In this article we introduce an exact backprojection filtered (BPF) type reconstruction algorithm for cone-beam scans based on Zou and Pan’s work. The algorithm can reconstruct images using only the projection data p...In this article we introduce an exact backprojection filtered (BPF) type reconstruction algorithm for cone-beam scans based on Zou and Pan’s work. The algorithm can reconstruct images using only the projection data passing through the parallel PI-line segments in reduced scans. Computer simulations and practical experiments are carried out to evaluate this algorithm. The BPF algorithm has a higher computational efficiency than the famous FDK algorithm. The BPF algorithm is evaluated using the practical CT projection data on a 450 keV X-ray CT system with a flat-panel detector (FPD). From the practical experiments, we get the spatial resolution of this CT system. The algo- rithm could achieve the spatial resolution of 2.4 lp/mm and satisfies the practical applications in industrial CT inspec- tion.展开更多
Aim at the defects of easy to fall into the local minimum point and the low convergence speed of back propagation（BP）neural network in the gesture recognition, a new method that combines the chaos algorithm with the...Aim at the defects of easy to fall into the local minimum point and the low convergence speed of back propagation（BP）neural network in the gesture recognition, a new method that combines the chaos algorithm with the genetic algorithm（CGA） is proposed. According to the ergodicity of chaos algorithm and global convergence of genetic algorithm, the basic idea of this paper is to encode the weights and thresholds of BP neural network and obtain a general optimal solution with genetic algorithm, and then the general optimal solution is optimized to the accurate optimal solution by adding chaotic disturbance. The optimal results of the chaotic genetic algorithm are used as the initial weights and thresholds of the BP neural network to recognize the gesture. Simulation and experimental results show that the real-time performance and accuracy of the gesture recognition are greatly improved with CGA.展开更多
基金Supported by a grant from the Ph.D. Programs Foundation of Ministry of Education of China (No. 20030003074) and the National Natural Science Founda-tion of China (No. 10575059).
文摘In this article we introduce an exact backprojection filtered (BPF) type reconstruction algorithm for cone-beam scans based on Zou and Pan’s work. The algorithm can reconstruct images using only the projection data passing through the parallel PI-line segments in reduced scans. Computer simulations and practical experiments are carried out to evaluate this algorithm. The BPF algorithm has a higher computational efficiency than the famous FDK algorithm. The BPF algorithm is evaluated using the practical CT projection data on a 450 keV X-ray CT system with a flat-panel detector (FPD). From the practical experiments, we get the spatial resolution of this CT system. The algo- rithm could achieve the spatial resolution of 2.4 lp/mm and satisfies the practical applications in industrial CT inspec- tion.
基金supported by Natural Science Foundation of Heilongjiang Province Youth Fund(No.QC2014C054)Foundation for University Young Key Scholar by Heilongjiang Province(No.1254G023)the Science Funds for the Young Innovative Talents of HUST(No.201304)
文摘Aim at the defects of easy to fall into the local minimum point and the low convergence speed of back propagation（BP）neural network in the gesture recognition, a new method that combines the chaos algorithm with the genetic algorithm（CGA） is proposed. According to the ergodicity of chaos algorithm and global convergence of genetic algorithm, the basic idea of this paper is to encode the weights and thresholds of BP neural network and obtain a general optimal solution with genetic algorithm, and then the general optimal solution is optimized to the accurate optimal solution by adding chaotic disturbance. The optimal results of the chaotic genetic algorithm are used as the initial weights and thresholds of the BP neural network to recognize the gesture. Simulation and experimental results show that the real-time performance and accuracy of the gesture recognition are greatly improved with CGA.
