CN111341392A - Multi-domain material volume data internal structure characteristic expression method - Google Patents

Multi-domain material volume data internal structure characteristic expression method Download PDF

Info

Publication number
CN111341392A
CN111341392A CN202010121247.4A CN202010121247A CN111341392A CN 111341392 A CN111341392 A CN 111341392A CN 202010121247 A CN202010121247 A CN 202010121247A CN 111341392 A CN111341392 A CN 111341392A
Authority
CN
China
Prior art keywords
sub
skeleton
volume data
interfaces
interface
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010121247.4A
Other languages
Chinese (zh)
Other versions
CN111341392B (en
Inventor
王映辉
张缓缓
薛香莲
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xian University of Technology
Original Assignee
Xian University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xian University of Technology filed Critical Xian University of Technology
Priority to CN202010121247.4A priority Critical patent/CN111341392B/en
Publication of CN111341392A publication Critical patent/CN111341392A/en
Application granted granted Critical
Publication of CN111341392B publication Critical patent/CN111341392B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/80—Data visualisation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Landscapes

  • Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Chemical & Material Sciences (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computing Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Image Generation (AREA)
  • Processing Or Creating Images (AREA)

Abstract

The invention discloses a multi-domain material volume data internal structure characteristic expression method, which is implemented according to the following steps: step 1, acquiring a volume data sub-interface, analyzing the correlation among the volume data sub-interfaces, and constructing a directed skeleton tree; step 2, representing the skeleton shape characteristics of the volume data; step 3, representing the ridge and valley shape characteristics of the volume data; and 4, constructing a tree structure topological graph and performing vector representation on the tree structure topological graph to realize comprehensive and effective representation of the three-dimensional space characteristics of the internal structure of the volume data. The method for expressing the internal structure characteristics of the multi-domain material volume data realizes complete description of the shape characteristics of the boundary surface of the volume data.

