CN103810729A - Raster image vectorizing method based on contour line - Google Patents

Abstract

The invention discloses a raster image vectorizing method based on a contour line. The raster image vectorizing method based on the contour line includes: generating an intensity image corresponding to a raster image firstly, and then establishing an image height field by using the intensity image after being smoothed; extracting equal altitude points in the image height field, and confirming colors of the equal altitude points; establishing parameter curve math expression of the contour line based on the equal altitude points, and conforming the color of an arbitrary point on the contour line. A parameter curve set with color information is vectorization expression of the raster image. When a reestablishment raster image is expressed in vectorization mode, zooming and discretization operations are performed on a vectorized parameter curve according to the size of the raster image to be established, the position and the color of each pixel occupied by the vectorized parameter curve in the image to be established are confirmed, a coloring pixel is used as initial data, and a color diffusion method is used to reestablish the raster image which is expressed in vectorization mode. The raster image vectorizing method based on the contour line can perform multiple scale vectorization processing on various raster images, can use relative parameters to control the data volume of a vector diagram, and can reestablish the raster image multiple in resolution through vector expression.

Description

A kind of based on isocontour raster image vector quantized method

Technical field

The invention belongs to digital image processing field, relate to a kind of vectorized process technology of raster image, relate in particular to a kind of raster image vector quantized method of extracting based on level line.

Background technology

In digital image processing techniques field, there are two kinds of modes to describe piece image: grating represents mode and vector representation mode.Grating represents that mode is by the iamge description rectangular lattice that the discrete pixel of rule forms of serving as reasons, and wherein each pixel is being stored different shading value (brightness or color).Represent the raster image more data volume of needs conventionally that a width is larger, take larger storage space; In the time that raster image is carried out to convergent-divergent, often can produce zigzag at the boundary profile place of color block areas and lose shape.The image of vector representation carrys out presentation video with geometric graphic elements such as point, straight line or polygons.With respect to raster image, vector image needs less data volume conventionally, is also easier to editor and revises, and in the process of image scaling, there will not be sawtooth to lose shape and the phenomenon such as fuzzy distortion.Raster image and vector image are because feature is separately widely applied in different fields.Along with the development of network and the communication technology and the increasingly extensive application of various high performance of mobile equipments, memory space and the editability of people to digital picture had higher requirement.In order to adapt to the current demand in multimedia application, the scholars of image processing field have started coloured image to carry out the research work of vectorized process in recent years.The at present existing multiple raster image vector quantized method based on different technologies thought.

Raster image vector quantized technology based on Octree Color Quantization Algorithm (referring to: Geng Guohua etc. the color quantization algorithm based on octree structure. a small-sized microcomputer system, 1997,18 (1): 24-29).The method first reduces the number of colours of image with Octree Color Quantization Algorithm, then go out the region of each color composition with curve tracing.The deficiency of these class methods is to only have image volume is changed into more color section, just can obtain reasonable vector quantization effect, and therefore the data volume of vector quantization file is larger.

Raster image vector quantized technology that one class is cut apart based on image (referring to: Lecot G, Levy B.ARDECO:Automatic region detection and conversion.In:17 ^theurographics Symposium on Rendering, 2006,1604-1616).This technology is by segmenting the image into some region units, then goes out the border of each zonule with curve tracing, determines the gradual change type Fill Color of the each zonule tracking out simultaneously.The deficiency of these class methods is, the image of apparent color details complexity preferably.

A kind of raster image vector quantized technology based on gradient grid (referring to: Sun J, Liang L.Image vectorization using optimized gradient meshes.ACM Transactions on Graphics, 2007,26 (3): 1-7).Such technology as element figure, is divided into some gradient grids figure with gradient grid, goes out the color between grid by interpolation calculation.This technology can Precise Representation gradation of hue image, but due to the limitation of quadrilateral mesh itself, be relatively difficult to the image that vector quantization represents some topological structure complexity.

