CN107767387A - Profile testing method based on the global modulation of changeable reception field yardstick - Google Patents

Profile testing method based on the global modulation of changeable reception field yardstick Download PDF

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CN107767387A
CN107767387A CN201711098829.XA CN201711098829A CN107767387A CN 107767387 A CN107767387 A CN 107767387A CN 201711098829 A CN201711098829 A CN 201711098829A CN 107767387 A CN107767387 A CN 107767387A
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msub
pixel
msup
value
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CN107767387B (en
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林川
李福章
张晴
潘亦坚
韦江华
潘勇才
覃溪
刘青正
张玉薇
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Guangxi University of Science and Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details

Abstract

The present invention is intended to provide a kind of profile testing method based on the global modulation of changeable reception field yardstick, comprises the following steps：A, image to be detected through gray proces is inputted, the normalization difference of Gaussian filter value of each pixel is calculated；B, high yardstick value, low scale-value, the threshold value of scaling function are preset, the normalization difference of Gaussian filter value of each pixel is contrasted with threshold value respectively, determines the scaling function value of each pixel；C, inhibition strength and respectively the multiple directions parameter of circumference are preset；Gabor filtering is carried out according to all directions parameter respectively to each pixel in image to be detected, calculates the classical receptive field stimuli responsive of each pixel；D, the inhibition response of each pixel is calculated；The profile that the classical receptive field stimuli responsive of each pixel and inhibition response are calculated to the pixel responds and handles to obtain the final profile value of each pixel E, and then obtains final profile figure.This method has the characteristics of simulated effect is good, outline identification rate is high.

Description

Profile testing method based on the global modulation of changeable reception field yardstick
Technical field
The present invention relates to Computer Image Processing field, and in particular to a kind of based on the global modulation of changeable reception field yardstick Profile testing method.
Background technology
Contour detecting is a basic task of computer vision field, changes institute different from being defined as strong brightness The edge of sign, profile generally represent a target to the border of other targets.Improve the basic skills of contour detecting performance just It is the information of amalgamation of global, in order to improve the performance of contour detecting model, what many researchers did the best goes to original detection calculation Son and suppression model are improved；Based on Scale-space theory, the size of the corresponding one group of neuron receptive field of each yardstick, god There is the characteristic under different scale through the different receptive field size of ganglion cell；Therefore, receptive field model yardstick is considered in a model Can be as the developing direction in the field.
The content of the invention
The present invention is intended to provide a kind of profile testing method based on the global modulation of changeable reception field yardstick, this method have The characteristics of simulated effect is good, outline identification rate is high.
Technical scheme is as follows：
A, image to be detected through gray proces is inputted, height is carried out using difference of Gaussian function to the gray value of each pixel This differential filtering, obtain the difference of Gaussian filter value of each pixel, respectively in the difference of Gaussian filter value of each pixel just Value and negative value are normalized, and obtain the normalization difference of Gaussian filter function of each pixel；Utilize returning for each pixel One, which changes gray value of the difference of Gaussian filter function respectively with corresponding pixel points, carries out convolution, obtains the normalization Gauss of each pixel Differential filtering value；
B, high yardstick value, the low scale-value of scaling function, predetermined threshold value, by the normalization difference of Gaussian of each pixel are preset Filter value is contrasted with threshold value respectively, if pixel normalization difference of Gaussian filter value is more than or equal to threshold value, the picture Scaling function value corresponding to vegetarian refreshments is high yardstick value, if pixel normalization difference of Gaussian filter value is less than threshold value, the picture Scaling function value corresponding to vegetarian refreshments is low scale-value；
C, inhibition strength is preset, presets the multiple directions parameter for dividing equally circumference；To each pixel in image to be detected point Gabor filtering is not carried out according to all directions parameter, obtains the response of all directions of each pixel, institute in wherein Gabor filtering The scaling function value used is each pixel identified high yardstick value or low scale-value in stepb；For each pixel, The maximum in the response of its all directions is chosen, the classical receptive field stimuli responsive as the pixel；
D, by the distance weighting function of the classical receptive field stimuli responsive of each pixel and the pixel carry out after convolution with Scale Multiplication corresponding to the pixel, obtain the inhibition response of each pixel；
E, the classical receptive field stimuli responsive of each pixel is subtracted into the inhibition response of the pixel and multiplying for inhibition strength Product, the profile response of the pixel is obtained, profile response is handled using non-maxima suppression and dual threshold, obtains each pixel Final profile value, and then obtain final profile figure.
