Ming and Qing Dynasty Official-Style Architecture Roof Types Classification Based on the 3D Point Cloud

The Ming and Qing Dynasty type of official-style architecture roof can provide plenty of prior knowledge relating to the structure and size of these works of architecture, and plays an important role in the fields of 3D modeling, semantic recognition and culture inheriting. In this paper, we take the 3D point cloud as the data source, and an automatic classification method for the roof type of Ming and Qing Dynasty official-style architecture based on the hierarchical semantic network is illustrated. To classify the roofs into the correct categories, the characteristics of different roof types are analyzed and features including SoRs, DfFtR, DoPP and NoREs are first selected; subsequently, the corresponding feature extraction methods are proposed; thirdly, aiming at the structure of the ridges, a matching graph relying on the attributed relational graph of the ridges is given; based on the former work, a hierarchical semantic network is proposed and the thresholds are determined with the help of the construction rules of the Ming and Qing Dynasty official-style architecture. In order to fully verify the efficiency of our proposed method, various types of Ming and Qing Dynasty official-style architecture roof are identified, and the experimental results show that all structures are classified correctly.

A Multi-Scale Settlement Matching Algorithm Based on ARG

Homonymous entity matching is an important part of multi-source spatial data integration, automatic updating and change detection. Considering the low accuracy of existing matching methods in dealing with matching multi-scale settlement data, an algorithm based on Attributed Relational Graph (ARG) is proposed. The algorithm firstly divides two settlement scenes at different scales into blocks by small-scale road network and constructs local ARGs in each block. Then, ascertains candidate sets by merging procedures and obtains the optimal matching pairs by comparing the similarity of ARGs iteratively. Finally, the corresponding relations between settlements at large and small scales are identified. At the end of this article, a demonstration is presented and the results indicate that the proposed algorithm is capable of handling sophisticated cases.

A Multi-Scale Settlement Matching Algorithm Based on ARG

Homonymous entity matching is an important part of multi-source spatial data integration, automatic updating and change detection. Considering the low accuracy of existing matching methods in dealing with matching multi-scale settlement data, an algorithm based on Attributed Relational Graph (ARG) is proposed. The algorithm firstly divides two settlement scenes at different scales into blocks by small-scale road network and constructs local ARGs in each block. Then, ascertains candidate sets by merging procedures and obtains the optimal matching pairs by comparing the similarity of ARGs iteratively. Finally, the corresponding relations between settlements at large and small scales are identified. At the end of this article, a demonstration is presented and the results indicate that the proposed algorithm is capable of handling sophisticated cases.

Similarity Assessment for Assembly Model Based on Component Attributed Relational Graph Matching

3D CAD model retrieval has received a lot of attentions in the academic community. Most existent methods for 3D model similarity assessment focus on component models instead of assembly models. In this paper, an assembly model similarity assessment method is presented in order to find the similar assemblies model for design reuse. First, assembly model is described as component attributed relational graph. Then, the compatibility matrix between two assemblies is calculated, which serves as the measure of their similarity. Finally, the optimal matching under the measures is calculated using Hungarian Method. Experimental results show that this method is able to support the assembly similarity evaluation.

Tracking Multiple Persons under Occlusion Using Appearance Model Based on Attributed Relational Graph

Appearance features are important for tracking persons in stationary scenes. The proposed algorithm was based on appearance mold built by attributed relational graph (ARG). The ARG was used for modeling human appearance features containing color and spatial information. The matching degree of the ARGs was utilized for analyzing tracking situations in current frame. For tracking persons under occlusion, multiple feature patches were generated and the genetic algorithm was used for finding optimal labels of patches. Experiments showed the utility and performance of the proposed approach.