Description

Multi-domain material volume data internal structure characteristic expression method
Technical Field
The invention belongs to the technical field of three-dimensional visualization, and relates to a method for expressing internal structural features of multi-domain material volume data.
Background
The expression of the internal structural features of the multi-domain material volume data plays an important role in the aspects of identification, understanding, retrieval and the like of the volume data, and at present, the research aiming at the internal structure of the multi-domain material volume data only focuses on the identification and extraction of boundary surfaces and boundary surface point sets of materials of a multi-domain object, but neglects the effective expression of the boundary surfaces. Because of the diversity among the materials of the volume data and the complexity of the internal structure thereof, if a plurality of interfaces among a plurality of materials are extracted and observed at the same time, the expression of the topological relation of the interfaces cannot be separated; in addition, the internal feature analysis of the volume data cannot be separated from the establishment of the topological structure, which is also a significant defect of the current interface extraction method, so that the expression of the internal feature of the volume data by using the topological relation is one of the focuses of the current researchers. However, the internal structure expression method of volume data based on topological relations is rarely studied, and the existing method is reviewed below.
The research results of the teaching group of the university Huishu people S.S. Huang, H.Fu, L.Y.Wei, actual.support substructures, support-induced part-level structural representation [ J ]. IEEE Transactions on Visualization and Computer Graphics,2016,22(8):2024-, the characteristics of the sub-interfaces cannot be expressed.
The skeleton is used as a special expression form of the shape based on the skeleton characteristic expression method, the topological connectivity of the model can be intuitively reflected, and the internal shape characteristic of the volume data sub-interface can be effectively described. J.M.Reddy, G.M.Turkiyah.computation of 3D skeletons using a generated delayed analysis technique [ J ]. Computer-air designed Design,1995,27(9):677-694. The method comprises the steps of constructing a continuous function, calculating a function value of each vertex of the model, clustering the vertexes with the same function value, and finally aggregating the vertexes of the same category according to a clustering result to obtain a skeleton structure diagram of the object, wherein the skeleton structure diagram comes from Y.Shinagawa, T.L.Kunii.structural aRoebgraph and automatic mechanics from mechanics sections [ J ]. IEEE Computer Graphics and applications,2002,11(6):44-51. although a skeleton obtaining method based on topology and geometric analysis obtains a certain result, the topological structure of the skeleton of a columnar sub-interface is always the same, so that the analysis is performed only by utilizing the skeleton characteristic interface, the analysis of the local characteristic of the effective interface is difficult, and the method generally needs more support shearing processing and needs to manually input shearing parameters, the ideal skeleton is obtained by continuously adjusting the size of the parameters, and the experience of an operator is excessively depended on.
Based on the ridge-valley characteristic expression method, the ridge-valley characteristics can well depict the local convex-concave change of the surface of an object, effectively describe the local shape characteristics of a model curved surface, represent the characteristics of a volume data sub-interface by the ridge-valley characteristics and well show the external convex-concave local shape characteristics of the interface; most of the existing ridge-valley feature extraction methods are ridge-valley extraction based on the idea of curvature calculation and feature point construction, and are mainly classified into methods based on surface fitting and methods based on principal component analysis. The method comprises the steps of firstly calculating the curvature of each point according to a local least square fitting surface polynomial of each point, and marking the curvature with a larger absolute value as a potential ridge and valley characteristic point by selecting the curvature with a larger absolute value; then projecting the marked feature points to the nearest potential feature line to obtain enhanced feature points; finally, smoothing the enhanced feature points to obtain ridge-valley points, and generating ridge-valley lines by selecting proper smooth points; and then correcting and optimizing the ridge-valley line to obtain a smooth ridge-valley line. Judd, F.Durand, E.Adelson.apparatus edges for line drawing [ J ]. ACM Transactions on Graphics,2007,26(3): Article No.19. the method extracts the ridge and valley lines from the shadow model by using a Principal