CN106447651A - Traffic sign detection method based on orthogonal Gauss-Hermite moment - Google Patents
Traffic sign detection method based on orthogonal Gauss-Hermite moment Download PDFInfo
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Abstract
The invention discloses a traffic sign detection method based on an orthogonal Gauss-Hermite moment. The method comprises the following steps of: obtaining a first image of an area of interest; converting an obtained colorful image into a gray level, wherein the method directly calls a library function; independently carrying out calculation along an X direction and a Y direction, and subsequently, calculating the moment in a vertical direction, wherein in specific implementation, a first-order moment is replaced with the first-order difference of a filtered image, a third-order moment is replaced with the linear group of the first-order difference and the third-order difference of the filtered image, and an experiment only uses the third-order moment; applying an Otus method to segment the image; and citing morphological filtering to remove noise. By use of the method, an image in a traffic scene is selected to do an experiment, and an experiment result indicates that the traffic sign detection method is effective. Meanwhile, the traffic sign detection method is compared with other methods, the difference of the effects of first-order moment detection is small, the third-order moment can be further used, and the advantages of the traffic sign detection method are embodied.
Description
Technical field
The present invention relates to a kind of traffic mark detection method based on orthogonal Gauss-Hermite square.
Background technology
Progress with urbanization and the popularization of automobile, vehicles number rolls up, congested in traffic aggravation, traffic thing
Therefore take place frequently, the safety of highway communication and conevying efficiency problem become to become increasingly conspicuous.And the driving auxiliary based on computer vision
System is to solve the problems, such as one of important measures of traffic safety and conevying efficiency so that it gradually obtains in intelligent transportation system
Application.Its research is substantially carried out at road Identification, collision recognition, Traffic Sign Recognition etc. three aspect.In road Identification, collision
Identification aspect research is relatively early, also obtains many preferably results, but research is less in terms of pavement marking identification.Due to handing over
Many important transport information, the such as change of driving road ahead situation, pavement, rate limitation, driving are comprised in logical mark
The information such as is pointed in direction, provides these information to be conducive to driver's reaction in good time to driver it is ensured that driving safety in good time, it is to avoid
Traffic accident occurs, and has great importance.
Content of the invention
The invention aims to overcoming deficiency of the prior art, thus providing one kind to be based on orthogonal Gauss-
The traffic mark detection method of Hermit square.
A kind of traffic mark detection method based on orthogonal Gauss-Hermit square, comprises the following steps：
Step one：Obtain image I (x, y) in interest region；
Step 2：The coloured image of acquisition is converted into gray scale (256 grades) image f (x, y).
If 3 colouring components that (x, y) puts are (R, G, B), then
F (x, y)=0.3R (x, y)+0.59G (x, y)+0.11B (x, y), this method directly invokes built-in function
I (x, y)=rgb2gray (I)；
Step 3：Calculate OGHM with Y-direction respectively in X direction, i.e. the square in first calculated level directionWherein n=2k+1, k=0,1,2 ..., the subsequent vertical direction that calculates
Square, that is,a2i+1It is constant；In our algorithm, σ is taken as 5；Specifically real
In existing, first moment is replaced by we with the first-order difference of filtered image, third moment with filtered image first-order difference with
Linear group of third order difference to replace entirely；Third moment is also only used in our experiment；
Step 4：With Otus method, image is split；If the total pixel of image is N, gray level is L, and gray scale is i's
Pixel is NiIndividual；W (k) and u (k) is made to represent that 0 arrives probability and the average gray that the pixel of gray level k occurs respectively, Set as being divided into M pixel class C againj(there is M-1 thresholding 0≤t1<t2<...<tM-1
≤ L-1), divide the image into M pixel class Cj,Cj∈[tj-1+1,tj], j=1 ..., M, t0=0, tM=L-1. then CjOccur
Probability wj, average gray and variance are respectively：wj=w (tj)-w(tj-1),Then interior class variance is
Inter-class varianceTherefore dividing method is to make inter- object distance minimum and between class distance
Maximum threshold values group { t1,t2,t3,...,tM-1As M threshold values optimal threshold values group；As M=2, that is, it is divided into 2 classes, now
For maximizing σ2=ω0(μ-μ0)2+ω1(μ-μ1)2Obtain image segmentation thresholding T；And with T, binaryzation is implemented with image, that is,
Step 5：Quote morphologic filtering and remove noise, first carry out a computingThen carry out opening operation X
B, here B be taken as a little 8 connection.
