Infrared(IR) small target detection is one of the key technologies of infrared search and track(IRST)systems. Existing methods have some limitations in detection performance, especially when the target size is irregul...Infrared(IR) small target detection is one of the key technologies of infrared search and track(IRST)systems. Existing methods have some limitations in detection performance, especially when the target size is irregular or the background is complex. In this paper, we propose a pixel-level local contrast measure(PLLCM), which can subdivide small targets and backgrounds at pixel level simultaneously.With pixel-level segmentation, the difference between the target and the background becomes more obvious, which helps to improve the detection performance. First, we design a multiscale sliding window to quickly extract candidate target pixels. Then, a local window based on random walker(RW) is designed for pixel-level target segmentation. After that, PLLCM incorporating probability weights and scale constraints is proposed to accurately measure local contrast and suppress various types of background interference. Finally, an adaptive threshold operation is applied to separate the target from the PLLCM enhanced map. Experimental results show that the proposed method has a higher detection rate and a lower false alarm rate than the baseline algorithms, while achieving a high speed.展开更多
为了提高推荐算法评分预测的准确度,该文在Trust Walker模型的基础上,提出了一个改进的基于信任网络和随机游走策略的评分预测模型——Referential User Walker模型。该模型通过随机游走策略,利用信任网络中的信任朋友对目标物品或与目...为了提高推荐算法评分预测的准确度,该文在Trust Walker模型的基础上,提出了一个改进的基于信任网络和随机游走策略的评分预测模型——Referential User Walker模型。该模型通过随机游走策略,利用信任网络中的信任朋友对目标物品或与目标物品相似的物品的评分进行评分预测,并在信任网络中找到最可信的Top N评分参考用户,同时引入信任度权重,降低了噪声数据的影响。实验结果表明,与Trust Walker模型相比,Referential User Walker模型的评分预测准确度有所提高。展开更多
基金supported by the National Natural Science Foundation of China under Grant 62003247, Grant 62075169, and Grant 62061160370。
文摘Infrared(IR) small target detection is one of the key technologies of infrared search and track(IRST)systems. Existing methods have some limitations in detection performance, especially when the target size is irregular or the background is complex. In this paper, we propose a pixel-level local contrast measure(PLLCM), which can subdivide small targets and backgrounds at pixel level simultaneously.With pixel-level segmentation, the difference between the target and the background becomes more obvious, which helps to improve the detection performance. First, we design a multiscale sliding window to quickly extract candidate target pixels. Then, a local window based on random walker(RW) is designed for pixel-level target segmentation. After that, PLLCM incorporating probability weights and scale constraints is proposed to accurately measure local contrast and suppress various types of background interference. Finally, an adaptive threshold operation is applied to separate the target from the PLLCM enhanced map. Experimental results show that the proposed method has a higher detection rate and a lower false alarm rate than the baseline algorithms, while achieving a high speed.
文摘为了提高推荐算法评分预测的准确度,该文在Trust Walker模型的基础上,提出了一个改进的基于信任网络和随机游走策略的评分预测模型——Referential User Walker模型。该模型通过随机游走策略,利用信任网络中的信任朋友对目标物品或与目标物品相似的物品的评分进行评分预测,并在信任网络中找到最可信的Top N评分参考用户,同时引入信任度权重,降低了噪声数据的影响。实验结果表明,与Trust Walker模型相比,Referential User Walker模型的评分预测准确度有所提高。