Component Analysis (PCA) and high-order derivatives, and the method better extracts the ridge and valley characteristics of the model. The curved surface ridge-valley characteristics can well represent the convex-concave degree of a curved surface, can well describe the shape characteristics of a model, most of the existing ridge-valley extraction methods are based on a grid model, but in practical application, the model is easier to obtain based on a point set, and the research on extracting the local characteristics of the curved surface based on the point set is less. Although the point set can be gridded first and then the local feature extraction of the curved surface ridge and valley is performed, the complexity of calculation is greatly increased, and in addition, the interface for acquiring the volume data is a scattered point set, and a ridge and valley feature extraction method based on a grid method cannot be directly applied, so further research is needed.
In summary, the oriented skeleton tree representation-based feature information of the number of the materials of the volume data and the topological relation between the interfaces, the skeleton representation-based feature information of the internal trend of the sub-interfaces, and the ridge-valley representation-based feature information of the external convex-concave local features of the sub-interfaces, all of which cannot completely and specifically express the structural features of the volume data.
Disclosure of Invention
The invention aims to provide a method for expressing internal structural features of multi-domain material volume data, which realizes complete description of shape features of a boundary surface of the volume data.
The technical scheme adopted by the invention is that the method for expressing the internal structural characteristics of the multi-domain material volume data is implemented according to the following steps:
step 1, acquiring a volume data sub-interface, analyzing the correlation among the volume data sub-interfaces, and constructing a directed skeleton tree;
step 2, representing the skeleton shape characteristics of the volume data;
step 3, representing the ridge and valley shape characteristics of the volume data;
and 4, constructing a tree structure topological graph and performing vector representation on the tree structure topological graph to realize comprehensive and effective representation of the three-dimensional space characteristics of the internal structure of the volume data.
The present invention is also characterized in that,
analyzing the mutual relation among the volume data sub-interfaces in the step 1, and specifically constructing the directed skeleton tree as follows:
step 1.1, determining the number of sub-interfaces and the relation among the sub-interfaces based on the acquired volume data sub-interfaces, wherein the relation among the sub-interfaces comprises, is adjacent to and is separated from each other;
step 1.2, mapping the inclusion relationship between the child interfaces to a directed skeleton tree to be converted into a parent-child relationship, wherein the parent-child relationship is represented by a solid line of a one-way arrow, if the child interface A contains the child interface B, the A is that the parent node B is a child node, and the parent-child relationship is represented as A → B;
mapping the adjacent relation between the sub-interfaces to a directed skeleton tree to be converted into a brother relation, wherein the brother relation is represented by a dotted line of a bidirectional arrow, if the sub-interface A is adjacent to the interface B, the A and the B are in a brother relation, and the brother relation is represented as A ← … → B;
the phase-separation relation between the sub-interfaces is not shown;
step 1.3, constructing a directed skeleton tree, wherein the directed skeleton tree is represented as G ═<P,E,AP,AE>Wherein P is a node set in the directed skeleton tree and represents a sub-interface; e is an edge set in the directed skeleton tree, and represents a topological relation among the interfaces of the volume data sub-interfaces; a. thePThe node attribute characteristics represent the number of the sub-interfaces; a. theEIs an edge attribute feature, namely AE＝{Ene，Ein，EdisIn which EneThe adjacent relation of the sub-interfaces is expressed by the expression method of the adjacent relation edges in the step 1.2; einThe subsurfaces contain relations, and the relations are represented by a representation method containing relation edges in step 1.2; edisRepresenting the phase separation relation of the sub-interfaces, and not carrying out edge representation.
The step 2 specifically comprises the following steps:
step 2.1: the skeleton characteristics of each sub-interface are obtained, the skeleton characteristics are analyzed, and the number of skeleton endpoints, the number of skeleton bifurcation nodes and the total number of skeleton branches of each sub-interface are determined;
