CN102385757B - Semantic restriction texture synthesis method based on geometric space - Google Patents

Abstract

The invention discloses a semantic restriction texture synthesis method based on geometric space, and the synthesis result has the structural characteristics different from the master drawing. The method has the key point that a texture topological structure is analyzed and processed, and a two-dimension texture grid is used for describing the particular characteristics of the texture. Firstly, pixel mask of the sample texture is used for forming a sample texture gird. Then the target texture grid is synthesized according to the restriction conditions of the given topological structure, namely the small blocks of grids given by a user. Finally, under the guide and control of the texture grid, each pixel of the target texture is synthesized by adopting a per-pixel synthesis algorithm based on characteristic vector, so that the aim of changing the texture topological structure can be realized. The invention realizes the aim of synthesizing a plurality of textures with different structural characteristics based on one sample texture.

Description

A kind of semantic constraint texture synthesis method based on geometric space

Technical field

The present invention relates to a kind of texture synthesis method based on master drawing.

Background technology

Synthetic based on the texture of master drawing is the very popular and important research direction in computer graphical and iconology field.In early days, people mainly study and how to utilize a fritter sample texture to obtain the bulk texture, have emerged synthetic and based on numerous outstanding methods such as the block-by-block of piece splicing thought are synthetic by pixel based on the neighborhood matching algorithm.Middle and later periods, particularly in recent years, a large amount of research is all no longer simple is devoted to promote synthetic quality and raising speed, but present diversity from many aspects, as synthetic in characteristic matching, dynamic texture synthetic, non-homogeneous texture is synthetic, multi-dimension texture is synthetic, and the mixing of texture and level and smooth conversion etc.

1) planar grains synthetic method

By the at every turn synthetic pixel of pixel texture synthesis method, synthesize based on the neighborhood matching algorithm.This algorithm sees the non-parametric sampling synthetic method of Efros etc. the earliest, and basic thought is: the distance of the neighborhood by contrasting all master drawing neighborhood of pixels and pixel to be synthesized is chosen the optimum matching pixel, so undertaken by pixel, thus synthetic target texture.Neighborhood of pixels is generally pressed vertical direction and horizontal direction structure, is divided into full neighborhood, half neighborhood, L shaped neighborhood.Full neighborhood is used for walking abreast, and texture synthesizes and the optimization of texture, and half neighborhood and L shaped neighborhood are suitable for the serial synthetic method.But parallel controllable synthesis method is a kind of exchange method in real time.Utilize the outward appearance vector to promote greatly synthetic quality based on the appearance space texture of the method is synthetic.Parallel controlledly synthesis is used for the mixing of structured image recently again by improvement such as Risser.The block-by-block synthetic method is chosen a texture block at every turn and is copied in target texture from master drawing.Efros etc. and Kwatra etc. use dynamic programming to seek a separatrix in the overlapping region of new piece and composite part.Wang Tiles ﹠ Future Opportunities of Texture Synthesis has real-time take the method for Efros etc. as the basis, and its newest research results is Deng half randomness Tile synthetic method.

2) curved surface texture synthesis method

Synthetic texture mainly contains three kinds of modes at present on curved surface: the first is directly synthetic, first with the curved surface refinement, then calculates the color on all curved surfaces summit.This mode is the most suitable dynamic texture synthetic.The second is indirect synthesis mode, and the method based on texture map of Ying etc. and Lefebvre etc. is divided into several zones with whole curved surface to be mapped on the plane, more synthetic in the plane these zone-textures.The third is to spread texture block to curved surface, until whole curved surface all is capped.In the method for Soler etc., the corresponding curved surface triangle of texture block.The method of Praun etc. repeats bedding with an irregular grain on curved surface.Fu etc. expand to Wang Tiles method on curved surface based on the PolyCube mapping of Tarini etc.

3) signature analysis and matching process

Texture particle and feature distortion method have realized controlling texture is synthetic at the pel layer.Wu etc. utilize characteristic pattern to keep the architectural feature of texture.Based on effective characteristic analysis method, the mixing of texture just is achieved with level and smooth conversion.Because the existence of the physical factors such as perspective and illumination, the texture in photo often has very strong stereoscopic sensation.Liu etc. utilize deformation domain to carry out texture in photo and replace, and Eisenacher etc. have realized from the synthetic texture of photo based on Jacobian field.Characteristic matching is synthetic based on the texture optimized algorithm, the synthetic characteristic curve on Matching Model surface as a result.Dischler etc. carry out semantic relevant control based on texture grid to texture is synthetic.

