CN101136067A - Man-made target detecting method based on synthetic feature coherence model - Google Patents

Abstract

The method comprises: creating a hybrid feature coherence model; using said model to detect the interested area in the nature image; using the corrosion and area-labeling in mathematical morphology to make post process for it.

Description

Man-made target detecting method based on synthetic feature coherence model

Technical field

The invention belongs to Flame Image Process and mode identification technology, be specifically related to the interested artificial object detection method in a kind of natural image.

Background technology

Target detection all is widely used in military and civilian.In modern war, various advanced weapons will play a role must reliable automatic recognition system, and target knowledge detection is exactly a gordian technique wherein.In civil area, judgement as sick cell in the medical science, the forecast of risk of forest fire, recognition of face, fingerprint recognition, iris recognition during identity is differentiated, the different fine letter sorting of cloth Defect Detection in the cotton textiles industry and raw cotton, or the like, Target Signature Analysis and detection technique all are the gordian techniquies in the relevant industries.

At present, the method for target detection mainly contain method based on profile, the method cut apart based on morphology area and based on the method for texture.Be to extract the edge of target object based on the method key of profile, then target object split.Because the complicacy of background in the natural image utilizes rim detection to be difficult to extract the profile of target from background, therefore the method based on profile is difficult to obtain effect preferably.The method of cutting apart based on morphology area is commonly used the region growing method.Region growing need be chosen suitable seed points, according to certain growth rule the neighborhood territory pixel point is carried out feature then and differentiates.But in natural image, choosing automatically of seed points is a difficult problem always, and therefore the method for cutting apart based on morphology area is difficult to automatically detect man-made target.Mainly consider the local grain feature difference of man-made target and background based on the method for texture, utilize this difference to detect target then.But contain complicated natural scene in the natural background, its texture is widely different, neither be fine so the man-made target in the complicated natural background is detected effect.P.Kovesi has proposed a kind of characteristics of image detection method based on phase equalization, but this method is more paid attention to the image detail Feature Extraction.Therefore and natural background includes rich details information, for the extraction of target in the natural image, based on the method for phase equalization significant limitation is arranged.

Support fund of the present invention is project of national nature science fund project (No:60602036).

Summary of the invention

The objective of the invention is to above-mentioned deficiency, a kind of man-made target detecting method based on synthetic feature coherence model is provided at prior art.This method can the filtering complexity natural background, detect man-made target effectively.The present invention has set up synthetic feature coherence model, utilizes this model that natural image is carried out preliminary area-of-interest then and detects, and utilizes corrosion and area marking in the mathematical morphology that it is carried out aftertreatment at last.

Man-made target detecting method based on synthetic feature coherence model of the present invention comprises the following steps:

Step 1: obtain the natural image that a width of cloth contains man-made target and has gray scale, carry out Fourier transform, obtain the Fourier components of image;

Step 2: set up the logGabor bank of filters, and make product with the Fourier components of logGabor bank of filters and image, the convolution of image and even symmetry logGabor wave filter is

, the convolution of image and odd symmetry logGabor wave filter is

Step 3: the single characteristic set { F that asks for local message according to following formula (a, b, c, d) ₁F ₂F ₃F ₄, utilize the required comprehensive characteristics of single latent structure then: $F^{(r,\mathrm{\θ})}(x,y)=\[{F_1}^{(r,\mathrm{\θ})}(x,y)...,{F_L}^{(r,\mathrm{\θ})}(x,y),...,{F_4}^{(r,\mathrm{\θ})}(x,y)\]$

A. (F is exported in the normalization of even symmetry wave filter ₁): ${F_1}^{(r,\mathrm{\θ})}(x,y)=\frac{O_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)}{{\max }_{(x,y)}{\max }_r{O}_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)}$

B. (F is exported in the normalization of odd symmetry wave filter ₂): ${F_2}^{(r,\mathrm{\θ})}(x,y)=\frac{O_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)}{{\max }_{(x,y)}{\max }_r{O}_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)}$

C. symmetrical metrics (the F of wave filter output ₃): ${F_3}^{(r,\mathrm{\θ})}(x,y)=\frac{\left|O_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)\right|-\left|O_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)\right|}{{\max }_{(x,y)}{\max }_r\left(\right|O_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)|-|O_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)\left|\right)}$

