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
针对传统地磁匹配中动态时间规整算法(Dynamic Time Warping,DTW)应用于地磁定位时存在误差较大、匹配速度较慢的问题,提出一种基于改进的DTW算法和边界搜索策略的地磁匹配定位方法。首先将传统地磁数据序列变换为一阶导数序列,其更能...针对传统地磁匹配中动态时间规整算法(Dynamic Time Warping,DTW)应用于地磁定位时存在误差较大、匹配速度较慢的问题,提出一种基于改进的DTW算法和边界搜索策略的地磁匹配定位方法。首先将传统地磁数据序列变换为一阶导数序列,其更能反应地磁场的变化趋势,以提高匹配的准确性。引入有限窗口对DTW算法计算单元数进行限制,以加快匹配速度。同时,采用边界搜索策略进行局部筛选,约束相邻两次定位点间的距离,避免出现不合理匹配点的现象。实验结果表明,所提算法相比传统DTW算法在室内环境下定位精度最高可提升87%,处理速度提升15%;室外环境下定位精度可提升17%,处理速度提升52%。研究成果满足地磁定位的实时性、高精度的需求,具有很强的实用性和广阔的发展空间。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
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.
文摘针对传统地磁匹配中动态时间规整算法(Dynamic Time Warping,DTW)应用于地磁定位时存在误差较大、匹配速度较慢的问题,提出一种基于改进的DTW算法和边界搜索策略的地磁匹配定位方法。首先将传统地磁数据序列变换为一阶导数序列,其更能反应地磁场的变化趋势,以提高匹配的准确性。引入有限窗口对DTW算法计算单元数进行限制,以加快匹配速度。同时,采用边界搜索策略进行局部筛选,约束相邻两次定位点间的距离,避免出现不合理匹配点的现象。实验结果表明,所提算法相比传统DTW算法在室内环境下定位精度最高可提升87%,处理速度提升15%;室外环境下定位精度可提升17%,处理速度提升52%。研究成果满足地磁定位的实时性、高精度的需求,具有很强的实用性和广阔的发展空间。
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.
