CN102708589A - Three-dimensional target multi-viewpoint view modeling method on basis of feature clustering - Google Patents

Abstract

The invention relates to a three-dimensional target multi-viewpoint view modeling method on the basis of the feature clustering, which comprises four steps of: 1, acquiring an all attitude image of a test target; 2, extracting a target feature vector set <xi|i=1,2,...N> from an acquired all attitude image set and adopting a 7-dimensional invariant moment formed by integrating an affine invariant moment with a geometric invariant moment and further carrying out regularization to describe an appearance feature of the all attitude image; 3, determining an optimal cluster number Cop; and 4, clustering target feature vectors into the Cop type by adopting a k-means clustering method and using a small amount of finally obtained clustering centers as modeling results. Aiming at the defect that under different viewpoints, due to difference of target images, the single target view description cannot identify the target, the invention establishes the three-dimensional target multi-viewpoint view modeling method which has a small description number and little redundant information and can be used for well describing the target all-attitude feature vector set. The three-dimensional target multi-viewpoint view modeling method has high practical value and wide application prospect in the field of the pattern recognition.

Description

A kind of many viewpoints of objective view modeling method based on feature clustering

Technical field

The present invention relates to a kind of many viewpoints of objective view modeling method, belong to area of pattern recognition, be specifically related to aspects such as Target Recognition, Target Modeling and data yojan based on feature clustering.Be used for the modeling of many viewpoints of objective, be applicable to the problem that target image difference causes under the different points of view simple target view description can not recognition objective.

Background technology

Objective identification is important research direction in the computer vision field.It is often very difficult in practical application, to obtain Three-dimension Target information at present, and the image that the identification objective still mainly forms through recognition objective two-dimensional projection is accomplished.Target two-dimensional imaging (projection) process has caused partial information to be lost, and under different points of view, the appearance difference of complex target is obvious, makes that the objective identification difficulty of fast and stable is very big.

Through objective being carried out modeling, be one of means that address this problem to form comprehensive description to target shape.This just needs many viewpoints view modeling method of research objective.Idea is that the description to target image under a plurality of viewpoints is integrated the description as target intuitively.Yet for same target, its attitude number can't be exhaustive, also comprised a lot of redundant informations between different the description.Therefore, need in the redundant description of yojan, keep important description as far as possible.

The viewpoint spherical surface partitioning algorithm of classical many viewpoints of objective view modeling method based target geometry and visible relation, the geometry topological characteristic of main based target image, the modeling result that finally obtains is the colourful attitude characteristic view collection about target.But these class methods depend on the geometry and the characteristic of target to the structure of target signature view sets, generally can only like solid of revolution, quadric surface body etc., be difficult in the reality use to a certain type of certain objects of certain complexity.

Summary of the invention

The objective of the invention is to: a kind of many viewpoints of objective view modeling method based on feature clustering is provided; The simple target view description that it causes to target image difference under the different points of view can not recognition objective shortcoming, made up a kind of many viewpoints of objective view modeling method of describing that quantity is little, redundant information is few, can better describing the full posture feature vector set of target.

Its technical scheme is following:

A kind of many viewpoints of objective view modeling method of the present invention based on feature clustering, it may further comprise the steps:

Step 1: the full attitude image that obtains test target.Target is placed viewpoint ball center, and the targeted attitude variation is equivalent to the difference object observing of video camera on viewpoint spherical surface.Uniform sampling on viewpoint spherical surface, obtaining quantity is the full attitude image set of the objective of N.

Step 2: from the full attitude image set that obtains, extract target feature vector set { x _i| i==1,2 ... N}.Employing combines the 7 dimension invariant moments that form after affine invariant moments and geometric invariant moment and the further regularization and describes the full attitude image of target resemblance.

Step 3: confirm optimum cluster numbers C _OpThrough analyzing cluster total error quadratic sum J _eAnd the relation curve (J between the cluster numbers C _e-c curve) and J _e-c second derivative curve zero crossing is confirmed optimum cluster numbers C _Op

Step 4: adopt k-mean cluster method that target feature vector is gathered and be C _OpType, with a small amount of cluster centre that finally obtains as modeling result.

Wherein, the described full attitude image that obtains test target of step 1, concrete implementation procedure is following:

At first establishment need be carried out the target three-dimensional model of many viewpoints view modeling.Then object module is placed the centre of sphere of imaginary viewpoint ball, be equivalent to the difference object observing of video camera on viewpoint spherical surface, therefore obtain video camera and observe the process that the process of viewpoint is the division viewpoint spherical surface because targeted attitude changes.Adopt the mode of similar division earth longitude and latitude, evenly divide viewpoint spherical surface with same intervals, and at the division points place of correspondence to target imaging, can obtain the full attitude image set of target.