Beneficial effect using technique scheme is：
The image that the present invention chooses in a traffic scene is tested, test result indicate that, the present invention is effective.Meanwhile,
Also by it, carried out than state more relatively with other methods (as the method for CANNY), and the effect difference of first moment detection is less；But three ranks
Square can use further, therefore its advantage embodies.
Brief description
Fig. 1 is detection method segmentation figure road original image figure.
Fig. 2 is detection method segmentation figure road result figure.
Fig. 3 is detection method segmentation figure label original image figure.
Fig. 4 is detection method segmentation figure label result figure.
Specific embodiment
A kind of traffic mark detection method based on orthogonal Gauss-Hermit square, comprises the following steps：
Step one：Obtain image I (x, y) in interest region；
Step 2：The coloured image of acquisition is converted into gray scale (256 grades) image f (x, y).
If 3 colouring components that (x, y) puts are (R, G, B), then
F (x, y)=0.3R (x, y)+0.59G (x, y)+0.11B (x, y), this method directly invokes built-in function
I (x, y)=rgb2gray (I)；
Step 3：Calculate OGHM with Y-direction respectively in X direction, i.e. the square in first calculated level directionWherein n=2k+1, k=0,1,2 ..., the subsequent vertical direction that calculates
Square, that is,a2i+1It is constant；In our algorithm, σ is taken as 5；Specifically real
In existing, first moment is replaced by we with the first-order difference of filtered image, third moment with filtered image first-order difference with
Linear group of third order difference to replace entirely；Third moment is also only used in our experiment；
Step 4：With Otus method, image is split；If the total pixel of image is N, gray level is L, and gray scale is i's
Pixel is NiIndividual；W (k) and u (k) is made to represent that 0 arrives probability and the average gray that the pixel of gray level k occurs respectively, Set as being divided into M pixel class C againj(there is M-1 thresholding 0≤t1<t2<...<tM-1
≤ L-1), divide the image into M pixel class Cj,Cj∈[tj-1+1,tj], j=1 ..., M, t0=0, tM=L-1. then CjOccur
Probability wj, average gray and variance are respectively：wj=w (tj)-w(tj-1),Then interior class variance is
Inter-class varianceTherefore dividing method is to make inter- object distance minimum and between class distance
Maximum threshold values group { t1,t2,t3,...,tM-1As M threshold values optimal threshold values group；As M=2, that is, it is divided into 2 classes, now
For maximizing σ2=ω0(μ-μ0)2+ω1(μ-μ1)2Obtain image segmentation thresholding T；And with T, binaryzation is implemented with image, that is,
Step 5：Quote morphologic filtering and remove noise, first carry out a computingThen carry out opening operation X B,
Here B is taken as 8 connections a little.
(A):Closed operation is a kind of elimination boundary point, makes the process that border is internally shunk；Can be used to eliminate little and nothing
The object of meaning；Algorithm is the structural element B with NxN, each pixel of scan image, two being covered with it with structural element
Value image does AND-operation, if being all 1, this pixel of result images is 1, otherwise for 0；
(B):Opening operation is that all background dots being contacted with object are merged in this object, makes border to outside expansion
Process.Can be used to fill up the cavity in object.Algorithm is the structural element B with NxN, each pixel knot of scan image
If it is all 0 that constitutive element and the bianry image that it covers do AND-operation, this pixel of result images is 0, otherwise for 1.
Orthogonal Gauss-Hermite square and fast algorithm
Square, such as geometric moment, orthogonal moment are widely used for pattern-recognition, image procossing, computer vision, many resolutions point
Analysis etc..