Summary of the invention

Technology of the present invention is dealt with problems and is: texture can be divided into structural and the unstructuredness texture from architectural feature, can be divided into according to the distribution situation of feature all even non-homogeneous textures again.For the homogeneous texture texture, the present invention proposes a kind of semantic constraint synthetic method, by its semantic feature (architectural feature) is analyzed and processed, makes the user can select by the wish of oneself architectural feature of synthetic texture.Solve existing texture synthesis method and can only synthesize the defective that has the texture of same architectural feature with the sample texture.

Technical scheme of the present invention is:

(1) texture grid is synthetic

Based on the synthetic target texture grid of piece splicing.Generate a left margin and right margin coupling with sample texture grid (extraction or user are given from sample texture), the gridblock of coboundary and lower boundary coupling.A plurality of copies of this gridblock are spliced mutually obtain the initial target texture grid, and then through optimizing, reduce the multiplicity of target texture grid, promote mesh quality.Here, be divided into two kinds of situations: the one, if be exactly the texture grid of sample texture for the synthesis of the sample grid of target texture grid, the grid of target texture grid and sample texture has the same topological structure so, and like this, final target texture just is consistent on semantic structure with master drawing; The 2nd, if synthesize with another sample grid that is different from the grid of sample texture, target gridding has different topological structures from the grid of sample texture, thereby final target texture will have the semantic structure of user's appointment, no longer be subject to sample texture.

(2) synthesize target texture based on the neighborhood matching algorithm under the constraint of texture grid

The present invention adopts traditional neighborhood matching algorithm can't obtain gratifying synthetic quality because will realize that the semantic constraint texture is synthetic, synthesizes so the introduced feature vector is assisted.Prior synthesizing method is not intended to change the texture semantic feature, can directly construct neighborhood of pixels by vertical and horizontal direction, and in the present invention, the structure of neighborhood need to be according to eigenvector.Eigenvector is to be determined by the relative position of pixel and textural characteristics.See intuitively eigenvector and primitive boundary cardinal principle vertical (Fig. 6).

For target texture, first to the grid cell of each target texture grid, choose at random one from all grid cells of the texture grid of sample texture, the mapping relations of these two grid cell internal point are set up in mapping according to polygon.Each pixel that is in this way target texture is determined a position in sample texture.Here, this position coordinates can not be just integer, can determine pixel color based on bilinear interpolation.After the initialization pixel, according to the full neighborhood of eigenvector structure pixel, then correct each pixel color based on the neighborhood matching algorithm, obtain net result.

The present invention mainly contains 2 contributions: the first, provide a kind of 2 d texture grid composition algorithm, be applicable to irregular, approximate rule, and regular grid.Than the recursive optimization method of Dischler etc., our method does not need a large amount of manual inputs.The second, the use characteristic vector promotes synthetic effect, has solved traditional neighborhood matching algorithm and has been used for the undesirable problem of semantic constraint texture synthetic effect, also is better than existing sub-texture method.

Description of drawings

Fig. 1 algorithm overall flow figure;

Fig. 2 (a) sample texture, Fig. 2 (b) pel mask, Fig. 2 (c) characteristic pattern, Fig. 2 (d) texture grid;

Fig. 3 gridblock Boundary Match cost;

The antidote of Fig. 4 gridblock;

Fig. 5 (a) is sample grid, and Fig. 5 (b) is by the synthetic result of sample grid;

Fig. 6 (a) has marked the eigenvector of pixel in the sample texture, and Fig. 6 (b) has marked the eigenvector of pixel in the target texture;

Fig. 7 (a) is sample texture, and Fig. 7 (b) is the existing methods synthetic effect, and Fig. 7 (c) is the synthetic effect of the inventive method;

Fig. 8 (a) is sample texture, and Fig. 8 (b) is semantic constraint condition (each master drawing has respectively two different constraint conditions), and Fig. 8 (c) is with same master drawing, the effect of synthetic texture under various boundary conditions.

Embodiment

Our method relies on texture grid, and it is the structure in a kind of two-dimensional geometry space, is used for the architectural feature of description texture, is comprised of summit and limit.This grid can obtain according to image processing techniques (Fig. 2): pel mask (a kind of binary map that first generates sample texture, white portion is the pel district, other zones are black), then according to pel mask generating feature figure, in characteristic pattern, each lines is that single pixel is wide, generates at last the texture grid of sample texture with characteristic pattern.This grid is taken as sample grid for the synthesis of the target texture grid.Because sample grid also can be artificially given, so the user just can determine according to the wish of oneself which type of architectural feature target texture has.