step 2.2: according to the number of the skeleton endpoints, the number of the skeleton bifurcation nodes and the total number of the skeleton branches determined in the step 2.1, expressing the skeleton shape feature vector of the volume data, wherein the skeleton shape feature vector of the volume data is as follows: t ═ T1T2T3...Tn]，TiDenotes the framework characteristic of the ith subinterface, i ═ 1.2.3i＝[B F N]B represents the number of skeleton endpoints corresponding to the ith sub-interface, F represents the number of skeleton bifurcation nodes corresponding to the ith sub-interface, and N represents the total number of skeleton branches corresponding to the ith sub-interface.
The step 3 specifically comprises the following steps:
step 3.1, respectively extracting all coordinates of ridge points and valley points of each sub-interface, and obtaining eight vertex coordinates of the minimum direction bounding box, namely (X)min,Ymin,Zmin)、(Xmax,Ymin,Zmin)、(Xmin,Ymax,Zmin)、(Xmax,Ymax,Zmin)、(Xmin,Ymin,Zmax)、(Xmax,Ymin,Zmax)、(Xmin,Ymax,Zmax)、(Xmax,Ymax,Zmax) Wherein X ismin，Xmax，Ymin，Ymax，Zmin，ZmaxRespectively representing the minimum coordinate and the maximum coordinate of the minimum bounding box of the point set on an X axis, the minimum coordinate and the maximum coordinate of the minimum bounding box of the point set on a Y axis, the minimum coordinate and the maximum coordinate of the minimum bounding box of the point set on a Z axis;
step 3.2, the whole bounding box is equally divided to obtain each grid, and the grids are numbered to respectively realize three-dimensional rasterization of ridge points and valley points of the sub-interfaces;
step 3.3, determining a spatial density histogram according to the number of ridge points and valley points in each grid and the number of total ridge points and valley points of the sub-interface respectively, wherein the density histogram is a one-dimensional discrete function, and the calculation is shown as a formula (2):
Figure BDA0002393028640000061
wherein f is the total ridge point or valley point number of the sub-interface, NiThe number of ridge points or valley points in the ith grid is;
step 3.4, calculating the distances from all ridge points and valley points in each grid subjected to three-dimensional rasterization to the point set centroid of the sub-interface respectively, obtaining the average distance in each grid, and generating distance feature histograms of the ridge points and the valley points respectively;
and 3.5, expressing the ridge and valley shape characteristic vector of the volume data: the ridge and valley shape feature vector of the volume data is: h ═ H1rH1v；H2rH2v；...；HnrHnv]，Hnr＝[ρrdr]Features of density histogram and distance histogram of ridge points representing a sub-interface, prIndicating the density of the ridge points corresponding to the sub-boundary surface, drIndicates the distance of the ridge point corresponding to the sub-boundary surface, Hnv＝[ρvdv]Features of density histogram and distance histogram representing a sub-boundary valley point, pvIndicating the density of the corresponding valleys of the sub-boundary surface, dvIndicating the distance of the ridge point corresponding to the sub-interface.
The step 4 specifically comprises the following steps:
step 4.1, determining the characteristic expressions of the oriented skeleton tree, the skeleton of the subinterface and the ridge and valley of the subinterface in the volume data and constructing a tree topology map of the data on the basis of the step 1-3;
and 4.2, the feature vector of the whole tree structure topological graph is M ═ Tr T H, wherein Tr is the feature vector of the volume data directed skeleton tree, T is the skeleton shape feature vector of the volume data, and H is the volume data ridge and valley vector.
The characteristic vector Tr ═ N of the volume data directed skeleton treePNEneNEinNEdis]In which N ispRepresenting the number of the sub-interfaces; n is a radical ofEneNumber, N, representing the sub-boundary surface adjacency relationshipEinNumber, N, representing containment relationship of sub-interfacesEdisThe number of the phase separation relationships between the sub-interfaces is shown.
The characteristic vector Tr ═ N of the volume data directed skeleton treePNEneNEinNEdis]The method is obtained by inquiring and reading a spatial incidence matrix of the directed skeleton tree, wherein the spatial incidence matrix of the directed skeleton tree is as follows:
Figure BDA0002393028640000071
wherein, PiAnd PjThe ith and jth subinterfaces are respectively shown, and since the subinterface boundaries are in an inclusion relationship, psi (i, j) ═ 2 and psi (i, j) ═ 2 are in a one-to-one correspondence relationship in the spatial relationship matrix, only one is taken when the vector is acquired.
The invention has the beneficial effects that:
the invention discloses a multi-domain material volume data internal structure feature expression method, which comprises the steps of analyzing the number and the mutual relation of sub-interfaces based on the sub-interfaces of acquired volume data, constructing a directed skeleton tree, and constructing a volume data tree structure topological graph with three-dimensional features based on the acquired sub-interface skeleton shape features and the ridge and valley shape features thereof, so that the complete description of the volume data boundary surface shape features is realized, and further, three-dimensional shape feature information is provided for the identification, understanding and retrieval of the volume data.
