Given a set U which is consisted of strings defined on alphabet Σ, string cross pattern matching is to find all the matches between every two strings in U. It is utilized in text processing like removing the duplicat...Given a set U which is consisted of strings defined on alphabet Σ, string cross pattern matching is to find all the matches between every two strings in U. It is utilized in text processing like removing the duplication of strings. This paper presents a fast string cross pattern matching algorithm based on extracting high frequency strings. Compared with existing algorithms including single-pattern algorithms and multi-pattern matching algorithms, this algorithm is featured by both low time complexity and low space complexity. Because Chinese alphabet is large and the average length of Chinese words is much short, this algorithm is more suitable to process the text written by Chinese, especially when the size of Σ is large and the number of strings is far more than the maximum length of strings of set U.展开更多
The traditional multiple pattern matching algorithm, deterministic finite state automata, is implemented by tree structure. A new algorithm is proposed by substituting sequential binary tree for traditional tree. It i...The traditional multiple pattern matching algorithm, deterministic finite state automata, is implemented by tree structure. A new algorithm is proposed by substituting sequential binary tree for traditional tree. It is proved by experiment that the algorithm has three features, its construction process is quick, its cost of memory is small. At the same time, its searching process is as quick as the traditional algorithm. The algorithm is suitable for the application which requires preprocessing the patterns dynamically.展开更多
A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low freq...A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.展开更多
This paper presents a modified multi-resolution telescopic search algorithm (MRTlcSA) for the block-matching motion estimation. A novel inverse telescopic search is substituted for the conventional telescopic search, ...This paper presents a modified multi-resolution telescopic search algorithm (MRTlcSA) for the block-matching motion estimation. A novel inverse telescopic search is substituted for the conventional telescopic search, that reduces the on-chip memory size and memory bandwidth for VLSI implementation. In addition, strategies of motion track and adaptive search window are applied to reduce the computational complexity of motion estimation. Simulation results show that, compared with the MRTleSA, the proposed algorithm reduces the computational load to only 30% while preserving almost the same image quality. Comparisons on hardware cost and power consumption of the VLSI implementations using the two algorithms are also presented in the paper.展开更多
A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a ...A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment.In this approach,the Harris algorithm is introduced to detect the corner points of the object,and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area.Then the local binary pattern（LBP） operator is used to build the observation model of the target based on the color and texture features,by which the second-order weights of particles and the accurate location of the target can be obtained.Moreover,a backstepping controller is proposed to complete the whole tracking system.Simulations and experiments are carried out,and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.展开更多
文摘Given a set U which is consisted of strings defined on alphabet Σ, string cross pattern matching is to find all the matches between every two strings in U. It is utilized in text processing like removing the duplication of strings. This paper presents a fast string cross pattern matching algorithm based on extracting high frequency strings. Compared with existing algorithms including single-pattern algorithms and multi-pattern matching algorithms, this algorithm is featured by both low time complexity and low space complexity. Because Chinese alphabet is large and the average length of Chinese words is much short, this algorithm is more suitable to process the text written by Chinese, especially when the size of Σ is large and the number of strings is far more than the maximum length of strings of set U.
基金This project was supported by the National "863" High Technology Research and Development Program of China(2003AA142160) and the National Natural Science Foundation of China (60402019)
文摘The traditional multiple pattern matching algorithm, deterministic finite state automata, is implemented by tree structure. A new algorithm is proposed by substituting sequential binary tree for traditional tree. It is proved by experiment that the algorithm has three features, its construction process is quick, its cost of memory is small. At the same time, its searching process is as quick as the traditional algorithm. The algorithm is suitable for the application which requires preprocessing the patterns dynamically.
基金supported by the National Natural Science Foundation of China (6117212711071002)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education (20113401110006)the Innovative Research Team of 211 Project in Anhui University (KJTD007A)
文摘A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.
文摘This paper presents a modified multi-resolution telescopic search algorithm (MRTlcSA) for the block-matching motion estimation. A novel inverse telescopic search is substituted for the conventional telescopic search, that reduces the on-chip memory size and memory bandwidth for VLSI implementation. In addition, strategies of motion track and adaptive search window are applied to reduce the computational complexity of motion estimation. Simulation results show that, compared with the MRTleSA, the proposed algorithm reduces the computational load to only 30% while preserving almost the same image quality. Comparisons on hardware cost and power consumption of the VLSI implementations using the two algorithms are also presented in the paper.
基金supported by the National Natural Science Foundation of China(61304097)the Projects of Major International(Regional)Joint Research Program NSFC(61120106010)the Foundation for Innovation Research Groups of the National National Natural Science Foundation of China(61321002)
文摘A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment.In this approach,the Harris algorithm is introduced to detect the corner points of the object,and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area.Then the local binary pattern（LBP） operator is used to build the observation model of the target based on the color and texture features,by which the second-order weights of particles and the accurate location of the target can be obtained.Moreover,a backstepping controller is proposed to complete the whole tracking system.Simulations and experiments are carried out,and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.
