CN113093198A - Acoustic imaging detection method based on multi-scale Markov random field - Google Patents

Acoustic imaging detection method based on multi-scale Markov random field Download PDF

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CN113093198A
CN113093198A CN202110258326.4A CN202110258326A CN113093198A CN 113093198 A CN113093198 A CN 113093198A CN 202110258326 A CN202110258326 A CN 202110258326A CN 113093198 A CN113093198 A CN 113093198A
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markov random
random field
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CN113093198B (en
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陈哲
王悦
周思源
王慧斌
沈洁
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Hohai University HHU
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/29—Graphical models, e.g. Bayesian networks
    • G06F18/295—Markov models or related models, e.g. semi-Markov models; Markov random fields; Networks embedding Markov models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation

Abstract

The invention discloses an acoustic imaging detection method based on a multi-scale Markov random field, which comprises the following steps: firstly, constructing a multi-scale acoustic imaging data Markov random field model; secondly, combining a multi-scale Markov random field model and a level set model to construct a multi-scale Markov random field-level set combined model; and finally, the acoustic imaging detection is realized through the convergence of the multi-scale Markov random field-level set combined model, and the substantial defects of low resolution and high noise of the acoustic imaging information in the detection process are overcome.

Description

Acoustic imaging detection method based on multi-scale Markov random field
Technical Field
The invention relates to an acoustic imaging detection method, in particular to an acoustic imaging detection method combining a multi-scale Markov random field model and a level set model.
Background
With the development of electronic computers, digitizing circuits and highly integrated microelectronic devices, acoustic imaging technology has improved dramatically. The acoustic imaging determines the position of a sound source by measuring the difference of signal phases of sound waves reaching each microphone in a certain space range and the amplitude of the sound source, and simultaneously displays the distribution of the sound source in the space in an image mode, wherein the strength of the sound is expressed by color and brightness in the image. Acoustic imaging is a measurement technique based on microphone arrays.
With the increasing demand for ocean exploration, the development of acoustic imaging exploration has been dramatically advanced as a key technology in ocean development. The development of the acoustic imaging technology improves the speed and the resolution of underwater target detection, and provides an important technical basis for realizing the detection and the identification of the underwater target. However, due to the complexity of the underwater acoustic environment, the technical level of the noise elimination material, the imaging nonlinearity of the sonar equipment and other limiting conditions, the acquired sonar image has low signal-to-noise ratio and the characteristics of poor target imaging conditions and weak characteristics in the image, and the characteristics cause great difficulty in the work of detection, identification and the like of the target in the sonar image.
An algorithm with a good sonar image segmentation effect is Markov (MRF) random field model segmentation. The Markov random field method is based on MRF model and Bayes theory, it links the uncertainty description with the prior knowledge, and determines the segmented target function according to the optimal criterion in the statistical decision and theoretical estimation by observing the image, and solves the maximum possible distribution of the function meeting these conditions, thus converting the segmentation problem into the optimal problem, the biggest advantage is that the model parameter is less and the space constraint force is strong. The markov random field model emphasizes the spatial constraint relationship of the image, and considers adjacent pixels in the image to be relevant and dependent. System for passing through domain of relationship between any pixel point and other pixel points in image
Figure BDA0002968861620000011
Where S is the set of all pixel points of the image, and N isiIs the field of pixel point i, whereinThe pixel point satisfies two conditions: (1) the neighborhood of a pixel does not contain itself i.e.
Figure BDA0002968861620000013
(2) The fields have mutual characters
Figure BDA0002968861620000012
Therefore, the markov random field can well suppress noise, but for images with poor imaging quality, the uneven intensity is also a big problem, and the pixel-based markov random field is far from enough, so that a region-based markov random field appears. The method takes the region as the minimum unit, better solves the problem of uneven intensity, but inevitably loses the detail information of the image while resisting noise and uneven intensity.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problem that the traditional single-scale Markov random field model is insufficient in noise and image non-uniform suppression in acoustic imaging detection, the invention aims to provide an acoustic imaging detection method based on a multi-scale Markov random field, which can better segment images with poor imaging quality.
The technical scheme is as follows: an acoustic imaging detection method based on a multi-scale Markov random field comprises the following steps:
(4) constructing a multi-scale Markov random field model of acoustic imaging data under a pixel scale and a region scale;
(5) combining a multi-scale Markov random field model with a level set model to construct a multi-scale Markov random field-level set combined model;
(6) and detecting the target by the convergence of the multi-scale Markov random field-level set combined model to realize acoustic imaging detection.
Further, in the step (1), the following Markov random field model under multiple scales is constructed:
P(X|Y)＝P(YP|X)P(YR|X)P(X)
wherein X is the tag field of the acoustic imaging,y is the observation field of the acoustic imagingPIs the observation field at the pixel scale, YRFor field of observation at the regional scale, P (Y)P|X)，P(YR| X) is the likelihood probability of the label at the pixel scale and the region scale, respectively; p (x) is the probability distribution of the acoustic imaging tag field;
P(YP| X) is modeled as:
Figure BDA0002968861620000021
wherein D is YPDimension of (2), YPRepresenting the grey scale, mu, of pixel information for the pixel informationPIs the average value, σ, of the pixel gray scale informationPIs the variance of the pixel gray scale information;
P(YR| X) is modeled as:
Figure BDA0002968861620000031
wherein D' is YRDimension (d); mu.sRIs the average value, σ, of the region informationRIs the variance of the region information, YRIs area information;
Figure BDA0002968861620000032
wherein p isRAs a ratio of the area of the divided region to the area of the whole image, NRFor the number of neighborhoods around the partition area, pTThe ratio of the area of the Tth neighborhood to the area of the whole image is set;
p (X) is modeled as:
Figure BDA0002968861620000033
wherein Z is a normalization constant;
Figure BDA0002968861620000034
s is the image size, S is the pixel point in the image, xsDemarcating x-labeled and s-positioned pixel points, NsIs the neighborhood size of the pixel s, ytIs a label on a pixel with t in the neighborhood of pixel s, V (x)s,yt) Is a potential energy function of the pixel point s and the pixel point t in the neighborhood,
Figure BDA0002968861620000035
beta is a preset constant.
Further, in the step (2), the following multi-scale Markov random field-level set joint model is constructed:
E＝EUMRF+Ereg
wherein E isUMRFFor multi-scale Markov random field model energy terms, EregIs a regular term;
EUMRFmodeling is as follows:
Figure BDA0002968861620000036
wherein the content of the first and second substances,
Figure BDA0002968861620000037
for the level set function, H (-) is the Heaviside function, Xout、XinRespectively the pixels outside and inside the profile of the horizontal set;
Eregmodeling is as follows:
Figure BDA0002968861620000041
wherein ν and η are weight adjustment coefficients, p is a potential function:
Figure BDA0002968861620000042
and p(s) is equal to or more than p (1) for all s;
in the end of this process,
Figure BDA0002968861620000043
further, in the step (3), a gradient descent method is adopted to converge the multi-scale Markov random field-level set combined model, and the specific steps are as follows:
Figure BDA0002968861620000044
wherein t is an evolution time variable,
Figure BDA0002968861620000045
is a regular term gradient function used for controlling the convergence process of the multi-scale Markov random field-level set combined model.
Has the advantages that:
compared with the prior art, the invention has the following remarkable progress: 1. the advantages and the disadvantages of a Markov random field model are integrated, the substantive defects of low resolution and high noise of acoustic imaging information in the detection process are overcome, images with poor imaging quality can be better segmented, and more detailed information is kept in the segmentation result; 2. aiming at the images with high noise and uneven intensity, the problem that the traditional single-scale Markov random field model is insufficient in suppression of noise and image unevenness in acoustic imaging detection is solved.
Drawings
FIG. 1 is a flow chart of a first embodiment of the present invention;
fig. 2 is an original sonar image of a second embodiment of the present invention;
fig. 3 shows the final segmentation result of the second embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further clarified by the following specific embodiments in combination with the attached drawings.
The first embodiment is as follows:
as shown in fig. 1, an acoustic imaging detection method based on a multi-scale markov random field includes the following steps:
the method comprises the following steps: and constructing a multi-scale Markov random field model. The model is as follows:
P(X|Y)＝P(YP|X)P(YR|X)P(X)
wherein X is a tag field of acoustic imaging, Y is an observation field of acoustic imaging, and Y isPIs the observation field at the pixel scale, YRFor field of observation at the regional scale, P (Y)P|X)，P(YR| X) is the likelihood probability of the label at the pixel scale and the region scale, respectively;
first, likelihood function P (Y) based on pixelP| X) and region-based likelihood function P (Y)RI X), the modeling adopts Gaussian distribution, and the method comprises the following steps:
Figure BDA0002968861620000051
Figure BDA0002968861620000052
wherein the content of the first and second substances,
(1) d, D' are each YPAnd YRDimension (d);
(2)YPrepresenting a gray scale of the pixel information for the pixel information; y isRFor region information, defined by the following equation:
Figure BDA0002968861620000053
wherein p isRAs a ratio of the area of the divided region to the area of the whole image, NRAs a region near the divided region, pTIs the proportion of the area of one region T in the field to the whole image area.
