CN108053415A - Based on the bionical profile testing method for improving non-classical receptive field - Google Patents
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- 238000012360 testing method Methods 0.000 title claims abstract description 13
- 230000005764 inhibitory process Effects 0.000 claims abstract description 30
- 230000004044 response Effects 0.000 claims abstract description 17
- 230000002401 inhibitory effect Effects 0.000 claims abstract description 6
- 238000001914 filtration Methods 0.000 claims description 7
- 230000001629 suppression Effects 0.000 claims description 6
- 230000009977 dual effect Effects 0.000 claims description 3
- 238000001514 detection method Methods 0.000 abstract description 10
- 230000007547 defect Effects 0.000 abstract description 2
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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Abstract
The present invention is intended to provide it is a kind of based on the bionical profile testing method for improving non-classical receptive field, comprise the following steps：A, image to be detected through gray proces is inputted；B, the Gabor filter functions for including multiple directions parameter are preset, carry out classical receptive field stimuli responsive, optimal direction of the corresponding direction as the pixel respectively to each pixel in image to be detected；C, built using log functions and inhibit kernel function, distance weighting function is built by inhibiting kernel function；The distance weighting function of the classical receptive field stimuli responsive of each pixel and the pixel is subjected to convolution and obtains the inhibition response of the pixel；D, the classical receptive field stimuli responsive of each pixel is subtracted into the inhibition response of the pixel and the product of default inhibition strength, and the final profile value of the pixel is calculated.The defects of detection method overcomes the prior art has the characteristics that the spatial character for meeting visual experience open country and detection result are more preferable.
Description
Technical field
The present invention relates to image processing fields, and in particular to a kind of based on the bionical contour detecting for improving non-classical receptive field
Method.
Background technology
For non-classical receptive field region, there is the model of many different inhibition situations.And traditional butterfly-type inhibits model, is
On the basis for inhibiting model in DoG, the suppression of background texture information is completed by the region of artificial limit lateral areas and petiolarea
System；It is the division by manually carrying out region that such butterfly-type, which inhibits core, it is necessary first to by ± 45 ° of directrixes to non-classical sense
Flank is carried out by open country and top area is divided, so as to show that the butterfly-type of a non-automatic defined area inhibits model.Press down in the butterfly-type
In simulation, lateral areas and petiolarea have different working rules, i.e. the calculating of lateral areas inhibition strength is based on an accurate ruler
Feature is spent come what is completed, and the calculating of petiolarea inhibition strength is then the adaptive change with the local feature on different spaces scale
's.In the local grain region that there may be effective contour, petiolarea can apply a weaker inhibition to classical receptive field,
The region of random texel is being filled on the contrary, a stronger inhibition can be applied.Traditional butterfly-type inhibits model simultaneously
It can not achieve the automatic division for inhibiting region and inhibit model and rationally rotated using the optimal direction of pixel, therefore,
Considerably increase the time cost of entire contour detecting process.
The content of the invention
It is the present invention is intended to provide a kind of based on the bionical profile testing method for improving non-classical receptive field, the detection method gram
The defects of taking the prior art has the characteristics that detection result is good, detection efficiency is high.
Technical scheme is as follows：
A kind of bionical profile testing method based on improvement non-classical receptive field, comprises the following steps：
A, image to be detected through gray proces is inputted；
B, the Gabor filter functions for including multiple directions parameter are preset, each pixel in image to be detected is distinguished
Gabor energy balanes are carried out using Gabor filter functions, obtain the Gabor energy values under all directions of each pixel；
For each pixel, the maximum in the Gabor energy values of its all directions is chosen, the classical receptive field as the pixel
Stimuli responsive, optimal direction of the corresponding direction of the maximum as the pixel；
C, structure inhibits kernel function, and distance weighting function is built by inhibiting kernel function；The classics of each pixel are felt
Convolution, which is carried out, by the distance weighting function of wild stimuli responsive and the pixel obtains the inhibition response of the pixel；
Inhibition kernel function log (x, the y；ε,σw) be：
Wherein For the optimal direction of pixel (x, y), ε=0.1,
σwTo inhibit core scale；
The classical receptive field stimuli responsive of each pixel is subtracted to the inhibition response of the pixel and default inhibition D,
The product of intensity obtains the profile response of the pixel, and profile response using non-maxima suppression and dual threshold is handled, is obtained
The final profile value of each pixel.
Preferably, the step B is specific as follows：
The expression formula of the Gabor filter functions is as follows：
Whereinγ represents oval receptive field major and minor axis ratio for one
Constant, parameter lambda is wavelength, and σ is scale, and 1/ λ is the spatial frequency of cosine function,It is phase angular dimensions, θ filters for Gabor
Directioin parameter；
Gabor energy values calculate as follows：
Wherein
Wherein θiFor a direction parameter of Gabor filtering, NθFor the number of the directioin parameter of Gabor filtering；I (x, y) is
Image to be detected, * are convolution operator；
The expression formula of classical receptive field stimuli responsive Ec (x, y) is as follows：
Preferably, the step C is specially：
The distance weighting function wσ(x,y；ε,σw) be：
Wherein,
Wherein | | | |1For (L1) norm, H (x)=max (0, x)；
The inhibition response Inh (x, y) of each pixel is：
Inh (x, y)=Ec (x, y) * wσ(x,y；ε,σw) (8)。
Preferably, the step D is specially：
The profile responds R (x, y)：
R (x, y)=H (Ec (x, y)-α Inh (x, y)) (9)；
Wherein H (x)=max (0, x), α are inhibition strength.