Preferably, described step A is specially：
Described difference of Gaussian function DoGσ(x, y) is：
Wherein σ is initialization yardstick；
Described normalization difference of Gaussian filter function wσ(x, y) is：
Described normalization difference of Gaussian filter value woutσ(x, y) is：
woutσ(x, y)=I (x, y) * wσ(x,y) (3)。
Preferably, described step B is specially：
Described scaling function σ (x, y) is：
Wherein σHFor high yardstick value, σLFor low scale-value, σH=σL+ s, t be level off to 0 threshold value, s be yardstick step-length.
Preferably, described step C is specially：
The two-dimensional Gabor function expression of described Gabor filter group is as follows：
Whereinγ is one and represents oval receptive field major and minor axis ratio Constant, parameter lambda is wavelength, and σ (x, y) is scaling function, and 1/ λ is the spatial frequency of cosine function,It is phase angular dimensions, θ is The directioin parameter of Gabor filtering；
I (x, y) is the gray value of each pixel of image to be detected, and * is convolution operator；
Gabor energy values are calculated as follows：
Wherein θiFor a direction of Gabor filtering, NθFor the number in the direction of Gabor filtering；
Described classical receptive field stimuli responsive Ec (x, y；σ (x, y)) be：
Preferably, described step D is specially：
Described distance weighting function wσ(x, y) is：
Wherein,
Wherein, | | | |1For (L1) norm, H (x)=max (0, x), DoG (x, y) they are expression formula corresponding to DoG templates；
The inhibition response Inh (x, y) of each pixel is：
Inh (x, y)=Ec (x, y；σ(x,y))*wσ(x,y)σ(x,y) (11)。
Preferably, described step E is specially：
Described profile responds R (x, y)：
R (x, y)=H (Ec (x, y；σ(x,y))-αInh(x,y)) (12)；
Wherein H (x)=max (0, x), α are inhibition strength.
Differentiation selection of the present invention to high and low scale-value is beneficial to extraction profile based on high yardstick value, low scale-value is beneficial to suppression The principle of texture processed, therefore judged according to the normalized difference of Gaussian filter value of each pixel, if the filter value is noticeably greater than 0, then the pixel is likely on the position of profile, therefore profile is extracted from high yardstick value；If otherwise filtering For value near 0, then the pixel is likely on the position of texture, therefore texture is suppressed from low scale-value；Will Difference of Gaussian is normalized so that the weight of positive and negative filter value is consistent when being filtered, and reduces error recognition rate；Cause This, is profile or texture with reference to corresponding to above-mentioned filter value judges the pixel, so as to select corresponding high yardstick value or low Scale-value carries out the calculating of inhibition response, and the suppression of texture can be also taken into account while ensureing and not missing profile, has been avoided There is excessive useless texture, influence the effect of outline identification；Also, suppress to ring to calculate by combining distance weighting function Should, it can preferably reflect the rejection characteristic of non-classical receptive field, improve contour detecting rate.
In summary, profile testing method of the invention both maintained the integrality of profile and also greatly eliminate it is unnecessary Grain background, and more meet the wild spatial frequency characteristic of visual experience.
Brief description of the drawings
Fig. 1 is the contour detecting comparison diagram of the method for the embodiment of the present invention 1 and the method for document 1.
Embodiment
The present invention is illustrated with reference to the accompanying drawings and examples.