Drawings
FIG. 1 is a schematic diagram of the relationship of the neutron interface in the method for expressing the internal structural features of multi-domain material volume data according to the present invention
FIG. 2 is a directed skeleton tree and a matrix representation thereof in the multi-domain material volume data internal structure feature expression method of the present invention
FIG. 3 is a schematic diagram of rasterization of neutron interface ridge-valley points in the multi-domain material volume data internal structure feature expression method of the present invention;
FIG. 4 is a schematic diagram of tree structure topology in the method for expressing internal structural features of multi-domain material volume data according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention relates to a multi-domain material volume data internal structure characteristic expression method, which is implemented according to the following steps:
step 1, acquiring a volume data sub-interface, analyzing the correlation among the volume data sub-interfaces, and constructing a directed skeleton tree; the directed skeleton tree expresses the topological relation of the body data sub-interfaces, the directed skeleton tree is an expanded tree, the branch nodes of the directed skeleton tree represent a bounded region of a three-dimensional space and are represented by hollow dots, a sub-interface of body data is represented by branches of the directed skeleton tree, and the branches of the directed skeleton tree represent the connection relation between the body data sub-interfaces and are represented by edges; the method comprises the following steps of analyzing the mutual relation among the sub-interfaces of the volume data, and specifically constructing the directed skeleton tree:
step 1.1, determining the number of sub-interfaces and the relationship among the sub-interfaces based on the acquired volume data sub-interfaces, wherein the relationship among the sub-interfaces comprises, is adjacent to and is separated from each other, as shown in figure 1;
step 1.2, mapping the inclusion relationship between the child interfaces to a directed skeleton tree to be converted into a parent-child relationship, wherein the parent-child relationship is represented by a solid line of a one-way arrow, if the child interface A contains the child interface B, the A is that the parent node B is a child node, and the parent-child relationship is represented as A → B;
mapping the adjacent relation between the sub-interfaces to a directed skeleton tree to be converted into a brother relation, wherein the brother relation is represented by a dotted line of a bidirectional arrow, if the sub-interface A is adjacent to the interface B, the A and the B are in a brother relation, and the brother relation is represented as A ← … → B;
the phase-separation relation between the sub-interfaces is not shown;
step 1.3, constructing a directed skeleton tree, wherein the directed skeleton tree is represented as G ═<P,E,AP,AE>Wherein P is a node set in the directed skeleton tree and represents a sub-interface; e is an edge set in the directed skeleton tree, and represents a topological relation among the interfaces of the volume data sub-interfaces; a. thePThe node attribute characteristics represent the number of the sub-interfaces; a. theEIs an edge attribute feature, namely AE＝{Ene，Ein，EdisIn which EneThe adjacent relation of the sub-interfaces is expressed by the expression method of the adjacent relation edges in the step 1.2; einThe subsurfaces contain relations, and the relations are represented by a representation method containing relation edges in step 1.2; edisRepresenting the phase separation relation of the sub-interfaces without edge representation;
step 2, representing the skeleton shape characteristics of the volume data; the method specifically comprises the following steps:
step 2.1: the skeleton characteristics of each sub-interface are obtained, the skeleton characteristics are analyzed, and the number of skeleton endpoints, the number of skeleton bifurcation nodes and the total number of skeleton branches of each sub-interface are determined;
step 2.2: according to the number of the skeleton endpoints, the number of the skeleton bifurcation nodes and the total number of the skeleton branches determined in the step 2.1, expressing the skeleton shape feature vector of the volume data, wherein the skeleton shape feature vector of the volume data is as follows: t ═ T1T2T3...Tn]，TiDenotes the framework characteristic of the ith subinterface, i ═ 1.2.3i＝[B F N]B represents the number of skeleton endpoints corresponding to the ith sub-interface, F represents the number of skeleton bifurcation nodes corresponding to the ith sub-interface, and N represents the total number of skeleton branches corresponding to the ith sub-interface;
