Since the specifications of most of the existing context-sensitive graph grammars tend to be either too intricate or not intuitive, a novel context-sensitive graph grammar formalism, called context-attributed graph gr...Since the specifications of most of the existing context-sensitive graph grammars tend to be either too intricate or not intuitive, a novel context-sensitive graph grammar formalism, called context-attributed graph grammar（CAGG）, is proposed. In order to resolve the embedding problem, context information of a graph production in the CAGG is represented in the form of context attributes of the nodes involved. Moreover, several properties of a set of confluent CAGG productions are characterized, and then an algorithm based on them is developed to decide whether or not a set of productions is confluent, which provides the foundation for the design of efficient parsing algorithms. It can also be shown through the comparison of CAGG with several typical context-sensitive graph grammars that CAGG is more succinct and, at the same time, more intuitive than the others, making it more suitably and effortlessly applicable to the specification of visual languages.展开更多
Automatic image classification is the first step toward semantic understanding of an object in the computer vision area.The key challenge of problem for accurate object recognition is the ability to extract the robust...Automatic image classification is the first step toward semantic understanding of an object in the computer vision area.The key challenge of problem for accurate object recognition is the ability to extract the robust features from various viewpoint images and rapidly calculate similarity between features in the image database or video stream.In order to solve these problems,an effective and rapid image classification method was presented for the object recognition based on the video learning technique.The optical-flow and RANSAC algorithm were used to acquire scene images from each video sequence.After the selection of scene images,the local maximum points on comer of object around local area were found using the Harris comer detection algorithm and the several attributes from local block around each feature point were calculated by using scale invariant feature transform （SIFT） for extracting local descriptor.Finally,the extracted local descriptor was learned to the three-dimensional pyramid match kernel.Experimental results show that our method can extract features in various multi-viewpoint images from query video and calculate a similarity between a query image and images in the database.展开更多
基金The National Natural Science Foundation of China(No.60571048,60673186,60736015)the National High Technology Researchand Development Program of China(863Program)(No.2007AA01Z178)
文摘Since the specifications of most of the existing context-sensitive graph grammars tend to be either too intricate or not intuitive, a novel context-sensitive graph grammar formalism, called context-attributed graph grammar（CAGG）, is proposed. In order to resolve the embedding problem, context information of a graph production in the CAGG is represented in the form of context attributes of the nodes involved. Moreover, several properties of a set of confluent CAGG productions are characterized, and then an algorithm based on them is developed to decide whether or not a set of productions is confluent, which provides the foundation for the design of efficient parsing algorithms. It can also be shown through the comparison of CAGG with several typical context-sensitive graph grammars that CAGG is more succinct and, at the same time, more intuitive than the others, making it more suitably and effortlessly applicable to the specification of visual languages.
文摘Automatic image classification is the first step toward semantic understanding of an object in the computer vision area.The key challenge of problem for accurate object recognition is the ability to extract the robust features from various viewpoint images and rapidly calculate similarity between features in the image database or video stream.In order to solve these problems,an effective and rapid image classification method was presented for the object recognition based on the video learning technique.The optical-flow and RANSAC algorithm were used to acquire scene images from each video sequence.After the selection of scene images,the local maximum points on comer of object around local area were found using the Harris comer detection algorithm and the several attributes from local block around each feature point were calculated by using scale invariant feature transform （SIFT） for extracting local descriptor.Finally,the extracted local descriptor was learned to the three-dimensional pyramid match kernel.Experimental results show that our method can extract features in various multi-viewpoint images from query video and calculate a similarity between a query image and images in the database.
