String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that...String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-core based environment. Experimental outcomes reveal that the overall performance of the mentioned string matching algorithm has been improved, and the improvement in the execution time which has been obtained is considerable enough to recommend the multi-core environment as the suitable platform for parallelizing the Quick Search string matching algorithm.展开更多
通过仿真对比研究了基于特征匹配的目标识别算法快速性及鲁棒性问题.采用目前常用的STAR、FAST、SIFT(scale invariant feature transform)、SURF(speeded up robust features)、ORB(oriented FAST and rotated BRIEF)、BRISK(binary ro...通过仿真对比研究了基于特征匹配的目标识别算法快速性及鲁棒性问题.采用目前常用的STAR、FAST、SIFT(scale invariant feature transform)、SURF(speeded up robust features)、ORB(oriented FAST and rotated BRIEF)、BRISK(binary robust invariant scalable keypoint)和FREAK(fast retina keypoint)等算法,对算法快速性和鲁棒性进行比较,并通过不同检测子与描述子的相互结合,找出最佳组合方式,提出了一种运用匹配点数与总耗时的比值来衡量算法综合性能好坏的新方法.仿真对比证明,FAST检测子、BRISK描述子以及STAR与BRISK的组合具有较好的性能.展开更多
文摘String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-core based environment. Experimental outcomes reveal that the overall performance of the mentioned string matching algorithm has been improved, and the improvement in the execution time which has been obtained is considerable enough to recommend the multi-core environment as the suitable platform for parallelizing the Quick Search string matching algorithm.