Semantic constraint texture synthesis method based on geometric space of the present invention is specific as follows:

First step: synthetic target texture grid

Our whole texture grid building-up process is take gridblock Boundary Match cost function as the basis.As shown in Figure 3, the computing method of Boundary Match cost are: for any two gridblock p and q, b _pThe right margin of p, b _qIt is the left margin of q.b _pWill with some grids in the limit intersect, changing a kind of saying namely has some (can be 0) limits by border b _pCut apart, exist equally some limits by b _qCut apart.If by b _p, b _qThe number on the limit of cutting apart separately is different, claims b _p, b _qDo not mate, or the coupling cost be the infinity, otherwise, claim them mutually to mate, also claim restrained boundary each other.Suppose that now they mate mutually, order Cut-point and b are pressed in expression _pThe i bar limit apart from the ascending order arrangement of initial end points (vertically upper extreme point is got on the border, and horizontal boundary is got left end point),

Respectively two end points on limit.The in here is endpoint attribute, represents this end points and b _pAffiliated gridblock is positioned at b _pHomonymy, otherwise out represents to be positioned at b _pHeteropleural, as shown in Figure 3.

Coordinate figure get with respect to b _pThe coordinate offset amount of initial end points.Equally,

Represent respectively by border b _qThe i bar limit of cutting apart, and two end points on this limit.The limit With

Be actually two line segments in two-dimensional space, we calculate distance between them from both direction respectively.Following formula calculate from

Arrive

Distance:

$D(e_p^i,e_q^i)=(1+\mathrm{Dist}(v_p^{i,\mathrm{in}},v_q^{i,\mathrm{out}}))(1+\mathrm{\λ}||\stackrel{\→}{n}(v_p^{i,\mathrm{in}},v_p^{i,\mathrm{out}})-\stackrel{\→}{n}(v_p^{i,\mathrm{in}},v_q^{i,\mathrm{in}})|\left|\right)---\left(1\right)$

Wherein,

Be

Distance,

The expression direction vector, λ is weight.

Calculating just in time with

On the contrary.We define the limit With

The coupling cost be:

$E(e_p^i,e_q^i)=D(e_p^i,e_q^i)+D(e_q^i,e_p^i)---\left(2\right)$

Now hypothesis is respectively by border b _pAnd b _qThe limit number of cutting apart separately is N _cut, the coupling cost on these two borders is so:

$E(b_p,b_q)=\underset{i=1}{\overset{N_{\mathrm{cut}}}{\mathrm{\Σ}}}E(e_p^i,e_q^i)---\left(3\right)$

For any one gridblock MP, remember that its four edges circle is

The set that is made of them is B (MP), and restrained boundary and the set thereof of this four edges circle simultaneously is designated as

Always mating cost is:

$E(B\left(\mathrm{MP}\right),\hat{B}\left(\mathrm{MP}\right))=\underset{u=t,b,l,r}{\mathrm{\Σ}}E(b_{\mathrm{MP}}^u,\widehat{b_{\mathrm{MP}}^u})---\left(4\right)$

Wherein

It is the border

And border

The coupling cost, calculate with formula (3),

Corresponding b _p,

Corresponding b _qFollowing formula is called gridblock Boundary Match cost function.

Based on the Boundary Match cost function, we will synthesize and be divided into two stages: initialization procedure and optimizing process.

1.1) our target gridding (is designated as M _tgt) initialization procedure is: first (user is given or extract from sample texture, is designated as M by sample grid _smp) generate a seamless gridblock, then a plurality of copies that will this seamless gridblock are spliced into the initial target grid.The seamless gridblock here refers to that only its copy need be carried out simple combination just can obtain a complete grid, and does not need interpolation, deletion, mobile grid summit or limit.In the present invention, this special gridblock is designated as MP _s

Our strategy is: from all from M _smpGridblock in the search " the best " gridblock (be designated as MP _o), then its vertex position is corrected obtained MP _sSo how to search for " the best " gridblock, this is the Boundary Match cost function of the present invention part that plays a role just.For gridblock MP, we are with its coboundary

As lower boundary

Restrained boundary, with the restrained boundary of lower boundary as the coboundary, and with the given left margin of the same manner And right margin Restrained boundary, then calculate MP with following formula _sThe border.