The present invention then no longer needs artificially to divide non-classical receptive field region using improved inhibition model,
But by carry angle parameter log functions inhibit verification optimal direction carry out inhibit region rotation, eliminate manually into
The workload of row division, the whole operation efficiency of raising；Improved inhibition model enables the inhibition of each pixel to tie
The optimal direction of the pixel is closed to carry out, to reach optimal inhibition, while also more meets the visual characteristic of receptive field.
Description of the drawings
Fig. 1 is 1 profile testing method of embodiment and the testing result comparison diagram of 1 method of document
Specific embodiment
The present invention is illustrated with reference to the accompanying drawings and examples.
Embodiment 1
A kind of bionical profile testing method based on improvement non-classical receptive field, comprises the following steps：
A, image to be detected through gray proces is inputted；
B, the Gabor filter functions for including multiple directions parameter are preset, each pixel in image to be detected is distinguished
Gabor energy balanes are carried out using Gabor filter functions, obtain the Gabor energy values under all directions of each pixel；
For each pixel, the maximum in the Gabor energy values of its all directions is chosen, the classical receptive field as the pixel
Stimuli responsive, optimal direction of the corresponding direction of the maximum as the pixel；
The step B is specific as follows：
The expression formula of the Gabor filter functions is as follows：
Whereinγ represents oval receptive field axial ratio for one
The constant of example, parameter lambda are wavelength, and σ is scale, and 1/ λ is the spatial frequency of cosine function,It is phase angular dimensions, θ filters for Gabor
The directioin parameter of ripple；
Gabor energy values calculate as follows：
Wherein
Wherein θiFor a direction parameter of Gabor filtering, NθFor the number of the directioin parameter of Gabor filtering；I (x, y) is
Image to be detected, * are convolution operator；
The expression formula of classical receptive field stimuli responsive Ec (x, y) is as follows：
WhereinFor the optimal direction of pixel (x, y)；
C, structure inhibits kernel function, and distance weighting function is built by inhibiting kernel function；The classics of each pixel are felt
Convolution, which is carried out, by the distance weighting function of wild stimuli responsive and the pixel obtains the inhibition response of the pixel；
Inhibition kernel function log (x, the y；ε,σw) be：
Whereinε=0.1, σwTo inhibit core scale；
The distance weighting function wσ(x,y；ε,σw) be：
Wherein,
Wherein, ε=0.1, σw=σ, | | | |1For (L1) norm, H (x)=max (0, x)；
The inhibition response Inh (x, y) of each pixel is：
Inh (x, y)=Ec (x, y) * wσ(x,y；ε,σw) (10)；
The classical receptive field stimuli responsive of each pixel is subtracted to the inhibition response of the pixel and default inhibition D,
The product of intensity obtains the profile response of the pixel, and profile response using non-maxima suppression and dual threshold is handled, is obtained
The final profile value of each pixel；
The step D is specially：
The profile responds R (x, y)：
R (x, y)=H (Ec (x, y)-α Inh (x, y)) (11)；
Wherein H (x)=max (0, x), α are inhibition strength；
Non-maxima suppression and binary conversion treatment involved in the present embodiment is using the method described in document 1, wherein wrapping
The two threshold value t containedh,tlIt is arranged to tl=0.5th, calculated by threshold value quantile p and obtained；
Document 1：GrigorescuC,PetkovN,WestenbergM.Contourdetectionbasedonnonclass
ical receptivefieldinhibition[J].IEEETransactionsonImageProcessing,2003,12
(7):729-739；
The contour detecting isotropic model for below providing the profile testing method of the present embodiment and document 1 carries out effective
Property comparison, wherein Performance Evaluating Indexes P uses the following standard that provides in document 1：
N in formulaTP、nFP、nFNThe number of the profile of correct profile, error profile and omission that detection obtains is represented respectively,
Evaluation metrics P values represent that the effect of contour detecting is better between [0,1], closer to 1, in addition, definition tolerance is：
The all calculations detected in the neighborhood of 5*5 correctly detect；
Choose 4 secondary classic map pictures in Fig. 1 and carry out Usefulness Pair ratio, be respectively adopted the isotropic model in document 1 and
1 method of embodiment carries out contour detecting to above-mentioned 4 width figure, and the parameter group of wherein 1 method selection of embodiment is as shown in table 1, chooses
The optimal result obtained in parameter group is compared；
1 embodiment of table, 1 parameter group table
Isotropic model in document 1 uses following parameter：α={ 1.0,1.2 }；σ=1.4,1.6,1.8,2.0,
2.2,2.4,2.6,2.8}；P={ 0.1,0.2,0.3,0.4,0.5 }；
Fig. 1 show artwork, the reality of respectively basket, elephant_2, goat_3, hyena totally 4 secondary classic map pictures
Profile diagram, the optimal profile of 1 method of document detection, the optimal profile of 1 method of embodiment detection；It is above-mentioned 4 width figure as shown in table 2
The comparison of the optimal P values and the optimal P values of 1 method of embodiment detection of 1 method of the document detection of picture；
2 optimal P values of table compare
No matter it can be seen from the results above that from the effect of contours extract or from performance indicator parameter, implement
1 method of example is superior to the isotropic model in document 1.