D. the antisymmetry of wave filter output is measured (F ₄): ${F_4}^{(r,\mathrm{\θ})}(x,y)=\frac{\left|O_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)\right|-\left|O_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)\right|}{{\max }_{(x,y)}{\max }_r\left(|O_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)\right|-\left|O_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)\right|)}$

Step 4: set up synthetic feature coherence model: ${\mathrm{IFC}}_F(x,y)=\frac{1}{Z}\underset{\mathrm{\θ}}{\mathrm{\Σ}}\{\underset{r}{\mathrm{\Σ}}\mathrm{\ρ}\[F^{(r,\mathrm{\θ})}(x,y),\stackrel{\‾}{F^{\mathrm{\θ}}(x,y)}\]-T\}$

In the formula, $\stackrel{\‾}{F^{\mathrm{\θ}(x,y)}}=\underset{r}{\mathrm{\Σ}}{F}^{(r,\mathrm{\θ})}(x,y),$

Related coefficient ρ is defined as ρ (F ¹, F ²)=| F ¹| * | F ²| cos (F ¹, F ²), wherein, cos (F ¹, F ²) be F ¹With F ²Between the cosine of angle,

$Z=\underset{\mathrm{\θ}}{\mathrm{\Σ}}\underset{r}{\mathrm{\Σ}}\left|F^{(r,\mathrm{\θ})}(x,y)\right|\×\left|\stackrel{\‾}{F^{\mathrm{\θ}}(x,y)}\right|+\mathrm{\ϵ}$ Wherein, ε is little positive constant,

T is an estimating noise;

Step 5: setting threshold, and utilization is carried out binary conversion treatment through the synthetic feature coherence model of noise compensation correction to image;

Step 6: the interference that frontier point in the preliminary removal of images of employing mathematical morphology corroding method and background cause;

Step 7: utilize the zone marker algorithm to mark each connected region respectively, calculate the number of pixels of each connected region, and, detect the zone at man-made target place by setting threshold.

The threshold value span of carrying out binary conversion treatment in the step 4 of above-mentioned man-made target detecting method is preferably in (0.1,0.3) interval.

The logGabor wave filter tolerance mechanism that the present invention adopts is consistent with human visual system's mechanism of measuring, and therefore the improved synthetic feature coherence model of setting up also has human visual system.This model has also overcome the problem that phase equalization too stresses image detail simultaneously.The aftertreatment that the present invention taked, further the complex background of filtering natural image detects man-made target effectively.This method is compared with existing object detection method, on the performance of the natural background of filtering complexity large increase has been arranged, for follow-up Target Recognition and crucial again meaning in location and use value.

Description of drawings

Fig. 1: the general flow chart of artificial object detection method of the present invention;

Fig. 2: original natural image;

Fig. 3: the image information of utilizing phase equalization to detect;

Fig. 4: the image information of utilizing synthetic feature coherence model of the present invention to detect;

Fig. 5: the image information of utilizing synthetic feature coherence model detection of the present invention and process aftertreatment;

Embodiment

The general flow chart of artificial object detection method of the present invention as shown in Figure 1, at first set up synthetic feature coherence model, utilize this model that natural image is carried out preliminary area-of-interest then and detect, utilize corrosion and area marking in the mathematical morphology that it is carried out aftertreatment at last.Below in conjunction with model construction and embodiment the present invention is further described.

1.logGabor the introduction of bank of filters and parameter setting

The logGabor wave filter is around certain centre frequency (r in spatial frequency domain ₀, θ ₀) a Gaussian function.In frequency field, the logGabor wave filter is made up of even symmetry logGabor wave filter and odd symmetry logGabor wave filter two parts.

$G_{(r,\mathrm{\θ})}=G_{\left(r\right)}{G}_{(\mathrm{\θ})}=\exp \{-\frac{{\[\log (r/r_0)\]}^2}{2{\[\log ({\mathrm{\σ}}_r/r_0)}^2}\}\exp \{-\frac{{(\mathrm{\θ}-{\mathrm{\θ}}_0)}^2}{2{\mathrm{\σ}}_{\mathrm{\θ}}}\}$

Wherein, θ ₀Be the deflection of wave filter, r ₀Be the central angle frequency, σ _θAnd σ _rBe respectively the angle direction standard deviation of Gaussian function and standard deviation radially.For two dimension that can overlay image plane and the local message that extracts image from different directions and yardstick frequently, the bank of filters that the present invention adopts the logGabor wave filter of different space frequency and direction to form is extracted feature.In image, local message can be expressed as the set of the single feature of extracting from some structure of image or characteristics.The bank of filters that the present invention selects for use has 4 different spatial frequencys, and 6 different directions are arranged on each spatial frequency.The ratio of the centre frequency of logGabor bank of filters is 2.