A kind of raster image vector quantized technology based on diffusion profile (referring to: Orzan A.Diffusion curves:a vector representation for smooth-shaded images.ACM Transactions on Graphics, 2008,27 (3): 1-8).The geometric primitive of this technology using diffusion profile as vector image, by the marginal information with Bézier curve fitted figure picture, and the color value information at reference mark, definite given curve both sides and fuzzy value Information generation vector image, finally adopt Poisson diffusion couple vector image to carry out rasterisation reconstruction.The main application of the method is for setting up cartoon image, can represent the image that color detail is comparatively complicated, but calculated amount is larger.

Summary of the invention

The present invention is directed to the deficiencies in the prior art, open a kind of based on isocontour raster image vector quantized method, by the level line in the gray-scale map height field of extraction raster image, and adopt color diffusion technique etc. to realize the vector quantization of raster image, can make up the large problem of calculated amount in existing vector technology, the image that its rasterisation is rebuild can approach original image more; Meanwhile, the inventive method is also supported in the multiresolution processing of vector quantization process.

The present invention is by the following technical solutions:

A kind of based on isocontour raster image vector quantized method, the steps include:

(1) raster image is carried out to gray processing pre-service: order is source images I by the raster image of vector quantization ₁, by source images I ₁transform into gray-scale map I ₂if, that is: source images I ₁itself is gray level image, makes I ₂=I ₁; If source images I ₁for coloured image,, by coloured image is carried out to gray processing processing, obtain gray-scale map I ₂;

(2) gray-scale map is carried out to filter preprocessing: use the gray-scale map I of digital picture smothing filtering operator to raster image ₂carry out smoothing processing, filter gray-scale map I ₂in noise signal, the color change in softening image, obtains the gray-scale map I after denoising ₃;

(3) extract vector quantization sampled point: by gray-scale map I ₃the gray-scale value of middle pixel, as height value, is set up the height field of image; Utilize the contour value parameter of series of input to extract the contour point in gray-scale map height field, be called sampled point, and by determining color value corresponding to each contour point, obtain the sampled point with colouring information;

(4) parameter curve of sampled point: adopt parameter curve method to set up each isocontour parametric line C by sampled point ₁; And determine parametric line C by the colouring information of each sampled point ₁the functional relation of upper difference color, thus obtain adding the parametric line C of colouring information ₂; By each curve C ₂the curve set forming, the vector quantization that is former raster image represents;

(5) rasterisation that vector quantization represents: according to the resolution of setting, each parametric line C during vector quantization is represented ₂carry out proportional convergent-divergent, obtain the parametric line C that the vector quantization consistent with set resolution represents ₃, and synchronously to parametric line C ₃on color corresponding relation adjust; By parametric line C ₃in the enterprising line raster discrete processes of image of new settings resolution, determine each location of pixels that it is occupied in image yet to be built; Then according to parametric line C ₃go up additional colouring information, calculate the color value of above-mentioned pixel, above-mentioned pixel is included into image original pixels point S set yet to be built;

(6) color diffusion: the raw information using original pixels point S set as image yet to be built, using pixel wherein as color diffuse source; By simulating the processes such as heat conduction, calculated the color value of undefined pixel in raster image yet to be built by color diffuse source, the rasterisation that reconstructs vector image represents.

The present invention, on the basis of contour lines creation technology, proposes a kind of raster image vector quantized method.The method, by preprocessing means such as image gray processing, digital picture filtering, according to distinctive regular grid contour lines creation algorithm, has comparatively accurately been extracted the level line of image as outline line, has realized the vector quantization of raster image; Simultaneously also can be by level line is carried out to rasterization process, adopt color to spread the reconstruction of raster image.The principle of the invention is simple, be easy to control, computing velocity is quick, can be used for various raster images to carry out multiple dimensioned vectorized process, and can utilize level line extracting parameter to control the data volume of vector image, can be reconstructed the raster image of different resolution by vector image.

Accompanying drawing explanation

Fig. 1: the process flow diagram of the inventive method.

Fig. 2: gray-scale map example.

Fig. 3: the height field situation being generated by image.

Fig. 4: the digital terrain elevation model representing by regular rectangular lattice.

