Abstract: Disclosed is a graph convolution auto-encoder based method for computing road network similarity. The method includes: creating a dual graph of a road network, and giving road network space feature information to nodes of the dual graph from three aspects of global, local and connection characteristics on the basis of a relation principle between an entire structure and parts of the structure, such that a quantitative expression of a road network graph structure is obtained; aggregating and updating node feature information and structure information of a road network graph by the graph convolution auto-encoder, and forming a deep understanding of the road network, such that a coded expression of node information of the road network is obtained; and mapping a complex high-dimensional feature space to an easy-to-measure low-dimensional feature space through an average pooling operation, so as to obtain a set of feature vectors, and computing the similarity.

Abstract: Disclosed is a graph convolution auto-encoder based method for computing road network similarity. The method includes: creating a dual graph of a road network, and giving road network space feature information to nodes of the dual graph from three aspects of global, local and connection characteristics on the basis of a relation principle between an entire structure and parts of the structure, such that a quantitative expression of a road network graph structure is obtained; aggregating and updating node feature information and structure information of a road network graph by the graph convolution auto-encoder, and forming a deep understanding of the road network, such that a coded expression of node information of the road network is obtained; and mapping a complex high-dimensional feature space to an easy-to-measure low-dimensional feature space through an average pooling operation, so as to obtain a set of feature vectors, and computing the similarity.

Abstract: Disclosed in the present disclosure is a similarity calculation method considering multiple features for a multi-scale road network, which includes structural similarity calculation of skeleton lines and local feature similarity calculation of the multi-scale road network. A road network stroke is generated, skeleton lines of the road network are extracted and transformed into a structure tree, and the skeleton similarity of the road network is evaluated by calculating structural similarity of the structure tree of the multi-scale road network. Topological similarity of the multi-scale road network is calculated by using a difference matrix of a conceptual domain graph of road meshes, geometric similarity of the multi-scale road network is calculated by using a density of road meshes, and local similarity of the multi-scale road network is obtained by integrating the hierarchy of the road network into the topological and geometric similarity calculation in the form of matrices.

Abstract: Disclosed in the present disclosure is a classification method based on skeleton lines for building shapes. The method includes: (1) expanding a template library of buildings by combining building shape classification in architecture and building shape features in real life on the basis of inheriting advantages of a traditional template matching method; (2) avoiding influence of small depressions and protrusions of the buildings on overall shapes by constructing a least-squares template; (3) extracting the skeleton lines of the buildings and calculating feature vectors of the buildings; and (4) calculating similarity between feature vectors of skeleton lines of the buildings and skeleton lines of templates by using cosine similarity, and selecting the template with the highest similarity as a classification result of the building shapes.

Abstract: The present utility model provides a test bed, a magnetic resonance imaging system, and a medical imaging system. The test bed includes a support frame used to bear a tested object, a base used to support the support frame, and at least one weighing assembly. The at least one weighing assembly is fixed to the base, and acquires weight information of the tested object by sensing stress from the support frame.

Abstract: The present utility model provides a test bed, a magnetic resonance imaging system, and a medical imaging system. The test bed includes a support frame used to bear a tested object, a base used to support the support frame, and at least one weighing assembly. The at least one weighing assembly is fixed to the base, and acquires weight information of the tested object by sensing stress from the support frame.

Abstract: The present invention relates to a best shooting moment selectable digital camera apparatus. This digital camera apparatus could decide the instantaneous best shooting moment to take photos. The technical scheme is: including camera head, memory circuit and signal processor, the signal processor includes the co-series face detecting module, eye & mouth positioning module and blink judging & mouth open judging module, meanwhile, the face detecting module reads out the digital image from memory circuit and delivers the result into eye & mouth positioning module after face positioning, the eye & mouth positioning module delivers the result into blink judging & mouth open judging module after eye and mouth positioning, the blink judging & mouth open judging module sends out the judgment result; the controlling circuit issues a shoot controlling order when the judgment result of the said blink judging & mouth open judging module means a best shooting moment.

Abstract: According to the present invention, there is provided a printed graphics inspection system and the inspection method thereof to inspect printed graphics including holograms. In the present invention improving the printing quality and reducing manpower cost are achieved by using of a plurality of sensors located in different angles to exclude zones not normally detectable on the graphics to be inspected, digitalizing the images inspected by such sensors, sending the digitalized images to a micro-processor, and using such micro-processor to analyze and process the images inspected and a set of corresponding templates to identify defects such as contamination, color deviation and the like.