在SAR图像机动目标自动识别过程中,因目标预筛选阶段采用次优的异常检测策略而产生大量虚假的感兴趣区域(Region of Interest,ROI),这些虚假ROIs很大程度上降低了目标识别的效率。该文提出一种基于多特征联合的序贯鉴别算法来去除虚假R...在SAR图像机动目标自动识别过程中,因目标预筛选阶段采用次优的异常检测策略而产生大量虚假的感兴趣区域(Region of Interest,ROI),这些虚假ROIs很大程度上降低了目标识别的效率。该文提出一种基于多特征联合的序贯鉴别算法来去除虚假ROIs。该算法首先对ROI切片的目标特征做冗余性、鲁棒性和可分离性的定量分析,以选取互补性强、稳定好的最优特征,并按所选特征鉴别性能的优略进行排序,来构建序贯鉴别的观测矢量,然后利用各鉴别特征的统计模型和设定的虚警概率来计算各特征对应判决阈值,最后联合优选的多个特征进行序贯判决。文中利用X波段的MSTAR数据验证了本文的算法,并与二项式距离鉴别算法做性能比较。展开更多
针对现有跨垄式采茶机导航中心线提取效率低的问题,该研究提出一种基于机器视觉跟踪生长ROI茶垄间导航线提取算法。首先采用固定ROI(region of interest)方法,选取图像左下方区域为第一块ROI,在ROI内进行超绿指数灰度化,最大类方差法分...针对现有跨垄式采茶机导航中心线提取效率低的问题,该研究提出一种基于机器视觉跟踪生长ROI茶垄间导航线提取算法。首先采用固定ROI(region of interest)方法,选取图像左下方区域为第一块ROI,在ROI内进行超绿指数灰度化,最大类方差法分割茶垄道路与背景,通过形态学操作对图像进行增强与降噪,使用最大连通域检测操作提取ROI内的坐标信息与特征点,根据ROI提取的坐标信息动态生成ROI,直到整个图像中所有茶垄道路信息提取完成,最后采用最小二乘法获取跨垄式采茶机底盘在垄间行驶的导航线。该方法经过连续帧测试,处理一帧1920×1080像素图像的平均时间为18 ms,该研究算法与人工提取导航线的航向角平均误差为0.405°,标准差为0.463°,可在一定杂草、落叶干扰的情况下完成导航角提取。展开更多
Semantic Communication(SemCom)can significantly reduce the transmitted data volume and keep robustness.Task-oriented SemCom of images aims to convey the implicit meaning of source messages correctly,rather than achiev...Semantic Communication(SemCom)can significantly reduce the transmitted data volume and keep robustness.Task-oriented SemCom of images aims to convey the implicit meaning of source messages correctly,rather than achieving precise bit-by-bit reconstruction.Existing image SemCom systems directly perform semantic encoding and decoding on the entire image,which has not considered the correlation between image content and downstream tasks or the adaptability to channel noise.To this end,we propose a content-aware robust SemCom framework for image transmission based on Generative Adversarial Networks(GANs).Specifically,the accurate semantics of the image are extracted by the semantic encoder,and divided into two parts for different downstream tasks:Regions of Interest(ROI)and Regions of Non-Interest(RONI).By reducing the quantization accuracy of RONI,the amount of transmitted data volume is reduced significantly.During the transmission process of semantics,a Signal-to-Noise Ratio(SNR)is randomly initialized,enabling the model to learn the average noise distribution.The experimental results demonstrate that by reducing the quantization level of RONI,transmitted data volume is reduced up to 60.53%compared to using globally consistent quantization while maintaining comparable performance to existing methods in downstream semantic segmentation tasks.Moreover,our model exhibits increased robustness with variable SNRs.展开更多
文摘在SAR图像机动目标自动识别过程中,因目标预筛选阶段采用次优的异常检测策略而产生大量虚假的感兴趣区域(Region of Interest,ROI),这些虚假ROIs很大程度上降低了目标识别的效率。该文提出一种基于多特征联合的序贯鉴别算法来去除虚假ROIs。该算法首先对ROI切片的目标特征做冗余性、鲁棒性和可分离性的定量分析,以选取互补性强、稳定好的最优特征,并按所选特征鉴别性能的优略进行排序,来构建序贯鉴别的观测矢量,然后利用各鉴别特征的统计模型和设定的虚警概率来计算各特征对应判决阈值,最后联合优选的多个特征进行序贯判决。文中利用X波段的MSTAR数据验证了本文的算法,并与二项式距离鉴别算法做性能比较。
文摘针对现有跨垄式采茶机导航中心线提取效率低的问题,该研究提出一种基于机器视觉跟踪生长ROI茶垄间导航线提取算法。首先采用固定ROI(region of interest)方法,选取图像左下方区域为第一块ROI,在ROI内进行超绿指数灰度化,最大类方差法分割茶垄道路与背景,通过形态学操作对图像进行增强与降噪,使用最大连通域检测操作提取ROI内的坐标信息与特征点,根据ROI提取的坐标信息动态生成ROI,直到整个图像中所有茶垄道路信息提取完成,最后采用最小二乘法获取跨垄式采茶机底盘在垄间行驶的导航线。该方法经过连续帧测试,处理一帧1920×1080像素图像的平均时间为18 ms,该研究算法与人工提取导航线的航向角平均误差为0.405°,标准差为0.463°,可在一定杂草、落叶干扰的情况下完成导航角提取。
基金supported by the National Science Fund for Excellent Young Scholars(No.62422112).
文摘Semantic Communication(SemCom)can significantly reduce the transmitted data volume and keep robustness.Task-oriented SemCom of images aims to convey the implicit meaning of source messages correctly,rather than achieving precise bit-by-bit reconstruction.Existing image SemCom systems directly perform semantic encoding and decoding on the entire image,which has not considered the correlation between image content and downstream tasks or the adaptability to channel noise.To this end,we propose a content-aware robust SemCom framework for image transmission based on Generative Adversarial Networks(GANs).Specifically,the accurate semantics of the image are extracted by the semantic encoder,and divided into two parts for different downstream tasks:Regions of Interest(ROI)and Regions of Non-Interest(RONI).By reducing the quantization accuracy of RONI,the amount of transmitted data volume is reduced significantly.During the transmission process of semantics,a Signal-to-Noise Ratio(SNR)is randomly initialized,enabling the model to learn the average noise distribution.The experimental results demonstrate that by reducing the quantization level of RONI,transmitted data volume is reduced up to 60.53%compared to using globally consistent quantization while maintaining comparable performance to existing methods in downstream semantic segmentation tasks.Moreover,our model exhibits increased robustness with variable SNRs.