Fig. 5: (a) (b) (c) (d) for level line four kinds of trends during by adjacent graticule mesh limit.

Fig. 6: the level line bunch example of two kinds of different resolutions that extracted by image in accompanying drawing 2, (a) be low resolution level line bunch, be (b) high resolving power level line bunch.

Fig. 7: the actual example that raster image level line extracts; Wherein (a) is original image, and (b) (c) is (d) that level line number is respectively n=3, n=5, the level line of the different scale extracting when n=10.

Fig. 8: the data-switching situation in raster image vector quantized process; Wherein (a) is original image, is (b) isocontour geometric representation, is (c) vector representation of image painted (gray scale).

Fig. 9: the raster image that is represented the different scale reconstructing by vector quantization; Wherein (a) (b) (c) be respectively that level line number is n=3, n=5, when n=10, the raster image of the different scale reconstructing.

Embodiment

The present invention is elaborated with embodiment below in conjunction with accompanying drawing.

As shown in Figure 1, the raster image vector quantized method of the present invention is total to two parts by vector quantization stage and reconstruction stage and forms, and relevant concrete implementation step is as follows:

According to the raster image I of user's input ₁and vector quantization control parameter (Gaussian Blur coefficient δ, level line are counted n etc.), the vector image of structure raster image.

1. pair raster image carries out gray processing pre-service: in the present embodiment, if I ₁for gray level image, gray-scale map I ₂=I ₁, gray processing work completes; If I ₁for coloured image, adopt method of weighted mean to convert the RGB information of chromatic grating image slices vegetarian refreshments to half-tone information, obtain corresponding gray-scale map I ₂, specific practice is: establish chromatic grating image I ₁in the RBG color component of any pixel p (x, y) be respectively r, g, b, is weighted to them that on average to obtain its gray-scale value be gray=α r+ β g+ γ b, wherein parameter alpha, beta, gamma (alpha+beta+γ=1) carries out value according to the importance of institute's corresponding color component etc.Because human eye is the highest to green susceptibility, redness is taken second place, and blue minimum, the weighted mean formula that the present embodiment adopts is: gray=0.30r+0.59g+0.11b.Obviously, gradation of image value in interval [0,1].Fig. 2 is the example of a gray-scale map.

2. pair gray-scale map carries out smothing filtering pre-service: the present embodiment adopts Gaussian filter to gray-scale map I ₂carry out smoothing processing, and regulate the degree of wave filter to image smoothing by the support width parameter δ of Gaussian function.In the process realizing, first by one dimension Gaussian function:

$K=\frac{1}{\sqrt{2\mathrm{\π\δ}}}{e}^{-\frac{i*i}{2\mathrm{\δ}*\mathrm{\δ}}}---\left(1\right)$

Wherein i is blur radius, calculates one dimension gaussian filtering core, and it is normalized to the coefficient T that obtains one dimension gaussian filtering by the template and the width parameter δ that set; Then coefficient T is first and gray-scale map I ₂in in the x-direction the gray-scale value of pixel be weighted on average, obtain " temporarily " gray-scale map, transposition " temporarily " gray-scale map, obtains gray-scale map I' ₂.Recycling coefficient T and gray-scale map I' ₂in in the y-direction the gray-scale value of pixel be weighted on average; Image transposition is gone back to original position, obtain the gray-scale map I that filtering completes ₃.

3. extract vector quantization sampled point: the present embodiment is the vector quantization sampled point as raster image by the contour point in extraction gray-scale map height field.First by the gray-scale map I obtaining through pre-service ₃gray-scale value as height value, the height field of synthetic image (referring to accompanying drawing 3); Then by gray-scale map I ₃in pixel p (x, y) as regular grid node, construct the digital terrain elevation model represented by regular rectangular lattice (referring to accompanying drawing 4).If the minimum value of elevation and maximal value are respectively h in picture altitude field _minand h _max, counting n according to the level line of setting, the vertical separation value Δ h that can obtain between adjacent contour is:

$\mathrm{\Δh}=\frac{h_{\max }-h_{\min }}{n-1}---\left(2\right)$

At this moment, get height value h _i:

H _i=h _min+ i × Δ h, (i=0,1,2 ..., n) (3) are as each the level line C that is about to extract _iheight value.Be h extracting height value in gray-scale map _ithe process of contour point in, first all Grid Edges in regular grid are all labeled as to " untreated "; Finding height value is h again _ilevel line C _ion Seed Points v _k, concrete grammar is: in scanning rule grid, be labeled as successively the Grid Edge e of " untreated ", and the limit e of mark scannng is " processed ".Relatively level line C _iheight value h _itwo summits with limit e

with ) height value

with

relation, judge level line C _iwhether crossing with limit e:

If $\left(h_k^1\text{-}{h}_i\right)(h_k^2-h_i)>0,$ C _inot crossing with limit e;

If (

c _ie is crossing with limit, and relevant intersection point v (x, y) can calculate by following formula:

$\left\{\begin{array}{c}x=x_k^1+\frac{h_i-h_{\mathrm{<figure-callout\; id="1"\; label="k\; i\; h"\; filenames="HDA0000467691500000023.png"\; state="\{\{state\}\}">k</figure-callout>}}^i}{h_{}^{}}(x_k^2-x_k^1)\\ y=y_k^1+\frac{h_i-h_k^1}{h_k^2-h_k^1}(y_k^2-y_k^1)\end{array}\right.---\left(4\right)$

And v (x, y) is deposited in dique L.

Then take v (x, y) as Seed Points, follow the trail of and find current level line C by limit e _ithe follow-up point of upper v (x, y), follow the trail of direction as shown in Figure 5: for rectangle rule grid, because sampled point all picks up from Grid Edge, level line C _iby the trend of adjacent mesh unit have four kinds may: from top to bottom, from left and right, from bottom to top, from right and left.According to level line C _itrend, judge in the grid at e place, limit which bar and level line C in other three Grid Edges _iintersect, calculate corresponding intersection point, and deposited in queue L, the Grid Edge that mark access is crossed is simultaneously " processed ".Using above-mentioned intersection point as new Seed Points, recurrence is found C _ion follow-up point, until C _iupper all contour points have been searched for.If search the Grid Edge that is labeled as " processed ", or search follow-up point and be positioned on the border of regular grid, finish recursive search process.Be labeled as the Grid Edge of " processed " if search, current level line C _ifor closed curve, C _ion all contour points searched for; If the follow-up point searching is positioned on regular grid border, look current level line C _ifor open curve, at this moment again from initial Seed Points v (x, y), continue search contour point along current isocontour opposite direction, and the contour point searching is inserted into the other end of queue L.Recursive search repeatedly, is positioned on regular grid border until search follow-up point.At this moment, C _iupper all contour points have been searched for.

Next, continue to be labeled as in scanning rule grid the Grid Edge of " untreated ", searching height value is h _icontour point, until searched for all Grid Edges.If there is new contour point found, illustrate that having a more than height value is h _ilevel line, adopt determine its contour point to similar process above; Repeatedly like this, until height value is h _ilevel line be search out.Each queue L only stores a contour point on level line, is h if there are many contour values _ilevel line, respectively they are deposited in different queue L.The level line bunch that accompanying drawing 6 is two kinds of different resolutions being extracted by accompanying drawing 4.

By initial raster image I ₁in pixel corresponding one by one with regular grid node, and the color value of getting regular grid node is source images I ₁the color value of middle respective pixel point.Making height value is h _iarbitrary contour point v _kcorresponding color value is v _k(r _k, g _k, b _k), its value can be by the color value on two summits on the Grid Edge at vk place with

and height value

with interpolation obtains, and computing method are:

$s_k=s_k^1+\frac{h_i-h_k^1}{h_k^2-h_k^1}(s_k^2-s_k^1),s=r,g,b---\left(5\right)$

The contour point that has added colouring information is raster image I ₁vector quantization sampled point.