$B\left({\mathrm{MP}}_o\right)=\underset{\mathrm{MP}\∈\mathrm{\Ω}\left(M_{\mathrm{smp}}\right)}{\arg \min }\left(E(B\left(\mathrm{MP}\right),\hat{B}\left(\mathrm{MP}\right))\right)---\left(5\right)$

Ω (M wherein _smp) be all M _smpIn the set that consists of of gridblock, arg min represents to make expression formula to reach the parameter value of minimum value.In theory, M _smpThe gridblock that comprises is infinitely many.We limit any two gridblocks at M in experiment _smpIn alternate position spike be the integral multiple of a constant, boundary length is also done this constraint, like this Ω (M _smp) be exactly a finite set.Need to prove in addition: M _smpWith the texture grid of sample texture be incoordinate, unless do not plan to change the semantic feature of target texture as the user, just at this moment both are equal to.

In most cases, MP _oA seamless gridblock, need to by correct it vertex position obtain MP _sThe vertex set of note arbitrary mess piece MP is V (MP), and the summit that wherein is positioned at outside, MP border consists of set V _out(MP), with V _out(MP) in, summit adjacent and that be positioned at inside, border, any one summit consists of set V _in(MP).For V _out(MP) and V _in(MP) any summit in, its new coordinate equal old coordinate add corresponding vertex coordinate in restrained boundary and 1/2 (Fig. 4).Other summits consist of set V _neu(MP), correct according to the following formula:

$d\left(v^{\mathrm{neu}}\right)=\underset{k=1}{\overset{N_{\mathrm{in}}}{\mathrm{\Σ}}}{\mathrm{\ω}}_{k}d\left(v_k^{\mathrm{in}}\right)/\underset{k=1}{\overset{N_{\mathrm{in}}}{\mathrm{\Σ}}}{\mathrm{\ω}}_k,v^{\mathrm{neu}}\∈V_{\mathrm{neu}}\left(\mathrm{MP}\right)---\left(6\right)$

Wherein, N _inV _in(MP) number of vertex in,

V _in(MP) k summit in, v ^neuV _neu(MP) any summit in.

d(v ^neu) be respectively

, v ^neuThe coordinate offset amount.ω _kThe expression weights,

So d (v ^neu) be exactly V _in(MP) weighted mean value of all apex coordinate side-play amounts in.Now, we just can use MP _sInitialization target texture grid.

1.2) repeat to splice obviously with a seamless gridblock and can cause too high multiplicity, impact effect is so what next will do is exactly to reduce target texture grid M _tgtMultiplicity, be in other words grid optimization.Our method is an iterative process, at every turn from M _tgtIn selected gridblock, then from M _smpIn look for a boundary length new piece consistent with it to replace it.In the time of selected to be replaced, determined that in fact four retrain the border, then just can utilize formula (5) to obtain the best gridblock of replacing and (be designated as MP _r).At last, with aforementioned summit antidote to MP _rBe spliced in target gridding after correcting.

At M _tgtMiddle selection gridblock to be replaced, method of the present invention are that target gridding is divided into the unified rectangular block of size, to every from M _smpK candidate's replace block of middle search also therefrom selected a replace block that conduct is final at random.Grid synthetic method effect of the present invention as shown in Figure 5.

Second step: calculated characteristics vector and synthetic target texture under the grid constraint

Neighborhood of pixels in the neighborhood matching algorithm is a high dimension vector, has comprised r, g, the b component of all pixels in neighborhood.If the size of neighborhood of pixels is 5*5, its dimension is exactly 75 so.In prior synthesizing method, the neighborhood of each pixel is to construct according to vertical direction and horizontal direction.Yet synthetic for the semantic constraint texture, because the architectural feature of target texture is different from the architectural feature of sample texture, can not simply adopt above-mentioned neighborhood make.For example, for the fragment of brick sample texture of rule, lines in its characteristic pattern or be vertical, or be level.Do now this restriction: the user wishes that the primitive shapes in target texture to be synthesized is hexagonal.Certainly exist oblique line in the characteristic pattern of target texture at this moment.When adopting the synthetic target texture of neighborhood matching algorithm, if still adopt traditional neighborhood make, know that easily its neighborhood can not find the neighborhood of pixels of special coupling in master drawing for the pixel near the hexagon hypotenuse.In order to address this problem, we ask a direction to sample texture and target texture for each pixel, are called eigenvector.Near the pixel of hypotenuse, its direction is substantially vertical with hypotenuse, and the neighborhood of constructing according to this direction just can find the neighborhood of pixels of coupling in master drawing; Before synthetic, calculate its eigenvector for each pixel; Take the fragment of brick sample texture as example, know that easily wherein pixel characteristic vector is all vertically or level basically.And have the texture of regular hexagonal pel, more than two kinds of the eigenvectors of its pixel, but be also several discrete values.