(3) μ and σ are the mean and standard deviation of different regions of the image, divided according to level set region. Mu.sPAnd σPIs the average value of the pixel characteristics, muPAnd σPIs the average of the regional features.
Figure BDA0002968861620000061
Figure BDA0002968861620000062
Figure BDA0002968861620000063
Figure BDA0002968861620000064
Wherein
Figure BDA0002968861620000065
Respectively the mean values of the inside and outside of the segmentation region under the pixel scale,
Figure BDA0002968861620000066
respectively the variance inside and outside the segmentation region at the pixel scale, wherein
Figure BDA0002968861620000067
Are respectively the average values inside and outside the segmentation region under the region scale,
Figure BDA0002968861620000068
the variance of the inside and outside of the segmentation region under the region scale is respectively, mean { } is a mean function, and default { } is a variance function.
Secondly, according to Hammersley-Clifford theorem, a one-to-one correspondence relationship exists between the Markov random field and the Gibbs random field, namely if the random field F has Markov property, the random field F has Gibbs distribution; conversely, if the random field F has a Gibbs distribution, then the random field F has a Markov property. Thus, p (x) modeling employs a gibbs distribution:
Figure BDA0002968861620000069
wherein Z is a normalization constant;
(1) z is a normalization constant such that
Figure BDA00029688616200000610
(2) U (x) is a function of energy:
Figure BDA00029688616200000611
s is an input image of size NxM, S ═ SiI ═ 1,2, L, nxm }, s is a pixel point in the image, x issDemarcating x-labeled and s-positioned pixel points, NsIs the neighborhood size of the pixel s, ytThe labels on the pixel points with the position t in the neighborhood of the pixel points s; v (x)s,yt) Is a potential energy function of the pixel point s and the pixel point t in the neighborhood,
Figure BDA0002968861620000071
β is a predetermined constant, typically 1.
Step two: and establishing a multi-scale Markov random field-level set combined model.
Combining a multi-scale markov random field model with a level set model:
E＝EUMRF+Ereg
wherein E isUMRFFor multi-scale Markov random field model energy terms, EregIs a regular term;
EUMRFmodeling is as follows:
Figure BDA0002968861620000072
wherein the content of the first and second substances,
Figure BDA0002968861620000073
for the level set function, H (-) is the Heaviside function, Xout，XinPixels outside and inside the horizontal set contour;
Eregmodeling is as follows:
Figure BDA0002968861620000074
wherein ν and η are weight adjustment coefficients, p is a potential function:
Figure BDA0002968861620000075
and p(s) is equal to or more than p (1) for all s;
finally, modeling the multi-scale Markov random field-level set combined model as follows:
Figure BDA0002968861620000076
step three: and imaging detection is realized through convergence of a multi-scale Markov random field-level set combined model.
Adopting a gradient descent method to perform convergence calculation on the multi-scale Markov random field-level set combined model:
Figure BDA0002968861620000081
wherein t is an evolution time variable,
Figure BDA0002968861620000082
the method is a regular term gradient function and is used for controlling the convergence process of the multi-scale Markov random field-level set combined model.
Example two:
taking the sonar image as an example, referring to fig. 2, the pixel feature Y in the sonar image is first extractedPAnd region feature YR：
YPProcessing is not needed, and the gray value of the sonar image is directly obtained;
Figure BDA0002968861620000083
wherein p isRAs a ratio of the area of the divided region to the area of the whole image, NRAs a region near the divided region, pTIs the proportion of the area of one region T in the field to the whole image area. The initial segmentation roughly divides the sonar image in an over-segmentation mode.
And respectively substituting the extracted features into likelihood functions to obtain:
Figure BDA0002968861620000084
Figure BDA0002968861620000085
where D is the dimension of Y, σ is the standard deviation of Y, and μ is the mean of Y.
The prior probability adopts Gibbs distribution:
Figure BDA0002968861620000086
wherein Z is a normalization constant;
Figure BDA0002968861620000087
representing the sum of all energy blobs in the sonar image.
Since the sonar image is divided into three regions, namely a target region, a highlight region and a background region, the level set needs to adopt a two-phase level set. Finally, the multi-scale Markov random field-level set combined model for image segmentation is constructed as follows:
Figure BDA0002968861620000091
wherein phi is1And phi2Is a level set function; h (-) is the Heaviside function; eregThe regularization term is used for controlling the regularization of the convergence of the level set function; v and mu are weight coefficients; e (X)1|Y)、E(X2|Y)、E(X3|Y)、E(X4Y) are respectively a target area, a shadow area, a background area and other four areas in the sonar image,
Figure BDA0002968861620000092
and
Figure BDA0002968861620000093
is a regular term.
Therefore, the multi-scale Markov random field-level set combined model provided by the invention is obtained.
And finally, carrying out convergence calculation on the multi-scale Markov random field-level set combined model by adopting a gradient descent method:
Figure BDA0002968861620000094
Figure BDA0002968861620000095
according to the iterative equation phik+1＝φk+ΔφkAnd obtaining a segmentation result after multiple iterations of Δ t.
Thus, an energy function evolution process is completed, and sonar image segmentation is realized, as shown in fig. 3.

