CN105139415A - Foreground and background segmentation method and apparatus of image, and terminal - Google Patents
Foreground and background segmentation method and apparatus of image, and terminal Download PDFInfo
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Abstract
The disclosure relates to a foreground and background segmentation method and apparatus of an image, and a terminal. The method comprises: a foreground region, a possible foreground region, and a background region of a to-be-segmented image are obtained; a neighbor pixel of each pixel is determined according to color information and spatial position information of each pixel in the to-be-segmented image; on the basis of the foreground region, the possible foreground region, and the background region as well as the neighbor pixel of each pixel, a probability value of each pixel as a target foreground region is determined; and foreground and background segmentation is carried out on the to-be-segmented image based on the probability value of each pixel as the target foreground region in the to-be-segmented image. According to the embodiment of the invention, the neighbor pixel of each pixel can be detected and similarity of all pixels in a full image range can be considered, so that the refined foreground and background segmentation can be realized precisely and the segmentation precision is high.
Description
Technical field
The application relates to technical field of image processing, particularly relates to scape dividing method, device and terminal before and after image.
Background technology
In image processing field, the demand of the front and back scape segmentation of image is comparatively large, such as often needs the personage in image split and be blended in other backgrounds.Front and back scape partitioning algorithm in correlation technique, scene area before and after the part of normally specifying according to user, builds statistical model respectively by prospect and background, represents the regularity on respective pixels statistics; Owing to being limited to the precision of statistical model, if model component is many, easily make front and back scape model confusion; If model component is few, easily miss the feature that some is important, so segmentation fineness is not ideal enough.
Summary of the invention
For overcoming Problems existing in correlation technique, present disclose provides scape dividing method, device and terminal before and after image.
According to the first aspect of disclosure embodiment, provide scape dividing method before and after a kind of image, described method comprises:
Obtain the foreground area of image to be split, possibility foreground area and background area;
The neighbor pixel of each pixel is determined according to the colouring information of each pixel in described image to be split and spatial positional information;
According to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region;
According to each pixel in described image to be split as the probable value in target prospect region, front and back scape segmentation is carried out to described image to be split.
Optionally, the foreground area of described acquisition image to be split, possible foreground area and background area, comprising:
Utilize image recognition algorithm to mark foreground area in described image to be split and may foreground area, mark foreground area in described image to be split and may the region outside foreground area be background area;
Or,
Obtain the foreground area to described image to be split of input, may the mark instructions of foreground area and background area, according to described mark instructions determine described image to be split foreground area, may foreground area and background area.
Optionally, the described colouring information according to each pixel in described image to be split and spatial positional information determine the neighbor pixel of each pixel, comprising:
The colouring information of pixel each in image to be split and spatial positional information are inputed in default non local nearest neighbor algorithm model, obtain the neighbor pixel of described each pixel according to the Output rusults of described non local nearest neighbor algorithm model.
Optionally, described according to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region, comprising:
According to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, solve the transparency of each pixel as described probable value.
Optionally, described according to each pixel in described image to be split as the probable value in target prospect region, scapes segmentation in front and back is carried out to described image to be split, comprising:
Self-adaption binaryzation is carried out to the transparency of each pixel in described image to be split;
According to the transparency after the self-adaption binaryzation of each pixel, detect the profile in described image to be split;
Detect the area in the region that each bar profile surrounds, region maximum for area is defined as described target prospect region;
Described target prospect region is partitioned into from described image to be split.
According to the second aspect of disclosure embodiment, provide scape segmenting device before and after a kind of image, described device comprises:
Area acquisition unit, is configured to obtain the foreground area of image to be split, possible foreground area and background area;
Neighbor pixel determining unit, is configured to the neighbor pixel determining each pixel according to the colouring information of each pixel in described image to be split and spatial positional information;
Probable value determining unit, be configured to according to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region;
Cutting unit, is configured to according to each pixel in described image to be split as the probable value in target prospect region, carries out front and back scape segmentation to described image to be split.
Optionally, described area acquisition unit, comprising:
Zone marker subelement, is configured to utilize image recognition algorithm to mark foreground area in described image to be split and may foreground area, marks foreground area in described image to be split and may the region outside foreground area be background area;
Or,
Subelement is determined in region, be configured to obtain the foreground area to described image to be split of input, may the mark instructions of foreground area and background area, according to described mark instructions determine described image to be split foreground area, may foreground area and background area.
Optionally, described neighbor pixel determining unit, comprising:
Neighbor pixel exports subelement, be configured to the colouring information of pixel each in image to be split and spatial positional information be inputed in default non local nearest neighbor algorithm model, obtain the neighbor pixel of described each pixel according to the Output rusults of described non local nearest neighbor algorithm model.
Optionally, described probable value determining unit, comprising:
Transparency solves subelement, be configured to according to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, solve the transparency of each pixel as described probable value.
Optionally, described cutting unit, comprising:
Self-adaption binaryzation subelement, is configured to carry out self-adaption binaryzation to the transparency of each pixel in described image to be split;
Contour detecting subelement, is configured to the transparency after according to the self-adaption binaryzation of each pixel, detects the profile in described image to be split;
Subelement is determined in target prospect region, is configured to the area detecting the region that each bar profile surrounds, region maximum for area is defined as described target prospect region;
Segmentation subelement, is configured to be partitioned into described target prospect region from described image to be split.
According to the third aspect of disclosure embodiment, a kind of terminal is provided, comprises:
Processor;
For the storer of storage of processor executable instruction;
Wherein, described processor is configured to:
Obtain the foreground area of image to be split, possibility foreground area and background area;
The neighbor pixel of each pixel is determined according to the colouring information of each pixel in described image to be split and spatial positional information;
According to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region;
According to each pixel in described image to be split as the probable value in target prospect region, front and back scape segmentation is carried out to described image to be split.
The technical scheme that embodiment of the present disclosure provides can comprise following beneficial effect:
In the disclosure, the neighbor pixel of each pixel is determined according to the colouring information of each pixel in described image to be split and spatial positional information, again in conjunction with foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region, finally according to the probable value of each pixel, front and back scape segmentation is carried out to described image to be split.Disclosure embodiment can detect the neighbor pixel of each pixel, considers the similarity of each pixel within the scope of full figure, and can realize more meticulous front and back scape segmentation, segmentation accurate rate is higher.
In the disclosure, image recognition algorithm identification can be utilized and mark foreground area, possibility foreground area and background area, also can be the mark instructions obtaining user's input, aforesaid way can determine the foreground area of image to be split, possible foreground area and background area exactly, improves Iamge Segmentation precision.
In the disclosure, utilize non local nearest neighbor algorithm can detect the neighbor pixel of each pixel, consider the similarity of each pixel within the scope of full figure, can realize more meticulous front and back scape segmentation, segmentation accurate rate is higher.
In the disclosure, by solving the transparency of pixel, the probable value of pixel as target prospect region can be determined exactly.
In the disclosure, can according to the transparency after self-adaption binaryzation, the area in the region that the profile in detected image and each bar profile surround, is defined as target prospect region by region maximum for area, therefore can accurately determine target prospect region, the segmentation precision of image is higher.
Should be understood that, it is only exemplary and explanatory that above general description and details hereinafter describe, and can not limit the disclosure.
Accompanying drawing explanation
Accompanying drawing to be herein merged in instructions and to form the part of this instructions, shows and meets embodiment of the present disclosure, and is used from instructions one and explains principle of the present disclosure.
Fig. 1 is the process flow diagram of scape dividing method before and after a kind of image of the disclosure according to an exemplary embodiment.
Fig. 2 is the application scenarios schematic diagram of scape dividing method before and after a kind of image of the disclosure according to an exemplary embodiment.
Fig. 3 is scape segmenting device block diagram before and after a kind of image of the disclosure according to an exemplary embodiment.
Fig. 4 is scape segmenting device block diagram before and after the another kind of image of the disclosure according to an exemplary embodiment.
Fig. 5 is scape segmenting device block diagram before and after the another kind of image of the disclosure according to an exemplary embodiment.
Fig. 6 is scape segmenting device block diagram before and after the another kind of image of the disclosure according to an exemplary embodiment.
Fig. 7 is scape segmenting device block diagram before and after the another kind of image of the disclosure according to an exemplary embodiment.
Fig. 8 is that the one of the disclosure according to an exemplary embodiment is for scape segmenting device block diagram before and after image.
Embodiment
Here will be described exemplary embodiment in detail, its sample table shows in the accompanying drawings.When description below relates to accompanying drawing, unless otherwise indicated, the same numbers in different accompanying drawing represents same or analogous key element.Embodiment described in following exemplary embodiment does not represent all embodiments consistent with the disclosure.On the contrary, they only with as in appended claims describe in detail, the example of apparatus and method that aspects more of the present disclosure are consistent.
The term used in the disclosure is only for the object describing specific embodiment, and the not intended to be limiting disclosure." one ", " described " and " being somebody's turn to do " of the singulative used in disclosure and the accompanying claims book is also intended to comprise most form, unless context clearly represents other implications.It is also understood that term "and/or" used herein refer to and comprise one or more project of listing be associated any or all may combine.
Term first, second, third, etc. may be adopted although should be appreciated that to describe various information in the disclosure, these information should not be limited to these terms.These terms are only used for the information of same type to be distinguished from each other out.Such as, when not departing from disclosure scope, the first information also can be called as the second information, and similarly, the second information also can be called as the first information.Depend on linguistic context, word as used in this " if " can be construed as into " ... time " or " when ... time " or " in response to determining ".
As shown in Figure 1, Fig. 1 is the process flow diagram of scape dividing method before and after a kind of image according to an exemplary embodiment, and the method can be applied in terminal, comprises the following steps:
In a step 101, the foreground area of image to be split, possibility foreground area and background area is obtained.
In a step 102, the neighbor pixel of each pixel is determined according to the colouring information of each pixel in described image to be split and spatial positional information.
In step 103, according to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region.
At step 104, according to each pixel in described image to be split as the probable value in target prospect region, front and back scape segmentation is carried out to described image to be split.
The terminal related in disclosure embodiment can be intelligent terminal, such as, and smart mobile phone, panel computer, PDA (PersonalDigitalAssistant, personal digital assistant), personal computer etc.
In the embodiment that the disclosure provides, the neighbor pixel of each pixel is determined according to the colouring information of each pixel in described image to be split and spatial positional information, again in conjunction with foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region, finally according to the probable value of each pixel, front and back scape segmentation is carried out to described image to be split.Disclosure embodiment can detect the neighbor pixel of each pixel, considers the similarity of each pixel within the scope of full figure, and can realize more meticulous front and back scape segmentation, segmentation accurate rate is higher.
Wherein, for step 101, foreground area refers to the target to be split in image, and possible foreground area refers to likely becomes order target area to be split.
In actual applications, obtain the foreground area of image to be split, possibility foreground area and background area, can have various ways, such as, can be utilize image recognition algorithm automatically to identify, also can be marked by user.
First kind of way: utilize image recognition algorithm to mark foreground area in described image to be split and may foreground area, marks foreground area in described image to be split and may the region outside foreground area be background area.
In the present embodiment, the normally object or person thing of the target to be split in image; If target to be split is object, image object recognizer can be utilized to identify, determine the foreground area of image to be split, possibility foreground area and background area.Wherein, image object recognizer can be the object recognition algorithm based on display model, or also can be the object recognition algorithm etc. of structure based.
If target to be split is personage, person recognition algorithm can be utilized to identify, determine the foreground area of image to be split, possibility foreground area and background area.Person recognition algorithm can comprise Face datection algorithm, trunk detection algorithm etc., utilize person recognition algorithm from image to be split, detect human face region, hair zones, torso area etc. and be defined as foreground area, determining further may foreground area and background area again.Wherein, Face datection algorithm can be specially the AdaBoost detection of classifier algorithm based on Haar feature, or based on the second order Gauss Skin Color Mixture Model and face feature recognizer etc. of H-SV and C'bC'r; It should be noted that, the concrete processing procedure of above-mentioned algorithm see the processing procedure in correlation technique, no longer can repeat this disclosure embodiment.
The second way: obtain the foreground area to described image to be split of input, may the mark instructions of foreground area and background area, according to described mark instructions determine described image to be split foreground area, may foreground area and background area.
In disclosure embodiment, the foreground area of image to be split, possible foreground area and background area can be marked by user, terminal can obtain the mark instructions of user's input, and then determines the foreground area of image to be split, possible foreground area and background area according to mark instructions.
It should be noted that, above-mentioned two kinds of modes can select an execution, also can perform simultaneously.As seen from the above-described embodiment, image recognition algorithm identification can be utilized and mark foreground area, possibility foreground area and background area, also can be the mark instructions obtaining user's input, aforesaid way can determine the foreground area of image to be split, possible foreground area and background area exactly, improves Iamge Segmentation precision.
For step 102, colouring information can be each component of RGB (Red, Green, Blue RGB) color space of pixel, or HSV (Hue tone, Saturation saturation degree, Value brightness) each component of color space, spatial positional information can be the volume coordinate of pixel.
In the embodiment that the disclosure provides, utilize colouring information and the spatial positional information of pixel, the correlativity of each pixel and other pixels of periphery can be analyzed, and then accurately determine the neighbor pixel of each pixel.
In an optional implementation, the described colouring information according to each pixel in described image to be split and spatial positional information determine the neighbor pixel of each pixel, can comprise:
The colouring information of pixel each in image to be split and spatial positional information are inputed in default non local nearest neighbor algorithm model, obtain the neighbor pixel of described each pixel according to the Output rusults of described non local nearest neighbor algorithm model.
In the present embodiment, non local nearest neighbor algorithm and KNN (k-NearestNeighbor) matting algorithm, the thought of this algorithm is: if the great majority in the sample of K (namely the most contiguous in feature space) the most similar of a sample in feature space belong to some classifications, then this sample also belongs to this classification.The present embodiment adopts this algorithm, after the colouring information of each pixel and spatial positional information are inputted this algorithm, can adopt k nearest neighbor cluster to each pixel, thus finds the K of each pixel the most close individual neighbor pixel.
As seen from the above-described embodiment, utilize non local nearest neighbor algorithm can detect the neighbor pixel of each pixel, consider the similarity of each pixel within the scope of full figure, can realize more meticulous front and back scape segmentation, segmentation accurate rate is higher.
For in step 103, owing to marked the foreground area of image to be split, possible foreground area and background area in a step 101, when detecting the most close neighbor pixel of the K of each pixel, in conjunction with the foreground area marked, possibility foreground area and background area, the probable value of each pixel as target prospect region can be obtained, to determine foreground area accurately.
In an optional implementation, described according to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region, comprising:
According to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, solve the transparency of each pixel as described probable value.
In the present embodiment, image I can be expressed as the linear combination of foreground image F and background image B:
I＝αF+(1-α)B
Wherein, α is the transparency of pixel, α ∈ [0,1].
Complete Iamge Segmentation, need prospect F, background B and the transparency α of determining image; .When α=1, represent that this pixel is prospect; When α=0, represent that this pixel is background; As 0 < α < 1, represent that this pixel is the superposition of prospect and background.
Complete Iamge Segmentation work, namely need the α value determining each pixel.The embodiment that the disclosure provides, by solving the transparency of pixel, can determine the probable value of pixel as target prospect region exactly.
For step 104, target prospect region, i.e. order target area to be split in image; When determining the probable value of each pixel as target prospect region, then can perform scape segmentation further.
In an optional implementation, according to each pixel in described image to be split as the probable value in target prospect region, front and back scape segmentation is carried out to described image to be split, comprising:
Self-adaption binaryzation is carried out to the transparency of each pixel in described image to be split.
According to the transparency after the self-adaption binaryzation of each pixel, detect the profile in described image to be split.
Detect the area in the region that each bar profile surrounds, region maximum for area is defined as described target prospect region.
Described target prospect region is partitioned into from described image to be split.
In disclosure embodiment, the transparency of each pixel can be carried out self-adaption binaryzation, the transparence value obtained only has 0 or 1, and 0 represents the background determined, 1 represents the prospect determined.
In order to level and smooth target prospect region can be obtained, can according to the transparency after the self-adaption binaryzation of each pixel, detect the profile in described image to be split, detect the area in the region that each bar profile surrounds afterwards, region maximum for area is defined as described target prospect region, from image to be split, is partitioned into described target prospect region.Wherein, when detecting the profile in described image to be split, existing contour detecting algorithm can be adopted.
Before the transparency of each pixel carries out self-adaption binaryzation in image to be split, can also carry out morphologic filtering remove isolated noise to image to be split, it is level and smooth to realize then to carry out medium filtering, thus improves the segmentation precision of image.
As seen from the above-described embodiment, can according to the transparency after self-adaption binaryzation, the area in the region that the profile in detected image and each bar profile surround, is defined as target prospect region by region maximum for area, therefore can accurately determine target prospect region, the segmentation precision of image is higher.
As shown in Figure 2, Fig. 2 is the schematic diagram that before and after image shown in a kind of Fig. 1 of utilization according to an exemplary embodiment, scape dividing method carries out scape segmentation before and after image, image to be split has been shown in Fig. 2, this image to be split comprises people's object area, this people's object area is defined as target prospect region, i.e. the target to be split of this image.
First, utilize image recognition algorithm to carry out Face datection, Face Detection, hair estimation, trunk estimation, obtain foreground area, gray-scale value is labeled as 255; Mark the region that personage may exist afterwards, remove the pixel being labeled as 255 in this region and be all labeled as 128 outward, express possibility foreground area; Remaining region is then background area, is labeled as 0; Three value figure Trimap can be obtained after mark.
Then, can by three value figure Trimap figure normalization, can be normalized to 1 by 255,128 are normalized to 0.5.Knnmatting algorithm (the matting algorithm based on non local arest neighbors) is adopted to solve the neighbor pixel of each pixel afterwards, and alpha (transparency) the value figure of whole pixel, alpha value scope is [0,1], between, show that each pixel belongs to the probability size in target prospect region.
Finally, morphologic filtering can be carried out to alpha figure and remove isolated noise; Carry out medium filtering again with smoothly; Again by picture self-adaption binaryzation, in the picture obtained, only have 0,1 two-value; Finally find all profiles in figure, find out the target prospect region of the maximum region of area as people place, all the other are background area, thus complete front and back scape segmentation.
Corresponding with the embodiment of scape dividing method before and after earlier figures picture, the embodiment of terminal that the disclosure additionally provides scape segmenting device before and after image and applies.
As shown in Figure 3, Fig. 3 is scape segmenting device block diagram before and after a kind of image of the disclosure according to an exemplary embodiment, and described device comprises: area acquisition unit 31, neighbor pixel determining unit 32, probable value determining unit 33 and cutting unit 34.
Wherein, area acquisition unit 31, is configured to obtain the foreground area of image to be split, possible foreground area and background area.
Neighbor pixel determining unit 32, is configured to the neighbor pixel determining each pixel according to the colouring information of each pixel in described image to be split and spatial positional information.
Probable value determining unit 33, be configured to according to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region.
Cutting unit 34, is configured to according to each pixel in described image to be split as the probable value in target prospect region, carries out front and back scape segmentation to described image to be split.
As seen from the above-described embodiment, the neighbor pixel of each pixel is determined according to the colouring information of each pixel in described image to be split and spatial positional information, again in conjunction with foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region, finally according to the probable value of each pixel, front and back scape segmentation is carried out to described image to be split.Disclosure embodiment can detect the neighbor pixel of each pixel, considers the similarity of each pixel within the scope of full figure, and can realize more meticulous front and back scape segmentation, segmentation accurate rate is higher.
As shown in Figure 4, Fig. 4 is scape segmenting device block diagram before and after the another kind of image of the disclosure according to an exemplary embodiment, this embodiment is on aforementioned basis embodiment illustrated in fig. 3, described area acquisition unit 31, zone marker subelement 311 can be comprised or subelement 312 is determined in region, also can comprise above-mentioned two subelements simultaneously.In order to example is convenient, in Fig. 4, also show above-mentioned two subelements.
Wherein, zone marker subelement 311, be configured to utilize image recognition algorithm to mark foreground area in described image to be split and may foreground area, mark foreground area in described image to be split and may the region outside foreground area be background area.
Subelement 312 is determined in region, be configured to obtain the foreground area to described image to be split of input, may the mark instructions of foreground area and background area, according to described mark instructions determine described image to be split foreground area, may foreground area and background area.
As seen from the above-described embodiment, image recognition algorithm identification can be utilized and mark foreground area, possibility foreground area and background area, also can be the mark instructions obtaining user's input, aforesaid way can determine the foreground area of image to be split, possible foreground area and background area exactly, improves Iamge Segmentation precision.
As shown in Figure 5, Fig. 5 is scape segmenting device block diagram before and after the another kind of image of the disclosure according to an exemplary embodiment, this embodiment is on aforementioned basis embodiment illustrated in fig. 3, and described neighbor pixel determining unit 32, comprising: neighbor pixel exports subelement 321.
Wherein, neighbor pixel exports subelement 321, be configured to the colouring information of pixel each in image to be split and spatial positional information be inputed in default non local nearest neighbor algorithm model, obtain the neighbor pixel of described each pixel according to the Output rusults of described non local nearest neighbor algorithm model.
As seen from the above-described embodiment, utilize non local nearest neighbor algorithm can detect the neighbor pixel of each pixel, consider the similarity of each pixel within the scope of full figure, can realize more meticulous front and back scape segmentation, segmentation accurate rate is higher.
As shown in Figure 6, Fig. 6 is scape segmenting device block diagram before and after the another kind of image of the disclosure according to an exemplary embodiment, and this embodiment is on aforementioned basis embodiment illustrated in fig. 3, and described probable value determining unit 33, comprising: transparency solves subelement 331.
Wherein, transparency solves subelement 331, be configured to according to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, solve the transparency of each pixel as described probable value.
As seen from the above-described embodiment, by solving the transparency of pixel, the probable value of pixel as target prospect region can be determined exactly.
As shown in Figure 7, Fig. 7 is scape segmenting device block diagram before and after the another kind of image of the disclosure according to an exemplary embodiment, this embodiment is on aforementioned basis embodiment illustrated in fig. 3, described cutting unit 34, comprising: self-adaption binaryzation subelement 341, contour detecting subelement 342, target prospect region determine subelement 343 and segmentation subelement 344.
Wherein, self-adaption binaryzation subelement 341, is configured to carry out self-adaption binaryzation to the transparency of each pixel in described image to be split.
Contour detecting subelement 342, is configured to the transparency after according to the self-adaption binaryzation of each pixel, detects the profile in described image to be split.
Subelement 343 is determined in target prospect region, is configured to the area detecting the region that each bar profile surrounds, region maximum for area is defined as described target prospect region.
Segmentation subelement 344, is configured to be partitioned into described target prospect region from described image to be split.
As seen from the above-described embodiment, can according to the transparency after self-adaption binaryzation, the area in the region that the profile in detected image and each bar profile surround, is defined as target prospect region by region maximum for area, therefore can accurately determine target prospect region, the segmentation precision of image is higher.
Accordingly, the disclosure also provides scape segmenting device before and after a kind of image, comprising:
Processor;
For the storer of storage of processor executable instruction;
Wherein, described processor is configured to:
Obtain the foreground area of image to be split, possibility foreground area and background area;
The neighbor pixel of each pixel is determined according to the colouring information of each pixel in described image to be split and spatial positional information;
According to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region;
According to each pixel in described image to be split as the probable value in target prospect region, front and back scape segmentation is carried out to described image to be split.
In said apparatus, the implementation procedure of the function and efficacy of modules specifically refers to the implementation procedure of corresponding step in said method, does not repeat them here.
For device embodiment, because it corresponds essentially to embodiment of the method, so relevant part illustrates see the part of embodiment of the method.Device embodiment described above is only schematic, the wherein said module illustrated as separating component can or may not be physically separates, parts as module display can be or may not be physical location, namely can be positioned at a place, or also can be distributed in multiple network element.Some or all of module wherein can be selected according to the actual needs to realize the object of disclosure scheme.Those of ordinary skill in the art, when not paying creative work, are namely appreciated that and implement.
As shown in Figure 8, Fig. 8 is a kind of structural representation for image before and after scape segmenting device 800 of the disclosure according to an exemplary embodiment.Such as, device 800 can be the mobile phone with routing function, computing machine, digital broadcast terminal, messaging devices, game console, tablet device, Medical Devices, body-building equipment, personal digital assistant etc.
With reference to Fig. 8, device 800 can comprise following one or more assembly: processing components 802, storer 804, power supply module 806, multimedia groupware 808, audio-frequency assembly 810, the interface 812 of I/O (I/O), sensor module 814, and communications component 816.
The integrated operation of the usual control device 800 of processing components 802, such as with display, call, data communication, camera operation and record operate the operation be associated.Processing components 802 can comprise one or more processor 820 to perform instruction, to complete all or part of step of above-mentioned method.In addition, processing components 802 can comprise one or more module, and what be convenient between processing components 802 and other assemblies is mutual.Such as, processing components 802 can comprise multi-media module, mutual with what facilitate between multimedia groupware 808 and processing components 802.
Storer 804 is configured to store various types of data to be supported in the operation of device 800.The example of these data comprises for any application program of operation on device 800 or the instruction of method, contact data, telephone book data, message, picture, video etc.Storer 804 can be realized by the volatibility of any type or non-volatile memory device or their combination, as static RAM (SRAM), Electrically Erasable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory EPROM (EPROM), programmable read only memory (PROM), ROM (read-only memory) (ROM), magnetic store, flash memory, disk or CD.
The various assemblies that power supply module 806 is device 800 provide electric power.Power supply module 806 can comprise power-supply management system, one or more power supply, and other and the assembly generating, manage and distribute electric power for device 800 and be associated.
Multimedia groupware 808 is included in the screen providing an output interface between described device 800 and user.In certain embodiments, screen can comprise liquid crystal display (LCD) and touch panel (TP).If screen comprises touch panel, screen may be implemented as touch-screen, to receive the input signal from user.Touch panel comprises one or more touch sensor with the gesture on sensing touch, slip and touch panel.Described touch sensor can the border of not only sensing touch or sliding action, but also detects the duration relevant to described touch or slide and pressure.In certain embodiments, multimedia groupware 808 comprises a front-facing camera and/or post-positioned pick-up head.When device 800 is in operator scheme, during as screening-mode or video mode, front-facing camera and/or post-positioned pick-up head can receive outside multi-medium data.Each front-facing camera and post-positioned pick-up head can be fixing optical lens systems or have focal length and optical zoom ability.
Audio-frequency assembly 810 is configured to export and/or input audio signal.Such as, audio-frequency assembly 810 comprises a microphone (MIC), and when device 800 is in operator scheme, during as call model, logging mode and speech recognition mode, microphone is configured to receive external audio signal.The sound signal received can be stored in storer 804 further or be sent via communications component 816.In certain embodiments, audio-frequency assembly 810 also comprises a loudspeaker, for output audio signal.
I/O interface 812 is for providing interface between processing components 802 and peripheral interface module, and above-mentioned peripheral interface module can be keyboard, some striking wheel, button etc.These buttons can include but not limited to: home button, volume button, start button and locking press button.
Sensor module 814 comprises one or more sensor, for providing the state estimation of various aspects for device 800.Such as, sensor module 814 can detect the opening/closing state of device 800, the relative positioning of assembly, such as described assembly is display and the keypad of device 800, the position of all right pick-up unit 800 of sensor module 814 or device 800 1 assemblies changes, the presence or absence that user contacts with device 800, the temperature variation of device 800 orientation or acceleration/deceleration and device 800.Sensor module 814 can comprise proximity transducer, be configured to without any physical contact time detect near the existence of object.Sensor module 814 can also comprise optical sensor, as CMOS or ccd image sensor, for using in imaging applications.In certain embodiments, this sensor module 814 can also comprise acceleration transducer, gyro sensor, Magnetic Sensor, pressure transducer, microwave remote sensor or temperature sensor.
Communications component 816 is configured to the communication being convenient to wired or wireless mode between device 800 and other equipment.Device 800 can access the wireless network based on communication standard, as WiFi, 2G or 3G, or their combination.In one exemplary embodiment, communications component 816 receives from the broadcast singal of external broadcasting management system or broadcast related information via broadcast channel.In one exemplary embodiment, described communications component 816 also comprises near-field communication (NFC) module, to promote junction service.Such as, can based on radio-frequency (RF) identification (RFID) technology in NFC module, Infrared Data Association (IrDA) technology, ultra broadband (UWB) technology, bluetooth (BT) technology and other technologies realize.
In the exemplary embodiment, device 800 can be realized, for performing said method by one or more application specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing appts (DSPD), programmable logic device (PLD) (PLD), field programmable gate array (FPGA), controller, microcontroller, microprocessor or other electronic components.
In the exemplary embodiment, additionally provide a kind of non-transitory computer-readable recording medium comprising instruction, such as, comprise the storer 804 of instruction, above-mentioned instruction can perform said method by the processor 820 of device 800.Such as, described non-transitory computer-readable recording medium can be ROM, random access memory (RAM), CD-ROM, tape, floppy disk and optical data storage devices etc.
A kind of non-transitory computer-readable recording medium, when the instruction in described storage medium is performed by the processor of terminal, make terminal can perform scape dividing method before and after a kind of image, described method comprises:
Obtain the foreground area of image to be split, possibility foreground area and background area;
The neighbor pixel of each pixel is determined according to the colouring information of each pixel in described image to be split and spatial positional information;
According to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region;
According to each pixel in described image to be split as the probable value in target prospect region, front and back scape segmentation is carried out to described image to be split.
Those skilled in the art, at consideration instructions and after putting into practice invention disclosed herein, will easily expect other embodiment of the present disclosure.The disclosure is intended to contain any modification of the present disclosure, purposes or adaptations, and these modification, purposes or adaptations are followed general principle of the present disclosure and comprised the undocumented common practise in the art of the disclosure or conventional techniques means.Instructions and embodiment are only regarded as exemplary, and true scope of the present disclosure and spirit are pointed out by claim below.
Should be understood that, the disclosure is not limited to precision architecture described above and illustrated in the accompanying drawings, and can carry out various amendment and change not departing from its scope.The scope of the present disclosure is only limited by appended claim.
The foregoing is only preferred embodiment of the present disclosure, not in order to limit the disclosure, all within spirit of the present disclosure and principle, any amendment made, equivalent replacements, improvement etc., all should be included within scope that the disclosure protects.
Claims (11)
1. a scape dividing method before and after image, is characterized in that, described method comprises:
Obtain the foreground area of image to be split, possibility foreground area and background area;
The neighbor pixel of each pixel is determined according to the colouring information of each pixel in described image to be split and spatial positional information;
According to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region;
According to each pixel in described image to be split as the probable value in target prospect region, front and back scape segmentation is carried out to described image to be split.
2. method according to claim 1, is characterized in that, the foreground area of described acquisition image to be split, possible foreground area and background area, comprising:
Utilize image recognition algorithm to mark foreground area in described image to be split and may foreground area, mark foreground area in described image to be split and may the region outside foreground area be background area;
Or,
Obtain the foreground area to described image to be split of input, may the mark instructions of foreground area and background area, according to described mark instructions determine described image to be split foreground area, may foreground area and background area.
3. method according to claim 1, is characterized in that, the described colouring information according to each pixel in described image to be split and spatial positional information determine the neighbor pixel of each pixel, comprising:
The colouring information of pixel each in image to be split and spatial positional information are inputed in default non local nearest neighbor algorithm model, obtain the neighbor pixel of described each pixel according to the Output rusults of described non local nearest neighbor algorithm model.
4. method according to claim 1, is characterized in that, described according to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region, comprising:
According to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, solve the transparency of each pixel as described probable value.
5. method according to claim 4, is characterized in that, described according to each pixel in described image to be split as the probable value in target prospect region, scapes segmentation in front and back is carried out to described image to be split, comprising:
Self-adaption binaryzation is carried out to the transparency of each pixel in described image to be split;
According to the transparency after the self-adaption binaryzation of each pixel, detect the profile in described image to be split;
Detect the area in the region that each bar profile surrounds, region maximum for area is defined as described target prospect region;
Described target prospect region is partitioned into from described image to be split.
6. a scape segmenting device before and after image, is characterized in that, described device comprises:
Area acquisition unit, is configured to obtain the foreground area of image to be split, possible foreground area and background area;
Neighbor pixel determining unit, is configured to the neighbor pixel determining each pixel according to the colouring information of each pixel in described image to be split and spatial positional information;
Probable value determining unit, be configured to according to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region;
Cutting unit, is configured to according to each pixel in described image to be split as the probable value in target prospect region, carries out front and back scape segmentation to described image to be split.
7. device according to claim 6, is characterized in that, described area acquisition unit, comprising:
Zone marker subelement, is configured to utilize image recognition algorithm to mark foreground area in described image to be split and may foreground area, marks foreground area in described image to be split and may the region outside foreground area be background area;
Or,
Subelement is determined in region, be configured to obtain the foreground area to described image to be split of input, may the mark instructions of foreground area and background area, according to described mark instructions determine described image to be split foreground area, may foreground area and background area.
8. device according to claim 6, is characterized in that, described neighbor pixel determining unit, comprising:
Neighbor pixel exports subelement, be configured to the colouring information of pixel each in image to be split and spatial positional information be inputed in default non local nearest neighbor algorithm model, obtain the neighbor pixel of described each pixel according to the Output rusults of described non local nearest neighbor algorithm model.
9. device according to claim 6, is characterized in that, described probable value determining unit, comprising:
Transparency solves subelement, be configured to according to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, solve the transparency of each pixel as described probable value.
10. device according to claim 9, is characterized in that, described cutting unit, comprising:
Self-adaption binaryzation subelement, is configured to carry out self-adaption binaryzation to the transparency of each pixel in described image to be split;
Contour detecting subelement, is configured to the transparency after according to the self-adaption binaryzation of each pixel, detects the profile in described image to be split;
Subelement is determined in target prospect region, is configured to the area detecting the region that each bar profile surrounds, region maximum for area is defined as described target prospect region;
Segmentation subelement, is configured to be partitioned into described target prospect region from described image to be split.
11. 1 kinds of terminals, is characterized in that, comprising:
Processor;
For the storer of storage of processor executable instruction;
Wherein, described processor is configured to:
Obtain the foreground area of image to be split, possibility foreground area and background area;
The neighbor pixel of each pixel is determined according to the colouring information of each pixel in described image to be split and spatial positional information;
According to described foreground area, may the neighbor pixel of foreground area and background area and each pixel, determine the probable value of each pixel as target prospect region;
According to each pixel in described image to be split as the probable value in target prospect region, front and back scape segmentation is carried out to described image to be split.
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