2. single Feature Extraction

Adopt artificial object detection method of the present invention, at first a width of cloth contains the natural image of man-made target, obtains the Fourier components of image.Image is a gray level image, and size of images is made as 2 ^NPixel (N=1,2 ...).

Utilize the logGabor bank of filters that image is carried out convolution then, obtain each wave filter G (r, output θ)

With

Extract following single feature thus:

(1) normalization of even symmetry wave filter output (F ₁): ${F_1}^{(r,\mathrm{\θ})}(x,y)=\frac{O_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)}{{\max }_{(x,y)}{\max }_r{O}_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)}$

(2) normalization of odd symmetry wave filter output (F ₂): ${F_2}^{(r,\mathrm{\θ})}(x,y)=\frac{O_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)}{{\max }_{(x,y)}{\max }_r{O}_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)}$

(3) symmetrical metrics (F of wave filter output ₃): ${F_3}^{(r,\mathrm{\θ})}(x,y)=\frac{\left|O_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)\right|-\left|O_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)\right|}{{\max }_{(x,y)}{\max }_r\left(\right|O_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)|-|O_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)\left|\right)}$

(4) the antisymmetry tolerance (F of wave filter output ₄): ${F_4}^{(r,\mathrm{\θ})}(x,y)=\frac{\left|O_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)\right|-\left|O_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)\right|}{{\max }_{(x,y)}{\max }_r\left(\right|O_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)|-|O_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)\left|\right)}$

Thus, can obtain required comprehensive characteristics.Comprehensive characteristics can be defined as a proper vector of being made up of single feature: $F^{(r,\mathrm{\θ})}(x,y)=\[F_1^{(r,\mathrm{\θ})}(x,y)...,F_L^{(r,\mathrm{\θ})}(x,y),...,F_4^{(r,\mathrm{\θ})}(x,y)\],$ Here F _L ^{(r, θ)}(x y) is single feature.L is the number of single feature, and 1＜L＜4.

3. the foundation of synthetic feature coherence model and noise compensation

According to the comprehensive characteristics of obtaining above, it is as follows to set up synthetic feature coherence model

${\mathrm{IFC}}_F(x,y)=\frac{1}{Z}\underset{\mathrm{\θ}}{\mathrm{\Σ}}\underset{r}{\mathrm{\Σ}}\mathrm{\ρ}\[F^{(r,\mathrm{\θ})}(x,y),\stackrel{\‾}{F^{\mathrm{\θ}}(x,y)}\]$

In the formula, $\stackrel{\‾}{F^{\mathrm{\θ}}(x,y)}=\underset{r}{\mathrm{\Σ}}{F}^{(r,\mathrm{\θ})}(x,y);$

Related coefficient ρ is defined as ρ (F ¹, F ²)=| F ¹| * | F ²| cos (F ¹, F ²), wherein, cos (F ¹, F ²) be F ¹With F ²Between the cosine of angle;

$Z=\underset{\mathrm{\θ}}{\mathrm{\Σ}}\underset{r}{\mathrm{\Σ}}\left|F^{(r,\mathrm{\θ})}(x,y)\right|\×\left|\stackrel{\‾}{F^{\mathrm{\θ}}(x,y)}\right|+\mathrm{\ϵ}$ Wherein, ε is little positive constant, prevents that denominator from being O.