Embodiment 1
The profile testing method based on the global modulation of changeable reception field yardstick that the present embodiment provides, comprises the following steps：
A, image to be detected through gray proces is inputted, height is carried out using difference of Gaussian function to the gray value of each pixel This differential filtering, obtain the difference of Gaussian filter value of each pixel, respectively in the difference of Gaussian filter value of each pixel just Value and negative value are normalized, and obtain the normalization difference of Gaussian filter function of each pixel；Utilize returning for each pixel One, which changes gray value of the difference of Gaussian filter function respectively with corresponding pixel points, carries out convolution, obtains the normalization Gauss of each pixel Differential filtering value；
Described difference of Gaussian function DoGσ(x, y) is：
Wherein σ is initialization yardstick；
Described normalization difference of Gaussian filter function wσ(x, y) is：
Described normalization difference of Gaussian filter value woutσ(x, y) is：
woutσ(x, y)=I (x, y) * wσ(x,y) (3)；
B, high yardstick value, the low scale-value of scaling function, predetermined threshold value, by the normalization difference of Gaussian of each pixel are preset Filter value is contrasted with threshold value respectively, if pixel normalization difference of Gaussian filter value is more than or equal to threshold value, the picture Scaling function value corresponding to vegetarian refreshments is high yardstick value, if pixel normalization difference of Gaussian filter value is less than threshold value, the picture Scaling function value corresponding to vegetarian refreshments is low scale-value；
Described step B is specially：
Described scaling function σ (x, y) is：
Wherein σHFor high yardstick value, σLFor low scale-value, σH=σL+ s, t be level off to 0 threshold value, s be yardstick step-length；
C, inhibition strength is preset, presets the multiple directions parameter for dividing equally circumference；To each pixel in image to be detected point Gabor filtering is not carried out according to all directions parameter, obtains the response of all directions of each pixel, institute in wherein Gabor filtering The scaling function value used is each pixel identified high yardstick value or low scale-value in stepb；For each pixel, The maximum in the response of its all directions is chosen, the classical receptive field stimuli responsive as the pixel；
The two-dimensional Gabor function expression of described Gabor filter group is as follows：
Whereinγ is one and represents oval receptive field major and minor axis ratio Constant, parameter lambda is wavelength, and σ (x, y) is scaling function, and 1/ λ is the spatial frequency of cosine function,It is phase angular dimensions, θ is The directioin parameter of Gabor filtering；
I (x, y) is the gray value of each pixel of image to be detected, and * is convolution operator；
Gabor energy values are calculated as follows：
Wherein θiFor a direction of Gabor filtering, NθFor the number in the direction of Gabor filtering；
Described classical receptive field stimuli responsive Ec (x, y；σ (x, y)) be：
D, by the distance weighting function of the classical receptive field stimuli responsive of each pixel and the pixel carry out after convolution with Scale Multiplication corresponding to the pixel, obtain the inhibition response of each pixel；
Described step D is specially：
Described distance weighting function wσ(x, y) is：
Wherein,
Wherein, | | | |1For (L1) norm, H (x)=max (0, x), DoG (x, y) they are expression formula corresponding to DoG templates；
The inhibition response Inh (x, y) of each pixel is：
Inh (x, y)=Ec (x, y；σ(x,y))*wσ(x,y)σ(x,y) (11)；
E, the classical receptive field stimuli responsive of each pixel is subtracted into the inhibition response of the pixel and multiplying for inhibition strength Product, the profile response of the pixel is obtained, profile response is handled using non-maxima suppression and dual threshold, obtains each pixel Final profile value, and then obtain final profile figure；
Described step E is specially：
Described profile responds R (x, y)：
R (x, y)=H (Ec (x, y；σ(x,y))-αInh(x,y)) (12)；
Wherein H (x)=max (0, x), α are inhibition strength.