Wherein, the described affine invariant moments of step 2 is obtained by (1) formula, wherein μ _PqThe p+q rank central moment of presentation video.

$\left\{\begin{array}{c}{I}_1=\frac{({\mathrm{\&mu;}}_{20}{\mathrm{\&mu;}}_{02}-{\mathrm{\&mu;}}_{11}^2)}{{\mathrm{\&mu;}}_{00}^4}\\ I_2=\frac{{\mathrm{\&mu;}}_{30}^2{\mathrm{\&mu;}}_{03}^2-6{\mathrm{\&mu;}}_{30}{\mathrm{\&mu;}}_{03}{\mathrm{\&mu;}}_{21}{\mathrm{\&mu;}}_{12}+4{\mathrm{\&mu;}}_{30}{\mathrm{\&mu;}}_{12}^3+4{\mathrm{\&mu;}}_{21}^3{\mathrm{\&mu;}}_{03}-3{\mathrm{\&mu;}}_{12}^2{\mathrm{\&mu;}}_{21}^2}{{\mathrm{\&mu;}}_{00}^{10}}\\ I_3=\frac{{\mathrm{\&mu;}}_{20}({\mathrm{\&mu;}}_{21}{\mathrm{\&mu;}}_{03}-{\mathrm{\&mu;}}_{12}^2)-{\mathrm{\&mu;}}_{11}({\mathrm{\&mu;}}_{30}{\mathrm{\&mu;}}_{03}-{\mathrm{\&mu;}}_{21}{\mathrm{\&mu;}}_{12})+{\mathrm{\&mu;}}_{02}({\mathrm{\&mu;}}_{30}{\mathrm{\&mu;}}_{12}-{\mathrm{\&mu;}}_{21}^2)}{{\mathrm{\&mu;}}_{00}^7}\end{array}\right.---\left(1\right)$

Being used for carrying out preceding four components of comprehensive geometric invariant moment with above-mentioned affine invariant moments is obtained by (2) formula

$\left\{\begin{array}{c}{\mathrm{\&Phi;}}_1={\mathrm{\&eta;}}_{20}+{\mathrm{\&eta;}}_{02}\\ {\mathrm{\&Phi;}}_2={({\mathrm{\&eta;}}_{20}-{\mathrm{\&eta;}}_{02})}^2+{4\mathrm{\&eta;}}_{11}^2\\ {\mathrm{\&Phi;}}_3={({\mathrm{\&eta;}}_{30}-3{\mathrm{\&eta;}}_{12})}^2+{(3{\mathrm{\&eta;}}_{21}-{\mathrm{\&eta;}}_{03})}^2\\ {\mathrm{\&Phi;}}_4={({\mathrm{\&eta;}}_{30}+{\mathrm{\&eta;}}_{12})}^2+{({\mathrm{\&eta;}}_{21}+{\mathrm{\&eta;}}_{03})}^2\end{array}\right.---\left(2\right)$

For solving the difference of the order of magnitude between above-mentioned invariant moments, need further above-mentioned invariant moments to be carried out the regularization computing

$\stackrel{\&OverBar;}{I_k}=\mathrm{sign}\left(I_k\right){\left|I_k\right|}^{2/S}---\left(3\right)$

I in the formula _kThe combination invariant moments of representing not regularization,

Expression regularization combination invariant moments, Sign representes Heaviside function, S representes the summation of square factor exponent number in each square component.Finally obtain describing 7 dimension regularization invariant moments of target shape information.

Wherein, the described definite optimum cluster numbers C of step 3 _Op, concrete implementation procedure is following:

Adopt k-mean cluster method that the full posture feature vector set of above-mentioned target is carried out cluster, making classification count c progressively increases, c=2, and c=3 ..., write down the total error quadratic sum J of each cluster _e, final according to obtaining J _e-c curve further calculates J _e-c second derivative curve under the situation of considering the certain numerical value fluctuation, is selected J _eOn-c second derivative the curve first approach zero point and relatively the mid point between flat zone as the cluster numbers c of optimum _OpEstimated value.

The advantage that the present invention has is: through feature clustering, represent the distribution of the full posture feature vector of target in feature space with the minority cluster centre, realized the data yojan, the method highly versatile can conveniently be confirmed optimum cluster numbers.

Description of drawings

Fig. 1 is a processing flow chart of the present invention;

The viewpoint ball synoptic diagram of Fig. 2 for relating among the present invention;

Fig. 3 is J among the present invention _e-c curve synoptic diagram;

Fig. 4 confirms optimum cluster numbers C for the present invention _OpSynoptic diagram.