From the viewpoint of functional analysis, one can consider that geometric moment is the space that the base that signal forms in monomial generates
In projection, when we project a signal in a space when, we always wish that this space is orthogonal, its objective is
In order to reduce amount of calculation, and the reconstruct of signal can be conducive to.Orthogonal Gauss-Hermite square meets outside orthogonal requirement,
It smooths very much, and therefore it is to insensitive for noise.This is especially advantageous for effectively detecting moving target in environment made an uproar by band.Under
Face we will provide its definition, property and fast algorithm.
A) geometric moment
One-dimensional n rank geometric moment, MnX () is defined as follows:
Wherein [- w, w] is the interval of a 2w+1, and x is interval midpoint, and s (x) is exactly our signals to be analyzed.
Two-dimensional case is similar with one-dimensional, is defined as follows:
(x, y) is window (2w1+1)×(2w2+ 1) center, s (x, y) 2D is 2D signal, for example, piece image.
Geometric moment is the base 1, x that signal s (x) forms in monomial, x2,…,xN... the projection in the space of generation.
B) orthogonal Legendre moments
Legendre polynomial function can be used as one group of orthogonal basis .Legendre polynomial function and is defined as follows:
Here n is the exponent number of Legendre. according to this multinomial, we can define a yardstick Legendre multinomial
Function is：
Wherein n is the exponent number of yardstick Legendre polynomial function, and w>0.
C) Hermite square
The another one man cluster of orthogonal polynomial is Hermite multinomial. an orthogonal Hermite polynomial is:
Pn=Hn(t/σ) (5)
Wherein
Orthogonal Hermite n rank square MnX () is defined as:
Wherein s (x) is given signal.
Two-dimentional (p, q) rank Hermite square is defined as:
Wherein I (x, y) is image, Hp,q(u/ σ, v/ σ)=Hp(u/σ)Hq(v/σ).
It is true that two-dimentional (p, q) rank Hermite square equally can be melted into one-dimensional cascade to calculate.
D) orthogonal Gauss-Hermite square (OGHM)
The multinomial function base of the individual event function base of geometric moment, Hermite and Legendre all show one at the edge of window
Individual big jump (discontinuous). in order to preferably portray the local feature of signal, especially for noise signal, we are from smooth
Orthogonal moment .Gauss function is widely adopted as smoothing kernel. and therefore, orthogonal Hermite square is therefore suggested.
A given Gaussian function g (t, σ), the orthogonal Gauss-Hermiten rank square of signal s (x), Mn(s, S (x)), fixed
Justice is:
Wherein Bn(t)=g (t, σ) Pn(t),σ is that Gauss standard is poor, Pn(t)
It is Hermite polynomial function, Pn=Hn(t/ σ),
In order to calculate orthogonal Gauss-Hermite n rank square, we can use fast algorithm:
M0(x,s(m)(x))=g (x, σ) * s(m)(x)(9)
And
Mn(x,s(m)(x))=2 (n-1) Mn-2(x,s(m)(x))+2σMn-1(x,s(m+1)(x)) to n >=2 (11)
Wherein s(m)(x)=dm/dxms(x),s(0)X ()=s (x), " * " represents convolution. special circumstances:
M0(x, s (x))=g (x, σ) * s (x),Two-dimensional quadrature Gauss-Hermite rank square
(OGHM) it is defined as:
Wherein
Hp,q(u/ σ, v/ σ)=Hp(u/σ)Hq(v/σ),
I (x, y) is piece image.
In order to calculate Mp,q, we can be realized with one-dimensional series connection：
The property of 2.OGHM
In order to apply n rank orthogonal Gauss-Hermite square (OGHM) in rim detection, we demonstrate that their several property
Matter.
From a Gaussian wave filter, Wo Menyou:
For n rank orthogonal Gauss-Hermite square (OGHM), we can prove that following property:
Property 1
To given Gaussian function g (t, σ) and image I (x, y), have:
Here aiOnly relevant with σ.
Prove:We use mathematical induction.
To n=0, M0(x, I (x, y))=g (x, σ) * I (x, y) is it is clear that give a0=σ0=1.
To n=1, M1(x, I (x, y))=2 σ g(1)(x, σ) * I (x, y), this provides a0=0；a1=2 σ.
If to n≤k (k is integer k >=2), (16) proposition is correct, we demonstrate that proposition is also correct to n=k+1.