The actual example that accompanying drawing 7 extracts for raster image level line.Wherein Fig. 7 (a) is original grating image, and Fig. 7 (b) (c) (d) is respectively that level line is counted n=3, n=5, the level line of the different scale extracting when n=10.

4. the parameter curve of sampled point: establish a wherein isocontour sampled point and classify l as _i(i=1,2 ..., m), first according to sampling point range l _ithe control vertex p of reverse B-spline Curve _i(i=0,1 ..., m, m+1).By the character of B-spline Curve, there is p _i-1+ 4p _i+ p _i+1=6l _i(i=1,2 ..., m), wherein total m equation, m+2 unknown number, supplements suitable boundary condition, increases by two equations, and above-mentioned system of equations can be separated.

In the present embodiment, for open curve, given is the border of free end points, i.e. first reference mark p of curve ₀with second reference mark p ₁overlap, m+1 reference mark p _m+1with m reference mark p _moverlap, even p ₀=p ₁, p _m+1=p _m, can obtain the system of equations of matrix form

$\left[\begin{array}{ccccccc}5& 1& 0& ...& 0& 0& 0\\ 1& 4& 1& ...& 0& 0& 0\\ ...& ...& ...& ...& ...& ...& ...\\ 0& 0& 0& ...& 1& 4& 1\\ 0& 0& 0& ...& 0& 1& 5\end{array}\right]\left[\begin{array}{c}{p}_1\\ p_2\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ p_{m-1}\\ p_m\end{array}\right]=6\left[\begin{array}{c}{l}_1\\ l_2\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ l_{m-1}\\ l_m\end{array}\right]---\left(6\right)$

Adopt chasing method to solve the control vertex p that obtains cubic B-spline _i;

For closed curve, its boundary condition is p ₀=p _m, p _m+1=p ₁, can obtain the system of equations of matrix representation

$\left[\begin{array}{ccccccc}4& 1& 0& ...& 0& 0& 1\\ 1& 4& 1& ...& 0& 0& 0\\ ...& ...& ...& ...& ...& ...& ...\\ 0& 0& 0& ...& 1& 4& 1\\ 1& 0& 0& ...& 0& 1& 4\end{array}\right]\left[\begin{array}{c}{p}_1\\ p_2\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ p_{m-1}\\ p_m\end{array}\right]=6\left[\begin{array}{c}{l}_1\\ l_2\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ l_{m-1}\\ l_m\end{array}\right]---\left(7\right)$

Adopt square-root method to solve the control vertex p that obtains cubic B-spline _i.

According to control vertex p _i, can obtain B-spline curve curve:

$L_i\left(t\right)=\frac{1}{6}\left[\begin{array}{cccc}1& t& t^2& t^3\end{array}\right]\left[\begin{array}{cccc}1& 4& 1& 0\\ -3& 0& 3& 0\\ 3& -6& 3& 0\\ -1& 3& -3& 1\end{array}\right]\left[\begin{array}{c}{p}_{i-3}\\ p_{i-2}\\ p_{i-1}\\ p_i\end{array}\right]t\&Element;\begin{array}{c}\left[\begin{array}{c}\mathrm{0,1}\end{array}\right]\end{array};i=\mathrm{3,4},......,m+1---\left(8\right)$

By reference mark p _iit is end to end and through oversampled points row l that interpolation goes out m bar _ib-spline curves section, i.e. the Parametric Representation C of high line ₁.

According to the colouring information of each sampled point, interpolation goes out level line C ₁the color value of upper difference, has obtained adding the parametric line C of colouring information ₂.Each C ₂set, the vector quantization that is former raster image represents.

The example that accompanying drawing 8 is raster image vector quantized process.Wherein Fig. 8 (a) is original grating image; Fig. 8 (b) is that the vector quantization of image represents, in figure, curve is represented by parametric line; Fig. 8 (c) is the vector representation of painted (gray scale).