We utilize characteristic distance generating feature vector by the following method in experiment.At first, in the image of white background, draw out texture grid with black.This image is carried out filtering with Gaussian filter (the Gaussian filter radius in experiment is 5.0).At this moment, the r of the pixel color in this image (or g, b, because r=g=b) component value (span 0.0～1.0) is exactly the characteristic distance of pixel in corresponding texture.The eigenvector of each pixel be it to the weighted mean value of the direction of all neighbor pixels (being designated as p), weights are calculated as follows:

w(p)＝1/((f _p+e)d _p) (7)

Wherein, f _pIt is the characteristic distance of p.d _pBe p to the distance of the centre of neighbourhood, e is one and is used for avoiding the decimal except 0, as 0.001.When the structure neighborhood of pixels, the vertical direction in the corresponding classic method of eigenvector.

Building-up process (also claiming the rasterizing process) under the grid constraint is: the characteristic distance of all pixels in first calculating sample texture and target texture according to texture grid, and the pixel in target texture is pressed the characteristic distance ascending sort.Then according to this order, each pixel basis eigenvector is constructed its neighborhood.Search for again the optimum matching pixel from master drawing.Compare method better effects if of the present invention (Fig. 7) with the sub-texture grid method of Dischler etc.

The inventive method is applicable to regular veins, approximate rule texture, and irregular grain.The sample texture that we select in experiment has been included this three class.The method effect as shown in Figure 8.Experimental situation is the PC of Inter Core (TM) i5 2.8GHz CPU, 4G internal memory, operation Windows 7 operating systems.Sample texture resolution is that 128 * 128, figure elemental size surpasses 20 * 20.The texture grid generated time of target texture is approximately 15 seconds, and to different sample grid, the generated time amplitude of variation is within 5 seconds.Semantic constraint based on grid is synthetic, about 2 minutes consuming time.

Claims (1)

1. semantic constraint texture synthesis method based on geometric space comprises following two steps:

(1) utilize image processing techniques to extract from sample texture or by the given sample texture grid of user, then synthesize the target texture grid based on the gridblock splicing;

(2) generate the eigenvector of sample texture and target texture according to sample texture grid and target texture grid, and adopt based on the synthetic target texture of the neighborhood matching algorithm of eigenvector;

Step (1) is specific as follows:

Synthesizing in geometric space of 2 d texture grid carried out, and the target texture grid is spliced mutually by several onesize gridblocks; For any two unidimensional gridblock p, q, if the number on the limit in the grid of being cut apart by the right margin of p equals the limit number cut apart by the left margin of q, claim so the right margin of p and the left margin of q to be complementary; The coupling cost function take by two groups of limits of these two boundary segmentation as input, to every limit of first group and its corresponding limit in second group, calculate their distance, then with all these limits between obtain an arithmetic number apart from addition, be called the coupling cost of the left margin of the right margin of p and q; For p, may there be a plurality of gridblocks, their left margin all is complementary with the right margin of p, and that gridblock that mates so Least-cost is best; Then pass through following steps, obtain the target texture grid: at first generate one group of gridblock that mate mutually on the border according to gridblock Boundary Match cost function and the given fritter grid of user, described fritter grid also is called the semantic constraint condition, and this fritter grid also can extract from texture; Concrete generative process is to select several gridblocks from the fritter grid, and mate mutually on the border of these gridblocks; To each matching scheme, go out in this matching scheme coupling cost between all couplings borders with the Boundary Match cost function calculation, and add up and obtain total coupling cost of this matching scheme; The matching scheme of total coupling Least-cost is best; Then the gridblock of optimum matching scheme is corrected vertex position, splice mutually afterwards and carry out grid optimization, just obtained the target texture grid;

Step (2) is specific as follows:

Employing is based on each pixel of the synthetic target texture of neighborhood matching algorithm of eigenvector, and the neighborhood of a pixel is a square area centered by this pixel; Neighborhood is equivalent to a high dimension vector, and building method is that the top left corner pixel from neighborhood begins to collect r, g, b component value by the sweep trace order by pixel; The distance of the neighborhood of neighborhood matching algorithm by contrasting all master drawing neighborhood of pixels and pixel to be synthesized is chosen the optimum matching pixel, and with the color of this pixel color as pixel to be synthesized; So undertaken by pixel, thus synthetic target texture, described neighborhood matching algorithm based on eigenvector is constructed neighborhood according to the eigenvector of pixel, and the eigenvector of described pixel is a direction, and this direction is substantially vertical with the hithermost primitive boundary of pixel.

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