Because Mk+1(x, I (x, y))=2kMk-1(x,I(x,y))+2σMk(x,I(1)(x,y)),k≥1,
Therefore, according to formula (17) and formula (18), Wo Menyou:
This property illustrates the smoothing kernel of n rank orthogonal Gauss-Hermite squareIt is high
The linear combination of n order derivative, M are arrived in the 0 of this functionn(x, I (x, y)) is the linear of the derivative of gaussian filtering image on X- domain
Combination.
Property 2
To given Gaussian function g (t, σ) and image I (x, y)
Prove:Equally use mathematical induction.
This property illustrates even order orthogonal Gauss-Hermite square Mn(x, I (x, y)) is the gaussian filtering on X- domain
The linear combination of the even order derivative of image, and odd order orthogonal Gauss-Hermite square Mn(x, I (x, y) are the Gausses on X- domain
The linear combination of the odd order derivative of filtering image.
This is an important properties, and in our work, we will use it for edge inspection according to this property
Survey.
Property 3
Given Gaussian function g (t, σ) and image I (x, y) and its squareSo to maskFor, it is in area
Between n different zero crossing is existed on (- ∞, ∞).
Prove slightly.Fn(t, σ) is also the template of OGHM.
3 use OGHM to detect traffic mark
(1) Cleaning Principle
According to formula (16), (20) and (21), because OGHM is the convolution of the derivative of gaussian filtering on X- domain for the image
Linear combination, or perhaps the linear combination of the different X- differential of filtering image；And differential can be used for rim detection detection.
Because odd order OGHM is the linear combination of image convolution of odd order derivative of gaussian filtering on X- domain, in other words
It is the linear combination of the different odd order differential of filtering image, therefore odd order OGHM can be used for the traffic mark symbol in detection image
Number edge. contain Gaussian smoothing due in OGHM, thus compared with classic differential method, it have stronger anti-noise ability.
Claims (1)
1. a kind of traffic mark detection method based on orthogonal Gauss-Hermit square it is characterised in that：It comprises the following steps：
Step one：Obtain image I (x, y) in interest region；
Step 2：The coloured image of acquisition is converted into gray scale (256 grades) image f (x, y). setting 3 colouring components that (x, y) put is
(R, G, B), then
F (x, y)=0.3R (x, y)+0.59G (x, y)+0.11B (x, y), this method directly invoke built-in function I (x, y)=
rgb2gray(I)；
Step 3：Calculate OGHM with Y-direction respectively in X direction, i.e. the square in first calculated level directionWherein n=2k+1, k=0,1,2 ..., the subsequent vertical direction that calculates
Square, that is,a2i+1It is constant；In our algorithm, σ is taken as 5；Specifically real
In existing, first moment is replaced by we with the first-order difference of filtered image, third moment with filtered image first-order difference with
Linear group of third order difference to replace entirely；Third moment is also only used in our experiment；
Step 4：With Otus method, image is split；If the total pixel of image is N, gray level is L, and gray scale is the pixel of i
For NiIndividual；W (k) and u (k) is made to represent that 0 arrives probability and the average gray that the pixel of gray level k occurs respectively, Set as being divided into M pixel class C againj(there is M-1 thresholding 0≤t1<t2<...<tM-1≤ L-1), will scheme
As being divided into M pixel class Cj,Cj∈[tj-1+1,tj], j=1 ..., M, t0=0, tM=L-1. then CjThe probability w occurringj, averagely
Gray scale and variance are respectively：wj=w (tj)-w(tj-1),Then
Interior class variance isInter-class varianceTherefore
Dividing method is to make the threshold values group { t that inter- object distance is minimum and between class distance is maximum1,t2,t3,...,tM-1As M threshold values
Optimal threshold values group；As M=2, that is, it is divided into 2 classes, now for maximizing σ2=ω0(μ-μ0)2+ω1(μ-μ1)2Obtain image to divide
Cut thresholding T；And with T, binaryzation is implemented with image, that is,
Step 5：Quote morphologic filtering and remove noise, first carry out a computingThen carry out opening operation X B, here
B is taken as 8 connections a little.
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