5. the rasterisation of parametric line: according to the resolution of setting, by the reference mark p of proportional convergent-divergent B-spline curves _i, to parametric line bunch C ₂carry out convergent-divergent processing, make it to adapt with the size of image yet to be built, the C after convergent-divergent ₂for the vector quantization of image yet to be built represents, note be C ₃.The present embodiment adopts the rasterization algorithm of the algebraically B-spline curves based on regularity conditions, to parametric line C ₃in the enterprising line raster discrete processes of image yet to be built of new settings resolution, determine its each location of pixels occupied in image yet to be built (specific practice is referring to Huang Jinji. the real-time rasterisation of algebraic curve. Zhejiang: Zhejiang University, 2012:40-51); Then the colouring information additional according to parametric line, calculates the color value c (x, y) of above-mentioned pixel, and above-mentioned pixel is included into image original pixels point S set yet to be built.

6. color diffusion: the present embodiment is first according to image original pixels point S set yet to be built, pixel in raster image I yet to be built is labeled as respectively to " colored spots " and " non-staining point ", the pixel that is wherein included into S set is labeled as " colored spots ", and rest of pixels is classified as " non-staining point ".

Scan successively the pixel in raster image I yet to be built, if " colored spots ", its color value i (x, y) keeps initial value constant in the process of iteration, i.e. i (x, y)=c (x, y); " if non-staining point ", its color value i (x, y), in the process of iteration, carries out color diffusion and obtains by constantly solving Laplace equation.The color diffusion of the present embodiment adopts the five points difference form of Laplace to represent:

Δi(x,y)=4i(x,y)-(i(x-1),y)+i(x+1,y)+i(x,y+1)+i(x,y-1))=0 （9）

Iterative process is by manual control, and in the time that the color value of image tends towards stability, iteration finishes, and the reconstruction of raster image completes.

The example that the raster image that accompanying drawing 9 is different scale is rebuild.Wherein Fig. 9 (a) (b) (c) be respectively that level line is counted n=3, n=5, the raster image of the different scale of rebuilding when n=10.

Claims (1)

1. based on an isocontour raster image vector quantized method, the steps include:

(1) raster image is carried out to gray processing pre-service: order is source images I by the raster image of vector quantization ₁, by source images I ₁transform into gray-scale map I ₂if, that is: source images I ₁itself is gray level image, makes I ₂=I ₁; If source images I ₁for coloured image,, by coloured image is carried out to gray processing processing, obtain gray-scale map I ₂;

(2) gray-scale map is carried out to filter preprocessing: use the gray-scale map I of digital picture smothing filtering operator to raster image ₂carry out smoothing processing, filter gray-scale map I ₂in noise signal, the color change in softening image, obtains the gray-scale map I after denoising ₃;

(3) extract vector quantization sampled point: by gray-scale map I ₃the gray-scale value of middle pixel, as height value, is set up the height field of image; Utilize the contour value parameter of series of input to extract the contour point in gray-scale map height field, be called sampled point, and by determining color value corresponding to each contour point, obtain the sampled point with colouring information;

(4) parameter curve of sampled point: adopt parameter curve method to set up each isocontour parametric line C by sampled point ₁; And determine parametric line C by the colouring information of each sampled point ₁the functional relation of upper difference color, thus obtain adding the parametric line C of colouring information ₂; By each curve C ₂the curve set forming, the vector quantization that is former raster image represents;

(5) rasterisation that vector quantization represents: according to the resolution of setting, each parametric line C during vector quantization is represented ₂carry out proportional convergent-divergent, obtain the parametric line C that the vector quantization consistent with set resolution represents ₃, and synchronously to parametric line C ₃on color corresponding relation adjust; By parametric line C ₃in the enterprising line raster discrete processes of image of new settings resolution, determine each location of pixels that it is occupied in image yet to be built; Then according to parametric line C ₃go up additional colouring information, calculate the color value of above-mentioned pixel, above-mentioned pixel is included into image original pixels point S set yet to be built;

(6) color diffusion: the raw information using original pixels point S set as image yet to be built, using pixel wherein as color diffuse source; By simulating the processes such as heat conduction, calculated the color value of undefined pixel in raster image yet to be built by color diffuse source, the rasterisation that reconstructs vector image represents.

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