Embodiment

The present invention is directed to many viewpoints of objective view modeling problem, proposed a kind of many viewpoints of objective view modeling method based on Support Vector data description.

See Fig. 1, a kind of many viewpoints of objective view modeling method of the present invention based on feature clustering, it may further comprise the steps:

Step 1: the full attitude image that obtains test target.Target is placed viewpoint ball center, and the targeted attitude variation is equivalent to the difference object observing of video camera on viewpoint spherical surface.Uniform sampling on viewpoint spherical surface, obtaining quantity is the full attitude image set of the objective of N.

Step 2: from the full attitude image set that obtains, extract target feature vector set { x _i| i=1,2 ... N}.Employing combines the 7 dimension invariant moments that form after affine invariant moments and geometric invariant moment and the further regularization and describes the full attitude image of target resemblance.

Step 3: confirm optimum cluster numbers C _OpThrough analyzing cluster total error quadratic sum J _eAnd the relation curve (J between the cluster numbers C _e-c curve) and J _e-c second derivative curve zero crossing is confirmed optimum cluster numbers C _Op

Step 4: adopt k-mean cluster method that target feature vector is gathered and be C _OpType, with a small amount of cluster centre that finally obtains as modeling result.

At first, obtain the full attitude image of target and need create the target three-dimensional model of treating modeling.See Fig. 2, object module is placed the centre of sphere of viewpoint ball.The viewpoint ball is the imaginary unit's ball that is defined in the three dimensional euclidean space; Can to regard target as be the process of starting point to the different directions projection with the centre of sphere to the imaging process of target under the different points of view; The corresponding projecting direction vector of every bit P P sees target along the rectangular projection of projecting direction P corresponding to P point on viewpoint spherical surface on the viewpoint spherical surface.Therefore obtain video camera and observe the process that the process of viewpoint is the division viewpoint spherical surface.Adopt the mode of similar division earth longitude and latitude, evenly divide viewpoint spherical surface with same intervals, and at the division points place of correspondence to target imaging, can obtain element number is the full attitude image set of target of N.

The attitude image set is extracted target signature, obtain to describe the proper vector set { x of the full attitude appearance information of this target _i| i=1,2 ... N}.The 7 dimension invariant moments that adopted comprehensive affine invariant moments and geometric invariant moment to form are described target shape.Affine invariant moments is obtained by (1) formula, wherein μ _PqThe p+q rank central moment of presentation video.

$\left\{\begin{array}{c}{I}_1=\frac{({\mathrm{\&mu;}}_{20}{\mathrm{\&mu;}}_{02}-{\mathrm{\&mu;}}_{11}^2)}{{\mathrm{\&mu;}}_{00}^4}\\ I_2=\frac{{\mathrm{\&mu;}}_{30}^2{\mathrm{\&mu;}}_{03}^2-6{\mathrm{\&mu;}}_{30}{\mathrm{\&mu;}}_{03}{\mathrm{\&mu;}}_{21}{\mathrm{\&mu;}}_{12}+4{\mathrm{\&mu;}}_{30}{\mathrm{\&mu;}}_{12}^3+4{\mathrm{\&mu;}}_{21}^3{\mathrm{\&mu;}}_{03}-3{\mathrm{\&mu;}}_{12}^2{\mathrm{\&mu;}}_{21}^2}{{\mathrm{\&mu;}}_{00}^{10}}\\ I_3=\frac{{\mathrm{\&mu;}}_{20}({\mathrm{\&mu;}}_{21}{\mathrm{\&mu;}}_{03}-{\mathrm{\&mu;}}_{12}^2)-{\mathrm{\&mu;}}_{11}({\mathrm{\&mu;}}_{30}{\mathrm{\&mu;}}_{03}-{\mathrm{\&mu;}}_{21}{\mathrm{\&mu;}}_{12})+{\mathrm{\&mu;}}_{02}({\mathrm{\&mu;}}_{30}{\mathrm{\&mu;}}_{12}-{\mathrm{\&mu;}}_{21}^2)}{{\mathrm{\&mu;}}_{00}^7}\end{array}\right.$

Being used for carrying out preceding four components of comprehensive geometric invariant moment with above-mentioned affine invariant moments is obtained by (2) formula