Utilize noise compensation that it is revised then, draw improved synthetic feature coherence model:

${\mathrm{IFC}}_F(x,y)=\frac{1}{Z}\underset{\mathrm{\θ}}{\mathrm{\Σ}}\{\underset{r}{\mathrm{\Σ}}\mathrm{\ρ}\[F^{(r,\mathrm{\θ})}(x,y),\stackrel{\‾}{F^{\mathrm{\θ}}(x,y)}\]-T\}$ In the formula, T is an estimating noise

During target in the detection of complex background, noise processed is the wherein work of a key.Usually adopt wave filter to come filtering noise, this paper adopts the way of Noise Estimation to remove noise.Supposed before Noise Estimation: (1) noise has additive property; (2) going up noise level at whole signal (image) is constant; (3) feature only appears at signal (image) insular position place.Noise Estimation: $T=\underset{n=1}{\overset{S-1}{\mathrm{\Σ}}}T\′\left(n\right)\×T\′(n+1)$

Wherein, T ' (n)=A (n) * k, $A\left(n\right)=\exp \left\{\frac{\underset{i=1}{\overset{\dim 1}{\mathrm{\Σ}}}\underset{j=1}{\overset{\dim 2}{\mathrm{\Σ}}}\log \left(\right|F(n,i,j)|+\mathrm{\ϵ})}{\dim 1\×\dim 2}\right\};$ K is a noise factor, k=5 * (1-0.5 ^S); S is for adopting the scale parameter of logGabor bank of filters; Dim1, dim2 are respectively the length of image and wide; | F (s, i, j) | at yardstick be under the S, (i j) locates the mould of comprehensive characteristics vector to point.ε is a less value (getting 0.001 here).Before synthetic feature coherence normalization, all to cut estimating noise T for the energy of each direction.The synthetic feature coherence function that can be improved: ${\mathrm{IFC}}_F(x,y)=\frac{1}{Z}\underset{\mathrm{\θ}}{\mathrm{\Σ}}\{\underset{r}{\mathrm{\Σ}}\mathrm{\ρ}\[F^{(r,\mathrm{\θ})}(x,y),\stackrel{\‾}{F^{\mathrm{\θ}}(x,y)}\]-T\}.$

4. aftertreatment

As shown in Figure 4: the noise compensation of synthetic feature coherence model to a certain extent filtering interference of noise, but have the natural background of bigger variation still powerless for local gray level.Therefore, in the detected area-of-interest of synthetic feature coherence model, also contain inartificial target.For this reason, the area-of-interest of filtering natural background generation is the most important thing of aftertreatment work.

Testing result to synthetic feature coherence model in this patent is successively carried out processing such as binaryzation, corrosion and zone marker.Followingly make introduction respectively:

(1) binary conversion treatment

Because corrosion and the handled image of zone marker are two-values, therefore need carry out binaryzation to the result that synthetic feature coherence model is handled.In the result that synthetic feature coherence model detects, the value of each pixel is within 0 to 1 scope.We can set a threshold value Z, if the synthetic feature coherence model detected value IFC of certain pixel _F(x, y)〉Z, then the value of this pixel is made as 1; IFC _F(x, y)＜Z, then the value of this pixel is made as 0.Can obtain one 0 and 1 binary image thus.If the selected value of Z is too big, the connectedness of target area is damaged, be unfavorable for area marking; If the selected value of Z is too little, then can make the point in more backgrounds is detected.The span of suggestion Z is made as (0.1,0.3).

(2) corrosion treatment

After the testing result of synthetic feature coherence model carried out binaryzation, can tentatively eliminate background dot and obtain binary image.In order the area-of-interest in target area and the background separately to carry out corrosion treatment to the image after the binaryzation.Simultaneously, the further filtering frontier point of this method.

(3) zone marker

At first the corrosion treatment result is carried out zone marker, mark each connected region respectively.Calculate the number of pixels of each connected region then.Compare with the connected region in the background, the number of pixels of the connected region at man-made target place is more relatively.For this reason, can detect the zone at man-made target place by setting threshold.If the number of connected domain is during less than threshold value, then this connected domain is considered as the background area, and its pixel value is made as 0; Otherwise, then be considered as the target area.For multiobject image,,, then can miss other less relatively targets if the threshold value setting is excessive as Fig. 2 (b).Therefore, need be according to selecting appropriate threshold according to the body situation.

Below in conjunction with accompanying drawing technique effect of the present invention is described further.

Fig. 2 is original natural image, and the image size is 128 * 128.From natural image as can be seen: contain abundant natural information in the natural background, as the woods, meadow, land etc.; Also can be subjected to simultaneously the influence of illumination, as the ground of Fig. 2 (a).This all can have influence on the effect of target detection.

Fig. 3 is for utilizing phase equalization detected image result of information.As can be seen from the results, this method is more paid attention to the detail detection of image.Therefore the result who handles is unfavorable for target detection.

