Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. Th...Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. This paper describes a novel method of fast face detection with multi-scale window search free from image resizing. We adopt statistics of gradient images (SGI) as image features and append an overlapping cell array to improve detection accuracy. The SGI feature is scale invariant and insensitive to small difference of pixel value. These characteristics enable the multi-scale window search without image resizing. Experimental results show that processing speed of our method is 3.66 times faster than a conventional method, adopting HOG features combined to an SVM classifier, without accuracy degradation.展开更多
A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely no...A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.展开更多
针对现有优化算法在求解带时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)时存在易陷入局部最优解和收敛速度慢等问题,提出了一种基于K均值聚类和改进大规模邻域搜索算法(K-means clustering algorithm and im...针对现有优化算法在求解带时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)时存在易陷入局部最优解和收敛速度慢等问题,提出了一种基于K均值聚类和改进大规模邻域搜索算法(K-means clustering algorithm and improved large neighborhood search algorithm,K-means-ILNSA)。采用先聚类后优化的策略,利用K-means算法对待配送客户进行分组,以提高优化效率。采用遗传算法对聚类产生的每组客户进行单独优化,以初步规划配送路径。引入大规模邻域搜索(large neighborhood search,LNS)算法对配送路径进一步优化,以有效避免算法陷入局部最优解。实验结果表明:所提算法能够有效解决带时间窗的车辆路径问题,其生成的车辆总路程短,优化求解效率高。展开更多
文摘Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. This paper describes a novel method of fast face detection with multi-scale window search free from image resizing. We adopt statistics of gradient images (SGI) as image features and append an overlapping cell array to improve detection accuracy. The SGI feature is scale invariant and insensitive to small difference of pixel value. These characteristics enable the multi-scale window search without image resizing. Experimental results show that processing speed of our method is 3.66 times faster than a conventional method, adopting HOG features combined to an SVM classifier, without accuracy degradation.
基金supported by the National Natural Science Foundation of China (7060103570801062)
文摘A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.
文摘针对现有优化算法在求解带时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)时存在易陷入局部最优解和收敛速度慢等问题,提出了一种基于K均值聚类和改进大规模邻域搜索算法(K-means clustering algorithm and improved large neighborhood search algorithm,K-means-ILNSA)。采用先聚类后优化的策略,利用K-means算法对待配送客户进行分组,以提高优化效率。采用遗传算法对聚类产生的每组客户进行单独优化,以初步规划配送路径。引入大规模邻域搜索(large neighborhood search,LNS)算法对配送路径进一步优化,以有效避免算法陷入局部最优解。实验结果表明:所提算法能够有效解决带时间窗的车辆路径问题,其生成的车辆总路程短,优化求解效率高。
