This paper discusses and sums up the basic criterions of guaranteeing the labeling quality and abstracts the four basic factors including the conflict for a label with a label,overlay for label with the features,posit...This paper discusses and sums up the basic criterions of guaranteeing the labeling quality and abstracts the four basic factors including the conflict for a label with a label,overlay for label with the features,position’s priority and the association for a label with its feature.By establishing the scoring system,a formalized four-factors quality evaluation model is constructed.Last,this paper introduces the experimental result of the quality evaluation model applied to the automatic map labeling system-MapLabel.展开更多
相位展开是磁共振成像技术应用中最关键的环节之一,可以为磁共振的某些重要临床应用提供精确的相位信息。然而,由于临床磁共振成像过程中,部分区域真实的相位存在急剧变化,同时伴有不同性态的噪声污染,导致相位展开时存在信息的高度不...相位展开是磁共振成像技术应用中最关键的环节之一,可以为磁共振的某些重要临床应用提供精确的相位信息。然而,由于临床磁共振成像过程中,部分区域真实的相位存在急剧变化,同时伴有不同性态的噪声污染,导致相位展开时存在信息的高度不一致性。为了有效地解决上述难题,基于马尔可夫-最大后验(Markov Random Field& Maximum A Posterioi,MRF-MAP)模型,首次将相位展开看作计算机视觉中的标记问题,并结合磁共振相位数据的特点,设计出相位图的模糊质量图,完成相位展开的能量函数构建。针对能量函数的优化求解,采用高效的图割算法进行。展开更多
This contribution proposes a new combination symbol mapper/8-ary constellation, which is a joint optimization of an 8-ary signal constellation and its symbol mapping operation, to improve the performance of Bit Interl...This contribution proposes a new combination symbol mapper/8-ary constellation, which is a joint optimization of an 8-ary signal constellation and its symbol mapping operation, to improve the performance of Bit Interleaved Coded Modulation with Iterative Decoding (BICM-ID). The basic idea was to use the so called (1,7) constellation (which is a capacitive efficient constellation) instead of the conventional 8-PSK constellation and to choose the most suitable mapping for it. A comparative study between the combinations most suitable mapping/(1,7) constellation and SSP mapping/conventional 8-PSK constellation has been carried out. Simulation results showed that the 1st combination significantly outperforms the 2nd combination and with only 4 iterations, it gives better performance than the 2nd combination with 8 iterations. A gain of 4 dB is given by iteration 4 of the 1st combination compared to iteration 8 of the 2nd combination at a BER level equal to 10-5, and it (iteration 4 of the 1st combination) can attain a BER equal to 10-7 for, only, a SNR = 5.6 dB.展开更多
针对无人机视角下烟雾尺度变化剧烈以及烟雾自身颜色差异大的问题,提出一种无人机航拍图像火灾烟雾检测算法。构建Smoke-YOLO(You Only Look Once)网络,通过跨空间学习的特征交互注意力模块,使用并行子结构增强多层次语义信息,提升特征...针对无人机视角下烟雾尺度变化剧烈以及烟雾自身颜色差异大的问题,提出一种无人机航拍图像火灾烟雾检测算法。构建Smoke-YOLO(You Only Look Once)网络,通过跨空间学习的特征交互注意力模块,使用并行子结构增强多层次语义信息,提升特征提取能力。利用跨层级特征融合模块对不同尺度目标进行融合,提高烟雾检测网络的稳健性。为解决现有公开烟雾数据集单一类别标注忽视烟雾类内差异的问题,给出双类标签映射策略,自建双类别无人机航拍火灾烟雾图像数据集,并采用标签映射模块将双类别烟雾标签统一为烟雾类,在自定义的映射规则中解决统一烟雾类目标时存在的分类冲突问题。实验结果表明,所提算法在自建数据集上比原有YOLOv8模型的准确率、召回率、类别精度分别提升4.4%、7%、6.7%,每秒检测帧数达到314.2,Smoke-YOLO网络在航拍图像火灾烟雾检测任务上具备高效的实时检测和精度优势。展开更多
基金Funded by the National Natural Science Foundation of China(N0.40001019).
文摘This paper discusses and sums up the basic criterions of guaranteeing the labeling quality and abstracts the four basic factors including the conflict for a label with a label,overlay for label with the features,position’s priority and the association for a label with its feature.By establishing the scoring system,a formalized four-factors quality evaluation model is constructed.Last,this paper introduces the experimental result of the quality evaluation model applied to the automatic map labeling system-MapLabel.
文摘相位展开是磁共振成像技术应用中最关键的环节之一,可以为磁共振的某些重要临床应用提供精确的相位信息。然而,由于临床磁共振成像过程中,部分区域真实的相位存在急剧变化,同时伴有不同性态的噪声污染,导致相位展开时存在信息的高度不一致性。为了有效地解决上述难题,基于马尔可夫-最大后验(Markov Random Field& Maximum A Posterioi,MRF-MAP)模型,首次将相位展开看作计算机视觉中的标记问题,并结合磁共振相位数据的特点,设计出相位图的模糊质量图,完成相位展开的能量函数构建。针对能量函数的优化求解,采用高效的图割算法进行。
文摘This contribution proposes a new combination symbol mapper/8-ary constellation, which is a joint optimization of an 8-ary signal constellation and its symbol mapping operation, to improve the performance of Bit Interleaved Coded Modulation with Iterative Decoding (BICM-ID). The basic idea was to use the so called (1,7) constellation (which is a capacitive efficient constellation) instead of the conventional 8-PSK constellation and to choose the most suitable mapping for it. A comparative study between the combinations most suitable mapping/(1,7) constellation and SSP mapping/conventional 8-PSK constellation has been carried out. Simulation results showed that the 1st combination significantly outperforms the 2nd combination and with only 4 iterations, it gives better performance than the 2nd combination with 8 iterations. A gain of 4 dB is given by iteration 4 of the 1st combination compared to iteration 8 of the 2nd combination at a BER level equal to 10-5, and it (iteration 4 of the 1st combination) can attain a BER equal to 10-7 for, only, a SNR = 5.6 dB.
文摘针对无人机视角下烟雾尺度变化剧烈以及烟雾自身颜色差异大的问题,提出一种无人机航拍图像火灾烟雾检测算法。构建Smoke-YOLO(You Only Look Once)网络,通过跨空间学习的特征交互注意力模块,使用并行子结构增强多层次语义信息,提升特征提取能力。利用跨层级特征融合模块对不同尺度目标进行融合,提高烟雾检测网络的稳健性。为解决现有公开烟雾数据集单一类别标注忽视烟雾类内差异的问题,给出双类标签映射策略,自建双类别无人机航拍火灾烟雾图像数据集,并采用标签映射模块将双类别烟雾标签统一为烟雾类,在自定义的映射规则中解决统一烟雾类目标时存在的分类冲突问题。实验结果表明,所提算法在自建数据集上比原有YOLOv8模型的准确率、召回率、类别精度分别提升4.4%、7%、6.7%,每秒检测帧数达到314.2,Smoke-YOLO网络在航拍图像火灾烟雾检测任务上具备高效的实时检测和精度优势。