Fig. 4 is the result who utilizes improved synthetic feature coherence model to detect.Compare with phase equalization, this method has had inhibition preferably to the details composition of image, has reduced the information of background in the image.For aftertreatment provides condition.

Fig. 5 is aftertreatment figure as a result.

Claims (2)

1. the man-made target detecting method based on synthetic feature coherence model comprises the following steps:

Step 1: obtain the natural image that a width of cloth contains man-made target and has gray scale, carry out Fourier transform, obtain the Fourier components of image;

Step 2: set up the logGabor bank of filters, and make product with the Fourier components of logGabor bank of filters and image, the convolution of image and even symmetry logGabor wave filter is O _Even ^{(r, θ)}(x, y), the convolution of image and odd symmetry logGabor wave filter is O _Odd ^{(r, θ)}(x, y);

Step 3: the single characteristic set { F that asks for local message according to following formula (a, b, c, d) ₁F ₂F ₃F ₄, utilize the required comprehensive characteristics of single latent structure then: $F^{(r,\mathrm{\θ})}(x,y)=[F_1^{(r,\mathrm{\θ})}(x,y)...,F_L^{(r,\mathrm{\θ})}(x,y),...,F_4^{(r,\mathrm{\θ})}(x,y)]$

A. (F is exported in the normalization of even symmetry wave filter ₁): $F_1^{(r,\mathrm{\θ})}(x,y)=\frac{O_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)}{{\max }_{(x,y)}{\max }_r{O}_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)}$

B. (F is exported in the normalization of odd symmetry wave filter ₂): $F_2^{(r,\mathrm{\θ})}(x,y)=\frac{O_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)}{{\max }_{(x,y)}{\max }_r{O}_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)}$

C. symmetrical metrics (the F of wave filter output ₃): $F_3^{(r,\mathrm{\θ})}(x,y)=\frac{\left|O_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)\right|-\left|O_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)\right|}{{\max }_{(x,y)}{\max }_r\left(\right|O_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)|-|O_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)\left|\right)}$

D. the antisymmetry of wave filter output is measured (F ₄): $F_4^{(r,\mathrm{\θ})}(x,y)=\frac{\left|O_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)\right|-\left|O_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)\right|}{{\max }_{(x,y)}{\max }_r\left(\right|O_{\mathrm{odd}}^{(r,\mathrm{\θ})}(x,y)|-|O_{\mathrm{even}}^{(r,\mathrm{\θ})}(x,y)\left|\right)}$

Step 4: set up synthetic feature coherence model: ${\mathrm{IFC}}_F(x,y)=\frac{1}{Z}\underset{\mathrm{\θ}}{\mathrm{\Σ}}\{\underset{r}{\mathrm{\Σ}}\mathrm{\ρ}[F^{(r,\mathrm{\θ})}(x,y),\stackrel{\‾}{F^{\mathrm{\θ}}(x,y)}]-T\}$

In the formula, $\stackrel{\‾}{F^{\mathrm{\θ}}(x,y)}=\underset{r}{\mathrm{\Σ}}{F}^{(r,\mathrm{\θ})}(x,y),$

Related coefficient ρ is defined as ρ (F ¹, F ²)=| F ¹| * | F ²| cos (F ¹, F ²), wherein, cos (F ¹, F ²) be F ¹With F ²Between the cosine of angle,

$Z=\underset{\mathrm{\θ}}{\mathrm{\Σ}}\underset{r}{\mathrm{\Σ}}\left|F^{(r,\mathrm{\θ})}(x,y)\right|\×\left|\stackrel{\‾}{F^{\mathrm{\θ}}(x,y)}\right|+\mathrm{\ϵ}$ Wherein, ε is little positive constant,

T is an estimating noise;

Step 5: setting threshold, and utilization is carried out binary conversion treatment through the synthetic feature coherence model of noise compensation correction to image;

Step 6: the interference that frontier point in the preliminary removal of images of employing mathematical morphology corroding method and background cause;

Step 7: utilize the zone marker algorithm to mark each connected region respectively, calculate the number of pixels of each connected region, and, detect the zone at man-made target place by setting threshold.

2. the man-made target detecting method based on synthetic feature coherence model according to claim 1 is characterized in that, the threshold value span of carrying out binary conversion treatment in the step 4 is made as (0.1,0.3).