Inexact graph matching algorithms have proved to be useful in many applications,such as character recognition,shape analysis,and image analysis. Inexact graph matching is,however,inherently an NP-hard problem with exp...Inexact graph matching algorithms have proved to be useful in many applications,such as character recognition,shape analysis,and image analysis. Inexact graph matching is,however,inherently an NP-hard problem with exponential computational complexity. Much of the previous research has focused on solving this problem using heuristics or estimations. Unfortunately,many of these techniques do not guarantee that an optimal solution will be found. It is the aim of the proposed algorithm to reduce the complexity of the inexact graph matching process,while still producing an optimal solution for a known application. This is achieved by greatly simplifying each individual matching process,and compensating for lost robustness by producing a hierarchy of matching processes. The creation of each matching process in the hierarchy is driven by an application-specific criterion that operates at the subgraph scale. To our knowledge,this problem has never before been approached in this manner. Results show that the proposed algorithm is faster than two existing methods based on graph edit operations.The proposed algorithm produces accurate results in terms of matching graphs,and shows promise for the application of shape matching. The proposed algorithm can easily be extended to produce a sub-optimal solution if required.展开更多
We present the solid model edit distance(SMED),a powerful and flexible paradigm for exploiting shape similarities amongst CAD models.It is designed to measure the magnitude of distortions between two CAD models in bou...We present the solid model edit distance(SMED),a powerful and flexible paradigm for exploiting shape similarities amongst CAD models.It is designed to measure the magnitude of distortions between two CAD models in boundary representation(B-rep).We give the formal definition by analogy with graph edit distance,one of the most popular graph matching methods.To avoid the expensive computational cost potentially caused by exact computation,an approximate procedure based on the alignment of local structure sets is provided in addition.In order to verify the flexibility,we make intensive investigations on three typical applications in manufacturing industry,and describe how our method can be adapted to meet the various requirements.Furthermore,a multilevel method is proposed to make further improvements of the presented algorithm on both effectiveness and efficiency,in which the models are hierarchically segmented into the configurations of features.Experiment results show that SMED serves as a reasonable measurement of shape similarity for CAD models,and the proposed approach provides remarkable performance on a real-world CAD model database.展开更多
文摘Inexact graph matching algorithms have proved to be useful in many applications,such as character recognition,shape analysis,and image analysis. Inexact graph matching is,however,inherently an NP-hard problem with exponential computational complexity. Much of the previous research has focused on solving this problem using heuristics or estimations. Unfortunately,many of these techniques do not guarantee that an optimal solution will be found. It is the aim of the proposed algorithm to reduce the complexity of the inexact graph matching process,while still producing an optimal solution for a known application. This is achieved by greatly simplifying each individual matching process,and compensating for lost robustness by producing a hierarchy of matching processes. The creation of each matching process in the hierarchy is driven by an application-specific criterion that operates at the subgraph scale. To our knowledge,this problem has never before been approached in this manner. Results show that the proposed algorithm is faster than two existing methods based on graph edit operations.The proposed algorithm produces accurate results in terms of matching graphs,and shows promise for the application of shape matching. The proposed algorithm can easily be extended to produce a sub-optimal solution if required.
基金Supported by National Science Foundation of China(61373071)
文摘We present the solid model edit distance(SMED),a powerful and flexible paradigm for exploiting shape similarities amongst CAD models.It is designed to measure the magnitude of distortions between two CAD models in boundary representation(B-rep).We give the formal definition by analogy with graph edit distance,one of the most popular graph matching methods.To avoid the expensive computational cost potentially caused by exact computation,an approximate procedure based on the alignment of local structure sets is provided in addition.In order to verify the flexibility,we make intensive investigations on three typical applications in manufacturing industry,and describe how our method can be adapted to meet the various requirements.Furthermore,a multilevel method is proposed to make further improvements of the presented algorithm on both effectiveness and efficiency,in which the models are hierarchically segmented into the configurations of features.Experiment results show that SMED serves as a reasonable measurement of shape similarity for CAD models,and the proposed approach provides remarkable performance on a real-world CAD model database.