step 3, representing the ridge and valley shape characteristics of the volume data; the method specifically comprises the following steps:
step 3.1, respectively extracting all coordinates of ridge points and valley points of each sub-interface, and obtaining eight vertex coordinates of the minimum direction bounding box, namely (X)min,Ymin,Zmin)、(Xmax,Ymin,Zmin)、(Xmin,Ymax,Zmin)、(Xmax,Ymax,Zmin)、(Xmin,Ymin,Zmax)、(Xmax,Ymin,Zmax)、(Xmin,Ymax,Zmax)、(Xmax,Ymax,Zmax) Wherein X ismin，Xmax，Ymin，Ymax，Zmin，ZmaxRespectively representing the minimum coordinate and the maximum coordinate of the minimum bounding box of the point set on an X axis, the minimum coordinate and the maximum coordinate of the minimum bounding box of the point set on a Y axis, the minimum coordinate and the maximum coordinate of the minimum bounding box of the point set on a Z axis;
step 3.2, equally dividing the whole bounding box to obtain each grid, numbering each grid, and respectively realizing three-dimensional rasterization of ridge points and valley points of the sub-interfaces, as shown in fig. 3;
step 3.3, determining a spatial density histogram according to the number of ridge points and valley points in each grid and the number of total ridge points and valley points of the sub-interface respectively, wherein the density histogram is a one-dimensional discrete function, and the calculation is shown as a formula (2):
Figure BDA0002393028640000101
wherein f is the total ridge point or valley point number of the sub-interface, NiThe number of ridge points or valley points in the ith grid is;
step 3.4, calculating the distances from all ridge points and valley points in each grid subjected to three-dimensional rasterization to the point set centroid of the sub-interface respectively, obtaining the average distance in each grid, and generating distance feature histograms of the ridge points and the valley points respectively;
and 3.5, expressing the ridge and valley shape characteristic vector of the volume data: the ridge and valley shape feature vector of the volume data is: h ═ H1rH1v；H2rH2v；...；HnrHnv]，Hnr＝[ρrdr]Features of density histogram and distance histogram of ridge points representing a sub-interface, prIndicating the density of the ridge points corresponding to the sub-boundary surface, drIndicates the distance of the ridge point corresponding to the sub-boundary surface, Hnv＝[ρvdv]Features of density histogram and distance histogram representing a sub-boundary valley point, pvIndicating the density of the corresponding valleys of the sub-boundary surface, dvRepresenting the distance of the ridge point corresponding to the sub-interface;
step 4, constructing a tree-shaped structure topological graph and performing vector representation on the tree-shaped structure topological graph to realize comprehensive and effective representation of the three-dimensional space characteristics of the internal structure of the volume data, which specifically comprises the following steps:
step 4.1, determining the characteristic expressions of the oriented skeleton tree, the skeleton of the sub-interface and the ridge and valley of the sub-interface in the volume data and constructing a tree topology map of the volume data on the basis of the step 1-3, wherein the tree topology map is shown in FIG. 4;
step 4.2, the feature vector of the whole tree-shaped structure topological graph is M ═ TrT H]Wherein Tr isThe volume data has a feature vector of a directed skeleton tree, T is a skeleton shape feature vector of the volume data, H is a volume data ridge-valley vector, and Tr is [ N ] of the volume data directed skeleton treePNEneNEinNEdis]In which N ispRepresenting the number of the sub-interfaces; n is a radical ofEneNumber, N, representing the sub-boundary surface adjacency relationshipEinNumber, N, representing containment relationship of sub-interfacesEdisThe number of the phase-separated relation between the sub-interfaces is shown, and the volume data has a characteristic vector Tr ═ N of the skeleton treePNEneNEinNEdis]The method is obtained by inquiring and reading a spatial incidence matrix of the directed skeleton tree, wherein the spatial incidence matrix of the directed skeleton tree is as follows:
Figure BDA0002393028640000111
wherein, PiAnd PjThe ith and jth subinterfaces are respectively shown, and since the subinterface boundaries are in an inclusion relationship, psi (i, j) ═ 2 and psi (i, j) ═ 2 are in a one-to-one correspondence relationship in the spatial relationship matrix, only one is taken when the vectors are acquired, as shown in fig. 2.
The invention relates to a multi-domain material volume data internal structure feature expression method, which is characterized in that a directed skeleton tree is constructed based on a sub-interface of acquired volume data, feature expression is carried out based on the acquired sub-interface skeleton shape feature and a ridge and valley shape feature structure, and finally a volume data tree structure topological graph with three-dimensional features is established, so that complete description of volume data boundary surface shape features is realized.