The contour detecting isotropic model for below providing the profile testing method of the present embodiment and document 1 carries out effective Property contrast, document 1 is as follows：
Document 1：Cosmin Grigorescu,Nicolai Petkov,and Michel A.Westenberg.Contour Detection Based on Nonclassical Receptive Field Inhibition[J].IEEE Transactions on image processing,vol.12,no.7,july 2003 729-739；
To ensure the validity of contrast, used and identical non-maxima suppression and dual threashold in document 1 for the present embodiment Value processing, wherein the two threshold value t includedh,tlIt is arranged to tl=0.5th, calculated by threshold value quantile p and obtained；
This Performance Evaluating Indexes F uses the following standard provided in document 2：
Document 2 is " D.R.Martin, C.C.Fowlkes, and J.Malik, " Learning to detect natural image boundaries using local brightness,color,and texture cues,"IEEE transactions on pattern analysis and machine intelligence,vol.26,pp.530-549, 2004.”；
P represents accuracy in formula, and R represents coverage rate, and evaluation metricses P values represent profile between [0,1], closer to 1 The effect of detection is better；
Choose 3 secondary classic map pictures shown in Fig. 1 and carry out Usefulness Pair ratio, method and reality in document 1 is respectively adopted Apply the method for example 1 and contour detecting is carried out to above-mentioned 3 width figure, the parameter group of the wherein method selection of embodiment 1 is as shown in table 1；
The parameter group table of 1 embodiment of table 1
Method in document 1 uses following 80 groups of parameters：α={ 1.0,1.2 },
σ={ 1.4,1.6,1.8,2.0,2.2,2.4,2.6,2.8 }, p={ 0.5,0.4,0.3,0.2,0.1 }；
It is as shown in Figure 1 the artwork of 3 secondary classic map pictures, TP figure, the optimal profile of the method for document 1 detection, implements The optimal profile of the method for example 1 detection；It is as shown in table 2 the optimal F values that the method for document 1 of above-mentioned 3 width image detects and implementation The optimal F values contrast of the method for example 1 detection；
The F value contrast tables of table 2
No matter it can be seen from the results above that from the effect of contours extract or from performance indications parameter, implement The method of example 1 is superior to the profile testing method in document 1.

Claims (6)

1. a kind of profile testing method based on the global modulation of changeable reception field yardstick, it is characterised in that comprise the following steps：
A, image to be detected through gray proces is inputted, Gaussian difference is carried out using difference of Gaussian function to the gray value of each pixel Point filtering, obtain the difference of Gaussian filter value of each pixel, respectively in the difference of Gaussian filter value of each pixel on the occasion of with Negative value is normalized, and obtains the normalization difference of Gaussian filter function of each pixel；Utilize the normalization of each pixel Difference of Gaussian filter function carries out convolution with the gray value of corresponding pixel points respectively, obtains the normalization difference of Gaussian of each pixel Filter value；
B, high yardstick value, the low scale-value of scaling function are preset, predetermined threshold value, the normalization difference of Gaussian of each pixel is filtered Value is contrasted with threshold value respectively, if pixel normalization difference of Gaussian filter value is more than or equal to threshold value, the pixel Corresponding scaling function value is high yardstick value, if pixel normalization difference of Gaussian filter value is less than threshold value, the pixel Corresponding scaling function value is low scale-value；
C, inhibition strength is preset, presets the multiple directions parameter for dividing equally circumference；Each pixel in image to be detected is pressed respectively Gabor filtering is carried out according to all directions parameter, obtains the responses of all directions of each pixel, employed in wherein Gabor filtering Scaling function value be each pixel identified high yardstick value or low scale-value in stepb；For each pixel, choose Maximum in the response of its all directions, the classical receptive field stimuli responsive as the pixel；
D, by the distance weighting function of the classical receptive field stimuli responsive of each pixel and the pixel carry out after convolution with the picture Scale Multiplication corresponding to vegetarian refreshments, obtain the inhibition response of each pixel；
E, the classical receptive field stimuli responsive of each pixel is subtracted into the inhibition response of the pixel and the product of inhibition strength, obtained Profile to the pixel is responded, and profile response is handled using non-maxima suppression and dual threshold, obtains each pixel most Whole profile value, and then obtain final profile figure.
