To preserve the sharp features and details of the synthetic aperture radar （SAR） image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform ...To preserve the sharp features and details of the synthetic aperture radar （SAR） image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform （NSBT） domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges axe detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture （BLS-GSM）.展开更多
The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circ...The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.展开更多
针对动态环境中实时定位与建图(Simultaneous Localization and Mapping,SLAM)算法位姿估计存在的定位漂移、实时性差等问题,提出一个名为YSG-SLAM的实时语义RGB-D SLAM系统。为了提高系统实时性,新增两个并行线程:一个用于获取二维语...针对动态环境中实时定位与建图(Simultaneous Localization and Mapping,SLAM)算法位姿估计存在的定位漂移、实时性差等问题,提出一个名为YSG-SLAM的实时语义RGB-D SLAM系统。为了提高系统实时性,新增两个并行线程:一个用于获取二维语义信息的语义分割线程,一个语义建图线程。为优化系统在处理动态物体时的准确性和鲁棒性,YSG-SLAM引入快速动态特征剔除算法,并耦合漏检补偿模块来应对基于实时实例分割(You Only Look At Coefficients,YOLACT)算法可能出现的漏检情况,有效地提升了特征点剔除的精确度和系统的整体稳定性。为减少由特征点聚集引起的定位误差从而优化特征点的空间分布,设计自适应角点提取阈值计算方法,使特征分布更加均匀。语义建图线程充分利用二维语义信息与三维点云数据,可选择性构建语义地图和八叉树地图,提高了系统的环境感知能力及机器人在复杂环境下的相关任务执行能力。YSG-SLAM在德国慕尼黑工业大学数据集、Bonn数据集上进行了评估,相较于原ORB-SLAM2,各项定位误差下降达93%。实验结果表明,YSG-SLAM有效提升了系统实时性,定位精度高,且可构建两种地图,具有一定的实用价值。展开更多
基金supported by the National Natural Science Foundation of China(6067309760702062)+3 种基金the National HighTechnology Research and Development Program of China(863 Program)(2008AA01Z1252007AA12Z136)the National ResearchFoundation for the Doctoral Program of Higher Education of China(20060701007)the Program for Cheung Kong Scholarsand Innovative Research Team in University(IRT 0645).
文摘To preserve the sharp features and details of the synthetic aperture radar （SAR） image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform （NSBT） domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges axe detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture （BLS-GSM）.
基金Sponsored by The National Natural Science Foundation of China(60872065)Science and Technology on Electro-optic Control Laboratory and Aviation Science Foundation(20105152026)State Key Laboratory Open Fund of Novel Software Technology,Nanjing University(KFKT2010B17)
文摘The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.
文摘针对动态环境中实时定位与建图(Simultaneous Localization and Mapping,SLAM)算法位姿估计存在的定位漂移、实时性差等问题,提出一个名为YSG-SLAM的实时语义RGB-D SLAM系统。为了提高系统实时性,新增两个并行线程:一个用于获取二维语义信息的语义分割线程,一个语义建图线程。为优化系统在处理动态物体时的准确性和鲁棒性,YSG-SLAM引入快速动态特征剔除算法,并耦合漏检补偿模块来应对基于实时实例分割(You Only Look At Coefficients,YOLACT)算法可能出现的漏检情况,有效地提升了特征点剔除的精确度和系统的整体稳定性。为减少由特征点聚集引起的定位误差从而优化特征点的空间分布,设计自适应角点提取阈值计算方法,使特征分布更加均匀。语义建图线程充分利用二维语义信息与三维点云数据,可选择性构建语义地图和八叉树地图,提高了系统的环境感知能力及机器人在复杂环境下的相关任务执行能力。YSG-SLAM在德国慕尼黑工业大学数据集、Bonn数据集上进行了评估,相较于原ORB-SLAM2,各项定位误差下降达93%。实验结果表明,YSG-SLAM有效提升了系统实时性,定位精度高,且可构建两种地图,具有一定的实用价值。