Claims (7)

1. A multi-domain material volume data internal structure feature expression method is characterized by comprising the following steps:
step 1, acquiring a volume data sub-interface, analyzing the correlation among the volume data sub-interfaces, and constructing a directed skeleton tree;
step 2, representing the skeleton shape characteristics of the volume data;
step 3, representing the ridge and valley shape characteristics of the volume data;
and 4, constructing a tree structure topological graph and performing vector representation on the tree structure topological graph to realize comprehensive and effective representation of the three-dimensional space characteristics of the internal structure of the volume data.
2. The method according to claim 1, wherein the step 1 of analyzing the correlation between the sub-interfaces of the volume data and constructing the directed skeleton tree specifically comprises:
step 1.1, determining the number of sub-interfaces and the relation among the sub-interfaces based on the acquired volume data sub-interfaces, wherein the relation among the sub-interfaces comprises, is adjacent to and is separated from each other;
step 1.2, mapping the inclusion relationship between the child interfaces to a directed skeleton tree to be converted into a parent-child relationship, wherein the parent-child relationship is represented by a solid line of a one-way arrow, if the child interface A contains the child interface B, the A is that the parent node B is a child node, and the parent-child relationship is represented as A → B;
mapping the adjacent relation between the sub-interfaces to a directed skeleton tree to be converted into a brother relation, wherein the brother relation is represented by a dotted line of a bidirectional arrow, if the sub-interface A is adjacent to the interface B, the A and the B are in a brother relation, and the brother relation is represented as A ← … → B;
the phase-separation relation between the sub-interfaces is not shown;
step 1.3, constructing a directed skeleton tree, wherein the directed skeleton tree is represented as G ═<P,E,AP,AE>Wherein P is a node set in the directed skeleton tree and represents a sub-interface; e is an edge set in the directed skeleton tree, and represents a topological relation among the interfaces of the volume data sub-interfaces; a. thePThe node attribute characteristics represent the number of the sub-interfaces; a. theEIs an edge attribute feature, namely AE＝{Ene，Ein，EdisIn which EneThe adjacent relation of the sub-interfaces is expressed by the expression method of the adjacent relation edges in the step 1.2; einThe subsurfaces contain relations, and the relations are represented by a representation method containing relation edges in step 1.2; edisRepresenting the phase separation relation of the sub-interfaces, and not carrying out edge representation.
3. The method according to claim 2, wherein the step 2 is specifically as follows:
step 2.1: the skeleton characteristics of each sub-interface are obtained, the skeleton characteristics are analyzed, and the number of skeleton endpoints, the number of skeleton bifurcation nodes and the total number of skeleton branches of each sub-interface are determined;
step 2.2: according to the number of the skeleton endpoints, the number of the skeleton bifurcation nodes and the total number of the skeleton branches determined in the step 2.1, expressing the skeleton shape feature vector of the volume data, wherein the skeleton shape feature vector of the volume data is as follows: t ═ T1T2T3...Tn]，TiDenotes the framework characteristic of the ith subinterface, i ═ 1.2.3i＝[B F N]B represents the number of skeleton endpoints corresponding to the ith sub-interface, F represents the number of skeleton bifurcation nodes corresponding to the ith sub-interface, and N represents the total number of skeleton branches corresponding to the ith sub-interface.
4. The method according to claim 3, wherein the step 3 is specifically as follows:
step 3.1, respectively extracting all coordinates of ridge points and valley points of each sub-interface, and obtaining eight vertex coordinates of the minimum direction bounding box, namely (X)min,Ymin,Zmin)、(Xmax,Ymin,Zmin)、(Xmin,Ymax,Zmin)、(Xmax,Ymax,Zmin)、(Xmin,Ymin,Zmax)、(Xmax,Ymin,Zmax)、(Xmin,Ymax,Zmax)、(Xmax,Ymax,Zmax) Wherein X ismin，Xmax，Ymin，Ymax，Zmin，ZmaxRespectively representing the minimum coordinate, the maximum coordinate, the minimum coordinate and the maximum coordinate of the minimum bounding box of the point set on the X axis, the Y axis and the Z axisMarking;
step 3.2, the whole bounding box is equally divided to obtain each grid, and the grids are numbered to respectively realize three-dimensional rasterization of ridge points and valley points of the sub-interfaces;
step 3.3, determining a spatial density histogram according to the number of ridge points and valley points in each grid and the number of total ridge points and valley points of the sub-interface respectively, wherein the density histogram is a one-dimensional discrete function, and the calculation is shown as a formula (2):
Figure FDA0002393028630000031
wherein f is the total ridge point or valley point number of the sub-interface, NiThe number of ridge points or valley points in the ith grid is;
step 3.4, calculating the distances from all ridge points and valley points in each grid subjected to three-dimensional rasterization to the point set centroid of the sub-interface respectively, obtaining the average distance in each grid, and generating distance feature histograms of the ridge points and the valley points respectively;
and 3.5, expressing the ridge and valley shape characteristic vector of the volume data: the ridge and valley shape feature vector of the volume data is: h ═ H1rH1v；H2rH2v；...；HnrHnv]，Hnr＝[ρrdr]Features of density histogram and distance histogram of ridge points representing a sub-interface, prIndicating the density of the ridge points corresponding to the sub-boundary surface, drIndicates the distance of the ridge point corresponding to the sub-boundary surface, Hnv＝[ρvdv]Features of density histogram and distance histogram representing a sub-boundary valley point, pvIndicating the density of the corresponding valleys of the sub-boundary surface, dvIndicating the distance of the ridge point corresponding to the sub-interface.
5. The method according to claim 4, wherein the step 4 is specifically as follows:
step 4.1, determining the characteristic expressions of the oriented skeleton tree, the skeleton of the subinterface and the ridge and valley of the subinterface in the volume data and constructing a tree topology map of the data on the basis of the step 1-3;
and 4.2, the feature vector of the whole tree structure topological graph is M ═ Tr T H, wherein Tr is the feature vector of the volume data directed skeleton tree, T is the skeleton shape feature vector of the volume data, and H is the volume data ridge and valley vector.
6. The method according to claim 5, wherein the characteristic vector Tr ═ N of the volume data directed skeleton tree is [ N ]PNEneNEinNEdis]In which N ispRepresenting the number of the sub-interfaces; n is a radical ofEneNumber, N, representing the sub-boundary surface adjacency relationshipEinNumber, N, representing containment relationship of sub-interfacesEdisThe number of the phase separation relationships between the sub-interfaces is shown.
7. The method according to claim 6, wherein the characteristic vector Tr ═ N of the volume data directed skeleton tree is [ N ]PNEneNEinNEdis]The method is obtained by inquiring and reading a spatial incidence matrix of the directed skeleton tree, wherein the spatial incidence matrix of the directed skeleton tree is as follows:
Figure FDA0002393028630000041
wherein, PiAnd PjThe ith and jth subinterfaces are respectively shown, and since the subinterface boundaries are in an inclusion relationship, psi (i, j) ═ 2 and psi (i, j) ═ 2 are in a one-to-one correspondence relationship in the spatial relationship matrix, only one is taken when the vector is acquired.
CN202010121247.4A 2020-02-26 2020-02-26 Multi-domain substance body data internal structure characteristic expression method Active CN111341392B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010121247.4A CN111341392B (en) 2020-02-26 2020-02-26 Multi-domain substance body data internal structure characteristic expression method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010121247.4A CN111341392B (en) 2020-02-26 2020-02-26 Multi-domain substance body data internal structure characteristic expression method

Publications (2)

Publication Number Publication Date
CN111341392A true CN111341392A (en) 2020-06-26
CN111341392B CN111341392B (en) 2023-05-02

Family

ID=71187950

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010121247.4A Active CN111341392B (en) 2020-02-26 2020-02-26 Multi-domain substance body data internal structure characteristic expression method

Country Status (1)

Country Link
CN (1) CN111341392B (en)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001008263A2 (en) * 1999-07-28 2001-02-01 National University Of Singapore Method and apparatus for generating atomic parts of graphic representation through skeletonization for interactive visualization applications
CN107330901A (en) * 2017-06-29 2017-11-07 西安理工大学 A kind of object component decomposition method based on skeleton
WO2019200683A1 (en) * 2018-04-19 2019-10-24 浙江大学 Sketch-based shape-preserving tree deformation animation method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001008263A2 (en) * 1999-07-28 2001-02-01 National University Of Singapore Method and apparatus for generating atomic parts of graphic representation through skeletonization for interactive visualization applications
CN107330901A (en) * 2017-06-29 2017-11-07 西安理工大学 A kind of object component decomposition method based on skeleton
WO2019200683A1 (en) * 2018-04-19 2019-10-24 浙江大学 Sketch-based shape-preserving tree deformation animation method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
张黎娜等: "融合形状特征的MRG骨架树三维检索方法", 《计算机工程与设计》 *

Also Published As

Publication number Publication date
CN111341392B (en) 2023-05-02

Similar Documents

Publication Publication Date Title
CN101807308B (en) Three-dimensional model segmenting device and method
CN101719140B (en) Figure retrieving method
Lai et al. Fast mesh segmentation using random walks
CN109325993B (en) Saliency feature enhanced sampling method based on class octree index
CN103310481B (en) A kind of point cloud compressing method based on fuzzy entropy iteration
CN105139379B (en) Based on the progressive extracting method of classified and layered airborne Lidar points cloud building top surface
CN113379898B (en) Three-dimensional indoor scene reconstruction method based on semantic segmentation
CN103701466A (en) Scattered point cloud compression algorithm based on feature reservation
CN106780458B (en) Point cloud framework extraction method and device
CN103390046A (en) Multi-scale dictionary natural scene image classification method based on latent Dirichlet model
CN110322497A (en) A kind of interactive point cloud object extraction method based on three-dimensional visualization
CN115661374B (en) Rapid retrieval method based on space division and model voxelization
CN102938161A (en) Three-dimensional shape automatic partition method based on Mean Shift
CN104036550A (en) Laser radar point-cloud interpretation and reconstruction method for building elevations on basis of shape semantics
Thiemann et al. 3D-symbolization using adaptive templates
CN111341392B (en) Multi-domain substance body data internal structure characteristic expression method
CN105139452B (en) A kind of Geologic Curve method for reconstructing based on image segmentation
Cárdenas-Donoso et al. Modeling of the 3D tree skeleton using real-world data: A survey
Cao et al. Gaussian-curvature-derived invariants for isometry
CN114638116A (en) Digital plate opening method based on three-dimensional shoe tree
CN111339332B (en) Three-dimensional volume data retrieval method based on tree structure topological graph
CN109684424B (en) Landform data rapid generation and optimization method based on discrete characteristic line
CN113298919A (en) Skeleton extraction method of three-dimensional plant point cloud model
Yang et al. Unsupervised co-segmentation of 3d shapes based on components
CN116563171B (en) Point cloud enhancement method and related equipment for building repeated structure

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant