object
Information；
First determining module, suitable for according to the prospect probabilistic information, determining foreground area accounting；
Processing module is mapped, suitable for according to the foreground area accounting, carrying out mapping processing to the prospect probabilistic information, obtaining
Image segmentation result.
9. a kind of computing device, including：Processor, memory, communication interface and communication bus, the processor, the storage
Device and the communication interface complete mutual communication by the communication bus；
The memory is used to store an at least executable instruction, and the executable instruction makes the processor perform right such as will
Ask the corresponding operation of the image partition method based on adaptive threshold any one of 1-7.
10. a kind of computer-readable storage medium, an at least executable instruction, the executable instruction are stored with the storage medium
Processor is set to perform the corresponding behaviour of the image partition method based on adaptive threshold as any one of claim 1-7
Make.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711377314.3A CN107945202B (en) | 2017-12-19 | 2017-12-19 | Image segmentation method and device based on adaptive threshold value and computing equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711377314.3A CN107945202B (en) | 2017-12-19 | 2017-12-19 | Image segmentation method and device based on adaptive threshold value and computing equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107945202A true CN107945202A (en) | 2018-04-20 |
| CN107945202B CN107945202B (en) | 2021-07-23 |
Family
ID=61941311
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711377314.3A Active CN107945202B (en) | 2017-12-19 | 2017-12-19 | Image segmentation method and device based on adaptive threshold value and computing equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107945202B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110930296A (en) * | 2019-11-20 | 2020-03-27 | Oppo广东移动通信有限公司 | Image processing method, device, equipment and storage medium |
| CN111598902A (en) * | 2020-05-20 | 2020-08-28 | 北京字节跳动网络技术有限公司 | Image segmentation method and device, electronic equipment and computer readable medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101282461A (en) * | 2007-04-02 | 2008-10-08 | 财团法人工业技术研究院 | Image processing methods |
| US20130278788A1 (en) * | 2012-04-23 | 2013-10-24 | Csr Technology Inc. | Method for determining the extent of a foreground object in an image |
| CN106558050A (en) * | 2015-09-24 | 2017-04-05 | 南京大学 | A kind of obvious object dividing method based on three threshold value of self adaptation |
| CN106713701A (en) * | 2016-12-20 | 2017-05-24 | 华中科技大学 | Cluster motion data acquisition method and system based on image processing technology |
| CN107292902A (en) * | 2017-07-07 | 2017-10-24 | 国家电网公司 | A kind of two-dimensional Otsu image segmentation method of combination drosophila optimized algorithm |
| CN107452013A (en) * | 2017-05-27 | 2017-12-08 | 深圳市美好幸福生活安全系统有限公司 | Conspicuousness detection method based on Harris Corner Detections and Sugeno fuzzy integrals |
-
2017
- 2017-12-19 CN CN201711377314.3A patent/CN107945202B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101282461A (en) * | 2007-04-02 | 2008-10-08 | 财团法人工业技术研究院 | Image processing methods |
| US20130278788A1 (en) * | 2012-04-23 | 2013-10-24 | Csr Technology Inc. | Method for determining the extent of a foreground object in an image |
| CN106558050A (en) * | 2015-09-24 | 2017-04-05 | 南京大学 | A kind of obvious object dividing method based on three threshold value of self adaptation |
| CN106713701A (en) * | 2016-12-20 | 2017-05-24 | 华中科技大学 | Cluster motion data acquisition method and system based on image processing technology |
| CN107452013A (en) * | 2017-05-27 | 2017-12-08 | 深圳市美好幸福生活安全系统有限公司 | Conspicuousness detection method based on Harris Corner Detections and Sugeno fuzzy integrals |
| CN107292902A (en) * | 2017-07-07 | 2017-10-24 | 国家电网公司 | A kind of two-dimensional Otsu image segmentation method of combination drosophila optimized algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 程婷婷: "基于高阶CRF的视频多目标自动分割技术研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110930296A (en) * | 2019-11-20 | 2020-03-27 | Oppo广东移动通信有限公司 | Image processing method, device, equipment and storage medium |
| CN110930296B (en) * | 2019-11-20 | 2023-08-08 | Oppo广东移动通信有限公司 | Image processing method, device, equipment and storage medium |
| CN111598902A (en) * | 2020-05-20 | 2020-08-28 | 北京字节跳动网络技术有限公司 | Image segmentation method and device, electronic equipment and computer readable medium |
| CN111598902B (en) * | 2020-05-20 | 2023-05-30 | 抖音视界有限公司 | Image segmentation method, device, electronic equipment and computer readable medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107945202B (en) | 2021-07-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105868829B (en) | Recurrent neural networks for data item generation | |
| CN109671020B (en) | Image processing method, device, electronic equipment and computer storage medium | |
| US11417130B2 (en) | System and method for facilitating graphic-recognition training of a recognition model | |
| US20200042833A1 (en) | Convolutional neural network with augmentation features | |
| CN108109161A (en) | Video data real-time processing method and device based on adaptive threshold fuzziness | |
| CN107958285A (en) | The mapping method and device of the neutral net of embedded system | |
| EP3241155B1 (en) | Exposure computation via depth-based computational photography | |
| CN108734052A (en) | character detecting method, device and system | |
| CN110443266A (en) | Object prediction method and device, electronic equipment and storage medium | |
| CN110023989A (en) | A kind of generation method and device of sketch image | |
| US20220058451A1 (en) | Identifying a type of object in a digital image based on overlapping areas of sub-images | |
| CN107590811A (en) | Landscape image processing method, device and computing device based on scene cut | |
| CN107610149A (en) | Image segmentation result edge optimization processing method, device and computing device | |
| CN107959798A (en) | Video data real-time processing method and device, computing device | |
| CN110992395A (en) | Image training sample generation method and device and motion tracking method and device | |
| CN107563357A (en) | Live dress ornament based on scene cut, which is dressed up, recommends method, apparatus and computing device | |
| CN107945202A (en) | Image partition method, device and computing device based on adaptive threshold | |
| CN107644423A (en) | Video data real-time processing method, device and computing device based on scene cut | |
| CN110827341A (en) | Picture depth estimation method and device and storage medium | |
| CN109445569A (en) | Information processing method, device, equipment and readable storage medium storing program for executing based on AR | |
| Joutard et al. | Permutohedral attention module for efficient non-local neural networks | |
| CN108062761A (en) | Image partition method, device and computing device based on adaptive tracing frame | |
| CN107767391A (en) | Landscape image processing method, device, computing device and computer-readable storage medium | |
| CN107578369A (en) | Video data handling procedure and device, computing device | |
| CN108010038A (en) | Live dress ornament based on adaptive threshold fuzziness is dressed up method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |