CN104050682B - Image segmentation method fusing color and depth information - Google Patents
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Abstract
The invention discloses an image segmentation method fusing color and depth information. According to the method, firstly, a meanshift algorithm is used for segmenting an input color image to obtain an over-segmentation region set, and then similarities among all the regions are calculated and include color similarities, depth similarities and fusions of the color similarities and the depth similarities; then according to a depth image, seed regions of a target and seed regions of a background are automatically selected; finally, an MSRM algorithm is used for merging the regions, so that a final segmentation result is obtained. In the process of calculating the similarities among the regions, the color information is used, besides, the depth information is dynamically fused, and the problem that when the target color and the background color are similar, and namely a long-scale contrast edge exists between objects, correct segmentation can not be achieved is solved. The seed regions are automatically selected by the utilization of the depth information of the image, the seed regions of the target and the seed regions of the background do not need to be marked manually and alternately, region characteristics of the depth image are directly used for determining the seed regions instead of edge characteristics, and therefore high robustness is achieved.
Description
Technical field
The invention belongs to computer vision field is and in particular to the image segmentation side of a kind of Fusion of Color and depth information
Method.
Background technology
Image segmentation is to divide the image into several regions specific, with unique properties and propose interesting target
Technology and process.It is the committed step that image procossing arrives graphical analyses.The target extracting after image segmentation can be used for figure
As semantics recognition, the field such as image retrieval.Traditional images dividing method is generally basede on the external appearance characteristic of image, such as color, brightness,
Texture, shape, structure etc..And really object exists in three-dimensional world, object should be defined by connectedness physically, knot
The depth information closing image carrys out segmentation figure picture, can obtain and have certain semanteme efficiently against over-segmentation and less divided problem
Image segmentation result.
At present, image partition method is countless, the region merging technique based on maximum similarity that wherein [document 1] proposes
Method (hereinafter referred to as msrm algorithm) is fairly simple, to picture material self adaptation, need not arrange similarity threshold, Neng Goucong in advance
Objective contour is extracted in complex background.The advantage of msrm algorithm is: cuts (graph cut) with the classic algorithm figure of man-machine interactively
Compare, under the conditions of identical man-machine interactively, segmentation effect is more preferable.It is limited in that: need labelling to cover main characteristic area
Domain, when shade, low contrast edge and fuzzy region, segmentation is failure.
Have a lot of documents that depth image is introduced image segmentation field in recent years, [document 2] propose a kind of based on depth and
The image object partitioning algorithm of colouring information, carries out over-segmentation first with meanshift partitioning algorithm to target image, simultaneously
Obtain the dense depth map of axonometric chart pair by binocular stereo vision algorithm, select from over-segmentation result according to depth discontinuity
Take the seed point set proceeding that " exquisite " is split, algorithm distribution label is cut to the region figure of unallocated Seed label, and right
The adjacent area not having depth noncoherent boundary each other but having different labels is merged.The limitation of this algorithm exists
In: when (1) is cut algorithm (graph cut) and carried out global optimization with figure, only use colouring information;(2) image two obtaining
Value depth discontinuous edge is strongly depend on experimental threshold values, and edge line in itself be interruption, discontinuous, this will affect seed
The reliability that point is chosen.[document 3] proposes a kind of multimode semantic segmentation method based on color and depth information, and this algorithm is by stricture of vagina
Reason, color description and 3d description are fused together by Markov random field model, are super-pixel distribution label.The party
Method needs to train, computationally intensive.What [document 4] proposed is positioned using colored and depth image in robot visual guidance system
Object, when being only applicable to that in image, several object colors are single and completely the same, the difference using depth is distinguished.
Document 1:ning j., zhang l., zhang d., et al.interactive image segmentation
by maximal similarity based region merging.pattern recognition,2010,43(2):
445-456;
Document 2: Pi Zhiming, Wang Zengfu. merge the image object partitioning algorithm of depth and colouring information. pattern recognition and people
Work intelligence, 2013,26 (2): 151-158;
Document 3:islem jebari, david filliat.color and depth-based superpixels
For background and object segmentation.procedia engineering, 2012,41:1307-
1315;
Document 4:jos é-juan, hern á ndez-l ó pez, ana-linnet.et al.detecting objects
using color and depth segmentation with kinect sensor.procedia technology,
2012,3:196-204.
Content of the invention
It is an object of the invention to provide one kind can more accurately in addition when target and background color is complicated and close
Distinguish and can utilize the image partition method in the region characteristic automatic selected seed region of depth image.
The technical solution adopted in the present invention is: the image partition method of a kind of Fusion of Color and depth information, its feature
It is, comprise the following steps:
Step 1: using meanshift algorithm, input color image is made to split, obtain overdivided region set g=
{gi}I=1 ..., rn, wherein, subscript i represents region sequence number, and rn is region total number;
Step 2: calculate the similarity between each region in g, including color similarity scWith depth similarity sd, Yi Jiyan
Color similarity scWith depth similarity sdFusion;
Step 3: target and background seed region is automatically chosen according to depth image;
Step 4: carry out region merging technique using msrm algorithm, obtain final segmentation result.
Preferably, the color similarity between each region in the calculating g described in step 2, it implements process is
Define the color similarity s of any two region r and q in g using bhattacharyya coefficientc:
Wherein, histrAnd histqIt is respectively the normalization color histogram of region r and q, subscript u represents histogrammic u
Individual element, u is histogrammic footstalk number.
Preferably, the depth similarity between each region in the calculating g described in step 2, it implements process is
The depth value of the pixel in each region in g is taken arithmetic average as the depth value in this region, constitute regional depth set d=
{di}I=1 ..., rn, subscript i represents region sequence number, defines depth similarity s of any two region r and q in gd:
Wherein, max { di}I=1 ..., rnRepresent that all regional depths take maximum, min { di}I=1 ..., rnFor all regional depths
Minima in addition to 0.
Preferably, the described depth value using the pixel in each region in g takes arithmetic average as the depth in this region
Value, the depth for partial pixel in image not can determine that due to the reason such as blocking, and in the depth image being given be using 0 as
The situation that depth value is filled up, concrete processing method is: overdivided region set g is mapped in depth image, if too
Cut region giThe depth value of middle element is all 0, show this region object depth information do not know, then only consider this region with
The color similarity of adjacent area;If overdivided region giIn have Partial Elements depth value be 0, then calculate this regional depth
Value diWhen, only to overdivided region giThose pixels that middle depth is not zero take arithmetic average.
Preferably, the color similarity s described in step 2cWith depth similarity sdFusion, scAnd sdTotal after fusion
Similarity be:
S=sc+w·sd
Wherein, scAnd sdThe weight w merging to be described using nonlinear sigmoid curve:
The maximum that wherein a determines sigmoid curve approaches value, b and c determine respectively sigmoid curve displacement and
Steep.
Preferably, automatically choosing target and background seed region according to depth image described in step 3, it implements
Process is first with k-means algorithm, the element in regional depth set d to be clustered, and classification number takes k=2, automatically
Be polymerized to two big class, i.e. target and background, then respectively from this some region of two big apoplexy due to endogenous wind random chooses as target and background
Seed region.This clustering method is simple, solves the problems, such as to need in Ning Ji peak method manually to participate in marking.
Present invention advantage compared with prior art is:
(1) between zoning during similarity, not only utilize colouring information, go back dynamic fusion depth information, then
When in image, target and background color is close, when low contrast edge occurring between object and object, can by depth not
With distinguishing;
(2) utilize image depth information automatic selected seed region, mark the kind of target and background without man-machine interactively
Subregion, directly utilizes the region characteristic of depth image, rather than local edge, to determine seed region, has preferable robust
Property.
Brief description
Fig. 1: be flow chart of the present invention;
Fig. 2: be the sigmoid curve used in the embodiment of the present invention;
Fig. 3-1: be the coloured image of input in the embodiment of the present invention;
Fig. 3-2: be the depth image of input in the embodiment of the present invention;
Fig. 4: be the result that in the embodiment of the present invention, coloured image is made with meanshift segmentation;
Fig. 5-1: be that in the embodiment of the present invention, regional depth is made with k-means cluster result;
Fig. 5-2: be the seed region chosen in the embodiment of the present invention;
Fig. 6-1: be the final segmentation result being obtained according to the inventive method in the embodiment of the present invention;
Fig. 6-2: be the final segmentation result being obtained according to Ning Ji peak method in the embodiment of the present invention.
Specific embodiment
Understand for the ease of those of ordinary skill in the art and implement the present invention, below in conjunction with the accompanying drawings and embodiment is to this
Bright be described in further detail it will be appreciated that described herein enforcement example be merely to illustrate and explain the present invention, not
For limiting the present invention.
Ask for an interview Fig. 1, Fig. 2, Fig. 3-1, Fig. 3-2 and Fig. 4, as a example the present invention adopts the segmentation of Aloe potted landscape shown in Fig. 3-1,
Fig. 3-2 is the depth image of Aloe potted landscape, make use of the information of depth image that coloured image is made more accurately to split.The present invention
Be the technical scheme is that the image partition method of a kind of Fusion of Color and depth information, comprised the following steps:
Step 1: using the framework of Ning Jifeng, using meanshift segmentation software edison to the Aloe basin shown in Fig. 3-1
Scape is split, and asks for an interview Fig. 4, is over-segmentation result, obtains overdivided region set g={ gi}I=1 ..., rn, wherein, subscript i generation
Table section sequence number, rn is region total number;Software relevant parameter is all using default setting.It can be seen that the background based on texture is divided
It is slit into a large amount of discreet region, and Aloe potted landscape is divided into larger region unit.
Step 2: 3 Color Channels of the rgb coloured image of input are divided into 16 deciles by codomain [0,255] respectively,
The color histogram in so each region just calculates in the feature space for 16 × 16 × 16=4096 for the dimension.Then right
Color histogram normalization, the color similarity of two adjacent areas of calculating:
histrAnd histqIt is respectively the normalization color histogram of any two region r and q in g, subscript u represents Nogata
U-th element of figure.Create two-dimentional similarity matrix sm, dimension is rn × rn.If two regions non-conterminous it is impossible to be closed
And, make smij=0, each region oneself is maximum with the similarity of oneself, is set to 1, i.e. diagonal entry smii=1, adjacent area
According to formula one calculate color similarity, span be [0,1).
The depth value of the pixel in each region in g is taken arithmetic average as the depth value in this region, constitute set d=
{di}I=1 ..., rn, subscript i represents region sequence number.Assume that ith zone has n pixel, the depth value set of each pixel is
{x1,…,xn, then:
Define depth similarity s of two region r and qd:
In formula three, max { di}I=1 ..., rnRepresent that all regional depths take maximum, min { di}I=1 ..., rnDeep for all regions
Minima in addition to 0 for the degree.If overdivided region giThe depth value of middle element is all 0, then it is believed that this region no depth
Information, only considers the color similarity in this region and adjacent area;If overdivided region giIn have Partial Elements depth value be
0, then calculate this regional depth value diWhen, only arithmetic average is taken to the pixel that depth is not zero.From formula three, depth phase
Like degree sdSpan be [- 1,0], the depth difference in two regions is bigger, then depth similarity is less.
Step 3: color similarity scWith depth similarity sdFusion, scAnd sdAfter fusion, total similarity is:
S=sc+w·sd(formula is wantonly)
Ask for an interview Fig. 2, scAnd sdThe weight w merging to be described using nonlinear sigmoid curve:
The maximum that wherein a determines sigmoid curve approaches value, b and c determine respectively sigmoid curve displacement and
Steep.
It is demonstrated experimentally that a=1, the objective contour effect extracted when b=0.2, c=0.5 is preferable.Due to color similarity sc
∈ [0,1], Fig. 2 only show and work as a=1, a part for sigmoid curve when b=0.2, c=0.5.
Step 4: with k-means algorithm to d={ di}I=1 ..., rnMiddle element is clustered, and takes k=2, is polymerized to two big class, that is,
Target class roWith background classes rb.Assume roIncluding m region, rbComprise n region, then m+n=rn.Respectively from this two big apoplexy due to endogenous wind
M1 and n1 region of random choose is as the seed region of target and background, wherein m1 < m, n1 < n.M1 and n1 specific number is permissible
By user from primary input program, find in practice, 1/40th effects that m1 and n1 is about taken as m and n are just fine, no
With taking too many seed region.If Fig. 5-1 is this example region depth cluster result, white represents target class, and black represents the back of the body
Scape class, then randomly selects 5 seed regions from target class (containing 191 overdivided regions), background classes are (containing 1267 over-segmentations
Region) 27 seed regions of random choose, because in background classes, each region area is less, region quantity is larger, therefore can fit
Choose some seed regions when, as shown in Fig. 5-2, green area represents target seed region, and blue region represents background kind more
Subregion.
Step 5: region merging technique is carried out using msrm algorithm, obtains final segmentation result.
If mbFor the background area set of labelling, moFor the target area set of labelling, n is unmarked regional ensemble, then
The basic procedure of msrm algorithm is as follows:
Step 5.1: merge the background area of unmarked region and labelling.For each region b ∈ mb, find the adjacent of it
Set of regionsFor each aiAndFind its Neighbourhood setObviouslyCalculateIfThen by region aiIt is merged into b.Update set
mbAnd n.Until mbIn region can not find new combined region till;
Step 5.2: the region in self-adopt combination n.In the same manner, for each region p ∈ n, find its neighborhood collectionFor each hiMeetAndFind its neighborhood collectionObviouslyCalculateIfThen by region hiIt is merged into p.Update collection
Close n.Till the region in n can not find new combined region.
Circulation carries out step 5.1 and step 5.2 until unmarked region can neither be merged into mbIn, nor close in n
And.This algorithm progressively by unmarked region merging technique in background area, remaining unmarked region is automatically incorporated into target area
In.
Under conditions of seed region is chosen unanimously, Fig. 6-1 is the final segmentation result being obtained according to the inventive method, figure
6-2 is the final segmentation result being obtained according to Ning Jifeng et al. proposition method.It is seen that, the color due to curtain in background is
Absinthe-green decorative pattern, and Aloe leave color is close, the target area therefore do not laid down hard and fast rule as seed all can be by mistakenly
It is merged in background classes.And background and target can be precisely separating by the present invention using the difference of depth.
It should be appreciated that the part that this specification does not elaborate belongs to prior art.
It should be appreciated that the above-mentioned description for preferred embodiment is more detailed, can not therefore be considered to this
The restriction of invention patent protection scope, those of ordinary skill in the art, under the enlightenment of the present invention, is weighing without departing from the present invention
Profit requires under protected ambit, can also make replacement or deform, each fall within protection scope of the present invention, this
Bright scope is claimed should be defined by claims.
Claims (3)
1. the image partition method of a kind of Fusion of Color and depth information is it is characterised in that comprise the following steps:
Step 1: using meanshift algorithm, input color image is made to split, obtain overdivided region set g=
{gi}I=1 ..., rn, wherein, subscript i represents region sequence number, and rn is region total number;
Step 2: calculate the similarity between each region in g, including color similarity scWith depth similarity sd, and color phase
Like degree scWith depth similarity sdFusion;
Wherein calculate the depth similarity between each region in g, it implements process is by the depth of the pixel in each region in g
Angle value takes arithmetic average as the depth value in this region, constitutes regional depth set d={ di}I=1 ..., rn, subscript i represents region
Sequence number, defines depth similarity s of any two region r and q in gd:
Wherein, max { di}I=1 ..., rnRepresent that all regional depths take maximum, min { di}I=1 ..., rnFor all regional depths except
Minima beyond 0;
Wherein the depth value of the pixel in each region in g is taken arithmetic average as the depth value in this region, in the middle part of image
The depth of point pixel not can determine that, and is using 0 situation about being filled up as depth value in the depth image being given, and concrete processes
Method is: overdivided region set g is mapped in depth image, if overdivided region giThe depth value of middle element is all 0, table
The depth information of this region object bright does not know, then only consider the color similarity in this region and adjacent area;If over-segmentation
Region giIn have Partial Elements depth value be 0, then calculate this regional depth value diWhen, only to overdivided region giMiddle depth
Those pixels being not zero take arithmetic average;
Step 3: target and background seed region is automatically chosen according to depth image;
Step 4: the method using the region merging technique based on maximum similarity carries out region merging technique, obtains final segmentation result.
2. Fusion of Color according to claim 1 and depth information image partition method it is characterised in that: in step 2
Described color similarity scWith depth similarity sdFusion, scAnd sdAfter fusion, total similarity is:
S=sc+w·sd
Wherein, scAnd sdThe weight w merging to be described using nonlinear sigmoid curve:
The maximum that wherein a determines sigmoid curve approaches value, b and c determines the displacement of sigmoid curve and precipitous respectively
Degree.
3. Fusion of Color according to claim 1 and depth information image partition method it is characterised in that: step 3 institute
That states chooses target and background seed region automatically according to depth image, and it implements process is to calculate first with k-means
Method clusters to the element in regional depth set d, and classification number takes k=2, is automatically polymerized to two big class, i.e. target and background,
Then respectively from this some region of two big apoplexy due to endogenous wind random chooses as target and background seed region.
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