$\left\{\begin{array}{c}{\mathrm{\&Phi;}}_1={\mathrm{\&eta;}}_{20}+{\mathrm{\&eta;}}_{02}\\ {\mathrm{\&Phi;}}_2={({\mathrm{\&eta;}}_{20}-{\mathrm{\&eta;}}_{02})}^2+{4\mathrm{\&eta;}}_{11}^2\\ {\mathrm{\&Phi;}}_3={({\mathrm{\&eta;}}_{30}-3{\mathrm{\&eta;}}_{12})}^2+{(3{\mathrm{\&eta;}}_{21}-{\mathrm{\&eta;}}_{03})}^2\\ {\mathrm{\&Phi;}}_4={({\mathrm{\&eta;}}_{30}+{\mathrm{\&eta;}}_{12})}^2+{({\mathrm{\&eta;}}_{21}+{\mathrm{\&eta;}}_{03})}^2\end{array}\right.$

For solving the difference of the order of magnitude between above-mentioned invariant moments, need further above-mentioned invariant moments to be carried out the regularization computing

$\stackrel{\&OverBar;}{I_k}=\mathrm{sign}\left(I_k\right){\left|I_k\right|}^{2/S}$

Wherein Ik representes the combination invariant moments of not regularization;

expression regularization combination invariant moments; Sign representes Heaviside function, and S representes the summation of square factor exponent number in each square component.For example for second component Ф of geometric invariant moment ₂, S=(2+0) * 2=4, wherein (2+0) expression square factor η ₂₀Exponent number with, * 2 the expression to square factor η ₂₀Power operation.

7 dimension regularization invariant moments of target shape information have finally been obtained describing.

The full posture feature vector set of above-mentioned target is carried out cluster, with a small amount of cluster centre that finally obtains as modeling result.Adopted k-mean cluster method.The basis of k-average is the error sum of squares criterion.If N _iBe i cluster Γ _iIn number of samples, m _iIt is the average of these samples.Γ _iIn each sample y and average m _iBetween error sum of squares after to all types addition be:

$J_e=\underset{i=1}{\overset{c}{\mathrm{\&Sigma;}}}\underset{y\&Element;{\mathrm{\&Gamma;}}_i}{\mathrm{\&Sigma;}}{\left|\right|y-m_i\left|\right|}^2---\left(4\right)$

J _eMeasured with c cluster centre m ₁, m ₂..., m _cThe total square-error that is produced when representing c sample subclass.To different cluster J _eValue be different, make J _eMinimum cluster is the optimal result under the error sum of squares criterion.

Adopt following method to confirm optimum cluster numbers c _Op: see shown in Figure 3ly, making classification count c progressively increases, to c=2, and c=3 ... use the k-mean algorithm respectively, error sum of squares will reduce along with the increase of c in theory.Then there is certain critical point OP, when cluster numbers is c _OpWhen individual, J _eC is increased to c from 1 along with cluster numbers _OpAnd reduce J when c increases again rapidly _eThough reduce to some extent, the speed that reduces is slowed down.See shown in Figure 4, according to calculating J _e-c curve further calculates J _e-c second derivative curve under the situation of considering the certain numerical value fluctuation, is selected J _eOn-c second derivative the curve first approach zero point and relatively the mid point between flat zone as the cluster numbers c of optimum _OpEstimated value.

Finally, adopting the k-means clustering algorithm, is c with the full posture feature vector set of target cluster _OpIndividual classification, with the cluster centre of each sub-category as many viewpoints of objective view modeling result.

Claims (4)

1. many viewpoints of objective view modeling method based on feature clustering, it is characterized in that: it may further comprise the steps:

Step 1: the full attitude image that obtains test target; Target is placed viewpoint ball center, and the targeted attitude variation is equivalent to the difference object observing of video camera on viewpoint spherical surface, uniform sampling on viewpoint spherical surface, and obtaining quantity is the full attitude image set of the objective of N;

Step 2: from the full attitude image set that obtains, extract target feature vector set { x _i| i=1,2 ... N}, employing combines the 7 dimension invariant moments that form after affine invariant moments and geometric invariant moment and the further regularization and describes the full attitude image of target resemblance;

Step 3: confirm optimum cluster numbers C _OpThrough analyzing cluster total error quadratic sum J _eAnd the relation curve between the cluster numbers C is J _e-c curve and J _e-c second derivative curve zero crossing is confirmed optimum cluster numbers C _Op

Step 4: adopt k-mean cluster method that target feature vector is gathered and be C _OpType, with a small amount of cluster centre that finally obtains as modeling result.

2. a kind of many viewpoints of objective view modeling method according to claim 1 based on feature clustering; It is characterized in that: the described full attitude image that obtains test target of step 1; Concrete implementation procedure is following: at first establishment need be carried out the target three-dimensional model of many viewpoints view modeling; Then object module is placed the centre of sphere of imaginary viewpoint ball; Be equivalent to the difference object observing of video camera on viewpoint spherical surface because targeted attitude changes, therefore obtain video camera and observe the process that the process of viewpoint is the division viewpoint spherical surface; Adopt the mode of similar division earth longitude and latitude, evenly divide viewpoint spherical surface with same intervals, and at the division points place of correspondence to target imaging, promptly obtain the full attitude image set of target.

3. a kind of many viewpoints of objective view modeling method based on feature clustering according to claim 1, it is characterized in that: the described affine invariant moments of step 2 is obtained by (1) formula, wherein μ _PqThe p+q rank central moment of presentation video;

$\left\{\begin{array}{c}{I}_1=\frac{({\mathrm{\&mu;}}_{20}{\mathrm{\&mu;}}_{02}-{\mathrm{\&mu;}}_{11}^2)}{{\mathrm{\&mu;}}_{00}^4}\\ I_2=\frac{{\mathrm{\&mu;}}_{30}^2{\mathrm{\&mu;}}_{03}^2-6{\mathrm{\&mu;}}_{30}{\mathrm{\&mu;}}_{03}{\mathrm{\&mu;}}_{21}{\mathrm{\&mu;}}_{12}+4{\mathrm{\&mu;}}_{30}{\mathrm{\&mu;}}_{12}^3+4{\mathrm{\&mu;}}_{21}^3{\mathrm{\&mu;}}_{03}-3{\mathrm{\&mu;}}_{12}^2{\mathrm{\&mu;}}_{21}^2}{{\mathrm{\&mu;}}_{00}^{10}}\\ I_3=\frac{{\mathrm{\&mu;}}_{20}({\mathrm{\&mu;}}_{21}{\mathrm{\&mu;}}_{03}-{\mathrm{\&mu;}}_{12}^2)-{\mathrm{\&mu;}}_{11}({\mathrm{\&mu;}}_{30}{\mathrm{\&mu;}}_{03}-{\mathrm{\&mu;}}_{21}{\mathrm{\&mu;}}_{12})+{\mathrm{\&mu;}}_{02}({\mathrm{\&mu;}}_{30}{\mathrm{\&mu;}}_{12}-{\mathrm{\&mu;}}_{21}^2)}{{\mathrm{\&mu;}}_{00}^7}\end{array}\right.---\left(1\right)$

Being used for carrying out preceding four components of comprehensive geometric invariant moment with above-mentioned affine invariant moments is obtained by (2) formula

$\left\{\begin{array}{c}{\mathrm{\&Phi;}}_1={\mathrm{\&eta;}}_{20}+{\mathrm{\&eta;}}_{02}\\ {\mathrm{\&Phi;}}_2={({\mathrm{\&eta;}}_{20}-{\mathrm{\&eta;}}_{02})}^2+{4\mathrm{\&eta;}}_{11}^2\\ {\mathrm{\&Phi;}}_3={({\mathrm{\&eta;}}_{30}-3{\mathrm{\&eta;}}_{12})}^2+{(3{\mathrm{\&eta;}}_{21}-{\mathrm{\&eta;}}_{03})}^2\\ {\mathrm{\&Phi;}}_4={({\mathrm{\&eta;}}_{30}+{\mathrm{\&eta;}}_{12})}^2+{({\mathrm{\&eta;}}_{21}+{\mathrm{\&eta;}}_{03})}^2\end{array}\right.---\left(2\right)$

For solving the difference of the order of magnitude between above-mentioned invariant moments, need further above-mentioned invariant moments to be carried out the regularization computing

$\stackrel{\&OverBar;}{I_k}=\mathrm{sign}\left(I_k\right){\left|I_k\right|}^{2/S}---\left(3\right)$

In the formula, I _kThe combination invariant moments of representing not regularization,

Expression regularization combination invariant moments, Sign representes Heaviside function, S representes the summation of square factor exponent number in each square component, finally obtains describing 7 dimension regularization invariant moments of target shape information.

4. a kind of many viewpoints of objective view modeling method based on feature clustering according to claim 1 is characterized in that: the described definite optimum cluster numbers C of step 3 _Op, concrete implementation procedure is following:

Adopt k-mean cluster method that the full posture feature vector set of above-mentioned target is carried out cluster, making classification count c progressively increases, c=2, and c=3 ..., write down the total error quadratic sum J of each cluster _e, final according to obtaining J _e-c curve further calculates J _e-c second derivative curve under the situation of considering the certain numerical value fluctuation, is selected J _eOn-c second derivative the curve first approach zero point and relatively the mid point between flat zone as the cluster numbers c of optimum _OpEstimated value.

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