Claims (4)
- It is 1. a kind of based on the bionical profile testing method for improving non-classical receptive field, it is characterised in that comprise the following steps：A, image to be detected through gray proces is inputted；B, the Gabor filter functions for including multiple directions parameter are preset, each pixel in image to be detected is utilized respectively Gabor filter functions carry out Gabor energy balanes, obtain the Gabor energy values under all directions of each pixel；For each A pixel chooses the maximum in the Gabor energy values of its all directions, and the classical receptive field as the pixel, which stimulates, to be rung Should, optimal direction of the corresponding direction of the maximum as the pixel；C, structure inhibits kernel function, and distance weighting function is built by inhibiting kernel function；By the classical receptive field of each pixel The distance weighting function of stimuli responsive and the pixel carries out convolution and obtains the inhibition response of the pixel；Inhibition kernel function log (x, the y；ε,σw) be：Wherein For the optimal direction of pixel (x, y), ε=0.1, σwFor suppression Core scale processed；The classical receptive field stimuli responsive of each pixel is subtracted to the inhibition response of the pixel and default inhibition strength D, Product, obtain the profile response of the pixel, profile response using non-maxima suppression and dual threshold handled, is obtained each The final profile value of pixel.
- 2. as described in claim 1 based on the bionical profile testing method for improving non-classical receptive field, it is characterised in that：The step B is specific as follows：The expression formula of the Gabor filter functions 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 is scale, and 1/ λ is the spatial frequency of cosine function,It is phase angular dimensions, θ is the side of Gabor filtering To parameter；Gabor energy values calculate as follows：WhereinWherein θiFor a direction parameter of Gabor filtering, NθFor the number of the directioin parameter of Gabor filtering；I (x, y) is to be checked Altimetric image, * are convolution operator；The expression formula of classical receptive field stimuli responsive Ec (x, y) is as follows：
- 3. as claimed in claim 2 based on the bionical profile testing method for improving non-classical receptive field, it is characterised in that：The step C is specially：The distance weighting function wσ(x,y；ε,σw) be：Wherein,Wherein | | | |1For (L1) norm, H (x)=max (0, x)；The inhibition response Inh (x, y) of each pixel is：Inh (x, y)=Ec (x, y) * wσ(x,y；ε,σw) (8)。
- 4. as claimed in claim 3 based on the bionical profile testing method for improving non-classical receptive field, it is characterised in that：The step D is specially：The profile responds R (x, y)：R (x, y)=H (Ec (x, y)-α Inh (x, y)) (9)；Wherein H (x)=max (0, x), α are inhibition strength.
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| CN109978898A (en) * | 2019-02-01 | 2019-07-05 | 广西科技大学 | Profile testing method based on vector field energy balane |
| CN111539969A (en) * | 2020-04-23 | 2020-08-14 | 武汉铁路职业技术学院 | Image edge detection method and device, computer equipment and storage medium |
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| CN109919945B (en) * | 2019-02-01 | 2022-03-25 | 广西科技大学 | Contour detection method based on non-classical receptive field non-linear two-side subunit response |
| CN109978898B (en) * | 2019-02-01 | 2023-07-18 | 广西科技大学 | Contour detection method based on vector field energy calculation |
| CN111539969A (en) * | 2020-04-23 | 2020-08-14 | 武汉铁路职业技术学院 | Image edge detection method and device, computer equipment and storage medium |
| CN111539969B (en) * | 2020-04-23 | 2023-06-09 | 武汉铁路职业技术学院 | Image edge detection method, device, computer equipment and storage medium |
| CN111968141A (en) * | 2020-06-30 | 2020-11-20 | 广西科技大学 | Non-classical receptive field contour detection method based on multi-scale superposition |
| CN111968141B (en) * | 2020-06-30 | 2023-06-16 | 广西科技大学 | Non-classical receptive field contour detection method based on multi-scale superposition |
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Application publication date: 20180518 Assignee: GUANGXI YINGTENG EDUCATION TECHNOLOGY Co.,Ltd. Assignor: GUANGXI University OF SCIENCE AND TECHNOLOGY Contract record no.: X2023980053979 Denomination of invention: A biomimetic contour detection method based on improved non classical receptive field Granted publication date: 20200522 License type: Common License Record date: 20231226 |