Claims (5)

1. An acoustic imaging detection method based on a multi-scale Markov random field is characterized by comprising the following steps:
(1) constructing a multi-scale Markov random field model of acoustic imaging data under a pixel scale and a region scale;
(2) combining a multi-scale Markov random field model with a level set model to construct a multi-scale Markov random field-level set combined model;
(3) and detecting the target by the convergence of the multi-scale Markov random field-level set combined model to realize acoustic imaging detection.
2. The method for acoustic imaging detection based on multi-scale markov random fields according to claim 1, wherein in step (1), the following multi-scale markov random field model is constructed:
P(X|Y)＝P(YP|X)P(YR|X)P(X)
wherein X is a tag field of acoustic imaging, Y is an observation field of acoustic imaging, and Y isPIs the observation field at the pixel scale, YRFor field of observation at the regional scale, P (Y)P|X)，P(YR| X) is the likelihood probability of the label at the pixel scale and the region scale, respectively; p (x) is the probability distribution of the acoustic imaging tag field;
P(YP| X) is modeled as:
Figure FDA0002968861610000011
wherein D is YPDimension of (2), YPRepresenting the grey scale, mu, of pixel information for the pixel informationPIs the average value, σ, of the pixel gray scale informationPIs the variance of the pixel gray scale information;
P(YR| X) is modeled as:
Figure FDA0002968861610000012
wherein D' is YRDimension (d); mu.sRIs the average value, σ, of the region informationRIs the variance of the region information, YRIs area information;
Figure FDA0002968861610000021
wherein p isRAs a ratio of the area of the divided region to the area of the whole image, NRFor the number of neighborhoods around the partition area, pTThe ratio of the area of the Tth neighborhood to the area of the whole image is set;
p (X) is modeled as:
Figure FDA0002968861610000022
wherein Z is a normalization constant;
Figure FDA0002968861610000023
s is the image size, S is the pixel point in the image, xsDemarcating x-labeled and s-positioned pixel points, NsIs the neighborhood size of the pixel s, ytIs a label on a pixel with t in the neighborhood of pixel s, V (x)s,yt) Is a potential energy function of the pixel point s and the pixel point t in the neighborhood,
Figure FDA0002968861610000024
beta is a preset constant.
3. The method for acoustic imaging detection based on multi-scale markov random field according to claim 2, wherein in step (2), the following multi-scale markov random field-level set joint model is constructed:
E＝EUMRF+Ereg
wherein E isUMRFFor multi-scale Markov random field model energy terms, EregIs a regular term;
EUMRFmodeling is as follows:
Figure FDA0002968861610000025
wherein the content of the first and second substances,
Figure FDA0002968861610000026
for the level set function, H (-), is the Heaviside function,Xout、Xinrespectively the pixels outside and inside the profile of the horizontal set;
Eregmodeling is as follows:
Figure FDA0002968861610000027
wherein ν and η are weight adjustment coefficients, p is a potential function:
Figure FDA0002968861610000028
and p(s) is equal to or more than p (1) for all s;
in the end of this process,
Figure FDA0002968861610000031
4. the method for acoustic imaging detection based on multi-scale Markov random fields as claimed in claim 3, wherein in step (3), the multi-scale Markov random field-level set joint model is converged by gradient descent.
5. The method for detecting acoustic imaging based on multi-scale Markov random field according to claim 4, wherein the step of converging the multi-scale Markov random field-level set combination model by gradient descent comprises the following steps:
Figure FDA0002968861610000032
wherein t is an evolution time variable,
Figure FDA0002968861610000033
is a regular term gradient function used for controlling the convergence process of the multi-scale Markov random field-level set combined model.
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