2. the profile testing method as claimed in claim 1 based on the global modulation of changeable reception field yardstick, it is characterised in that：
Described step A is specially：
Described difference of Gaussian function DoGσ(x, y) is：
<mrow> <msub> <mi>DoG</mi> <mi>&amp;sigma;</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mn>2</mn> <mi>&amp;pi;</mi> <msup> <mrow> <mo>(</mo> <mn>4</mn> <mi>&amp;sigma;</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> <mi>exp</mi> <mrow> <mo>(</mo> <mo>-</mo> <mfrac> <mrow> <msup> <mi>x</mi> <mn>2</mn> </msup> <mo>+</mo> <msup> <mi>y</mi> <mn>2</mn> </msup> </mrow> <mrow> <mn>2</mn> <msup> <mrow> <mo>(</mo> <mn>4</mn> <mi>&amp;sigma;</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>-</mo> <mfrac> <mn>1</mn> <mrow> <mn>2</mn> <msup> <mi>&amp;pi;&amp;sigma;</mi> <mn>2</mn> </msup> </mrow> </mfrac> <mi>exp</mi> <mrow> <mo>(</mo> <mo>-</mo> <mfrac> <mrow> <msup> <mi>x</mi> <mn>2</mn> </msup> <mo>+</mo> <msup> <mi>y</mi> <mn>2</mn> </msup> </mrow> <mrow> <mn>2</mn> <msup> <mi>&amp;sigma;</mi> <mn>2</mn> </msup> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
Wherein σ is initialization yardstick；
Described normalization difference of Gaussian filter function wσ(x, y) is：
<mrow> <msub> <mi>w</mi> <mi>&amp;sigma;</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mfrac> <mrow> <msub> <mi>DoG</mi> <mi>&amp;sigma;</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </mrow> <mrow> <msub> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>DoG</mi> <mi>&amp;sigma;</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&lt;</mo> <mn>0</mn> </mrow> </msub> <msub> <mi>DoG</mi> <mi>&amp;sigma;</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>,</mo> <msub> <mi>DoG</mi> <mi>&amp;sigma;</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&lt;</mo> <mn>0</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <mrow> <msub> <mi>DoG</mi> <mi>&amp;sigma;</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </mrow> <mrow> <msub> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>DoG</mi> <mi>&amp;sigma;</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&amp;GreaterEqual;</mo> <mn>0</mn> </mrow> </msub> <msub> <mi>DoG</mi> <mi>&amp;sigma;</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>,</mo> <msub> <mi>DoG</mi> <mi>&amp;sigma;</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&amp;GreaterEqual;</mo> <mn>0</mn> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
Described normalization difference of Gaussian filter value woutσ(x, y) is：
woutσ(x, y)=I (x, y) * wσ(x,y) (3)。
3. the profile testing method as claimed in claim 2 based on the global modulation of changeable reception field yardstick, it is characterised in that：
Described step B is specially：
Described scaling function σ (x, y) is：
<mrow> <mi>&amp;sigma;</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>&amp;sigma;</mi> <mi>L</mi> </msub> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>wout</mi> <mi>&amp;sigma;</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&lt;</mo> <mi>t</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;sigma;</mi> <mi>H</mi> </msub> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>wout</mi> <mi>&amp;sigma;</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&amp;GreaterEqual;</mo> <mi>t</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
Wherein σHFor high yardstick value, σLFor low scale-value, σH=σL+ s, t be level off to 0 threshold value, s be yardstick step-length.
4. the profile testing method as claimed in claim 3 based on the global modulation of changeable reception field yardstick, it is characterised in that：
Described step C is specially：
The two-dimensional Gabor function expression of described Gabor filter group is as follows：
Whereinγ is one and represents the normal of oval receptive field major and minor axis ratio Number, parameter lambda are wavelength, and σ (x, y) is scaling function, and 1/ λ is the spatial frequency of cosine function,It is phase angular dimensions, θ Gabor The directioin parameter of filtering；
I (x, y) is the gray value of each pixel of image to be detected, and * is convolution operator；
Gabor energy values are calculated as follows：
<mrow> <msub> <mi>E</mi> <mrow> <mi>&amp;lambda;</mi> <mo>,</mo> <mi>&amp;sigma;</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>&amp;theta;</mi> <mi>i</mi> </msub> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <msqrt> <mrow> <msub> <msup> <mi>e</mi> <mn>2</mn> </msup> <mrow> <mi>&amp;lambda;</mi> <mo>,</mo> <mi>&amp;sigma;</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>&amp;theta;</mi> <mi>i</mi> </msub> <mo>,</mo> <mn>0</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <msup> <mi>e</mi> <mn>2</mn> </msup> <mrow> <mi>&amp;lambda;</mi> <mo>,</mo> <mi>&amp;sigma;</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>&amp;theta;</mi> <mi>i</mi> </msub> <mo>,</mo> <mi>&amp;pi;</mi> <mo>/</mo> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </mrow> </msqrt> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
<mrow> <msub> <mi>&amp;theta;</mi> <mi>i</mi> </msub> <mo>=</mo> <mfrac> <mrow> <mi>&amp;pi;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> <msub> <mi>N</mi> <mi>&amp;theta;</mi> </msub> </mfrac> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <msub> <mi>N</mi> <mi>&amp;theta;</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
Wherein θiFor a direction of Gabor filtering, NθFor the number in the direction of Gabor filtering；
Described classical receptive field stimuli responsive Ec (x, y；σ (x, y)) be：
<mrow> <mi>E</mi> <mi>c</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>;</mo> <mi>&amp;sigma;</mi> <mo>(</mo> <mrow> <mi>x</mi> <mo>,</mo> <mi>y</mi> </mrow> <mo>)</mo> <mo>)</mo> </mrow> <mo>=</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>{</mo> <msub> <mi>E</mi> <mrow> <mi>&amp;lambda;</mi> <mo>,</mo> <mi>&amp;sigma;</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>&amp;theta;</mi> <mi>i</mi> </msub> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>|</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <msub> <mi>N</mi> <mi>&amp;theta;</mi> </msub> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> <mo>.</mo> </mrow>
5. the profile testing method as claimed in claim 4 based on the global modulation of changeable reception field yardstick, it is characterised in that：
Described step D is specially：
Described distance weighting function wσ(x, y) is：
<mrow> <msub> <mi>w</mi> <mi>&amp;sigma;</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>H</mi> <mrow> <mo>(</mo> <mi>D</mi> <mi>o</mi> <mi>G</mi> <mo>(</mo> <mrow> <mi>x</mi> <mo>,</mo> <mi>y</mi> </mrow> <mo>)</mo> <mo>)</mo> </mrow> </mrow> <mrow> <mo>|</mo> <mo>|</mo> <mi>H</mi> <mrow> <mo>(</mo> <mi>D</mi> <mi>o</mi> <mi>G</mi> <mo>(</mo> <mrow> <mi>x</mi> <mo>,</mo> <mi>y</mi> </mrow> <mo>)</mo> <mo>)</mo> </mrow> <mo>|</mo> <msub> <mo>|</mo> <mn>1</mn> </msub> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>10</mn> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
Wherein,
<mrow> <mi>D</mi> <mi>o</mi> <mi>G</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mn>2</mn> <mi>&amp;pi;</mi> <msup> <mrow> <mo>(</mo> <mn>4</mn> <mi>&amp;sigma;</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> <mi>exp</mi> <mrow> <mo>(</mo> <mo>-</mo> <mfrac> <mrow> <msup> <mi>x</mi> <mn>2</mn> </msup> <mo>+</mo> <msup> <mi>y</mi> <mn>2</mn> </msup> </mrow> <mrow> <mn>2</mn> <msup> <mrow> <mo>(</mo> <mn>4</mn> <mi>&amp;sigma;</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>-</mo> <mfrac> <mn>1</mn> <mrow> <mn>2</mn> <msup> <mi>&amp;pi;&amp;sigma;</mi> <mn>2</mn> </msup> </mrow> </mfrac> <mi>exp</mi> <mrow> <mo>(</mo> <mo>-</mo> <mfrac> <mrow> <msup> <mi>x</mi> <mn>2</mn> </msup> <mo>+</mo> <msup> <mi>y</mi> <mn>2</mn> </msup> </mrow> <mrow> <mn>2</mn> <msup> <mi>&amp;sigma;</mi> <mn>2</mn> </msup> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
Wherein, | | | |1For (L1) norm, H (x)=max (0, x), DoG (x, y) they are expression formula corresponding to DoG templates；
The inhibition response Inh (x, y) of each pixel is：
Inh (x, y)=Ec (x, y；σ(x,y))*wσ(x,y)σ(x,y) (11)。
6. the profile testing method as claimed in claim 5 based on the global modulation of changeable reception field yardstick, it is characterised in that：
Described step E is specially：
Described profile responds R (x, y)：
R (x, y)=H (Ec (x, y；σ(x,y))-αInh(x,y)) (12)；
Wherein H (x)=max (0, x), α are inhibition strength.
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