We consider the extraction of accurate silhouettes of foreground objects in combined color image and depth map data.This is of relevance for applications such as altering the contents of a scene,or changing the depths...We consider the extraction of accurate silhouettes of foreground objects in combined color image and depth map data.This is of relevance for applications such as altering the contents of a scene,or changing the depths of contents for display purposes in 3DTV,object detection,or scene understanding.To展开更多
视频卫星稳像是实现卫星视频高精度应用的前提和基础。由于卫星姿态指向精度不足以及平台姿态稳定度不足等原因,通常需要引入基于图像配准的稳像技术以实现视频凝视的效果;然而在观测海上目标时,由于没有控制点标校,帧间无法开展基于特...视频卫星稳像是实现卫星视频高精度应用的前提和基础。由于卫星姿态指向精度不足以及平台姿态稳定度不足等原因,通常需要引入基于图像配准的稳像技术以实现视频凝视的效果;然而在观测海上目标时,由于没有控制点标校,帧间无法开展基于特征点的配准,所以天基凝视视频相机在观测时经常会出现目标在像面上反复跳变的问题。提出一种基于海上多目标舰船检测的全局前景视频稳像GFVS(global foreground video stabilization)方法,构建高斯误差模型,通过优化后位置和原始位置的偏差修正像面错位,最后进行稳像视频合成。实验结果表明,该方法能够有效解决海上控制点不足时抖动图像难以配准的问题,得到更加稳定的凝视视频效果,应用吉林一号卫星星座采集的两组卫星数据进行验证实验,最终稳像的误差能够控制在0.9个像素以内。展开更多
Motionless foreground objects are key targets in applications of home care monitoring and abandoned object detection, and pose a great challenge to foreground detection. Most algorithms incorporate the motionless fore...Motionless foreground objects are key targets in applications of home care monitoring and abandoned object detection, and pose a great challenge to foreground detection. Most algorithms incorporate the motionless foreground objects into their background models because they have to adapt to environmental changes. To overcome this challenge, a foreground detection method based on nonlinear independent component analysis （ICA） was proposed. Considering that each video frame was actually a nonlinear mixture of the background image and the foreground image, the nonlinear ICA was employed to accurately separate the independent components from each frame. Then, the entropy of grayscale image was calculated to classify which resulting independent component was the foreground image. The proposed nonlinear ICA model was trained offiine and this model was not updated online, so the method can cope with the motionless foreground objects. Experimental results demonstrate that, the method achieves remarkable results and outperforms several advanced methods in dealing with the motionless foreground objects.展开更多
移动主体获得准确的定位信息是构建稳定的混合现实(mixed reality,MR)系统的关键,然而MR中的前景对象对传统定位算法的精度影响较大.现阶段基于深度学习的定位算法可以通过识别前景对象来提升精度,但深度学习模型耗时过高,导致算法实时...移动主体获得准确的定位信息是构建稳定的混合现实(mixed reality,MR)系统的关键,然而MR中的前景对象对传统定位算法的精度影响较大.现阶段基于深度学习的定位算法可以通过识别前景对象来提升精度,但深度学习模型耗时过高,导致算法实时性下降.针对该问题,提出了一种MR中融合语义特征传播模型的前景对象感知定位算法.该算法依托语义分割网络与一种快速旋转的二进制独立稳定描述子特征(oriented fast and rotated binary robust independent elementary feature,ORB)提取算法构建了语义特征传播模型,实现高速语义特征提取;融合该模型和几何特征检测方法实现算法中的前景对象感知层,并依赖该感知层剔除MR中前景对象的特征点,构建了背景特征点集,实现高精度、高实时性的定位.实验结果表明:在慕尼黑工业大学(Technical University of Munich,TUM)公共数据集的高动态前景对象场景中,相比动态语义视觉同步定位与建图(dynamic semantic visual simultaneous localization and mapping,DS-SLAM)算法,该算法相对位姿误差降低了60.5%,定位实时性提升了39.5%,可见该算法在MR中具有较高的应用价值.展开更多
基金supported by Key Project No. 61332015 of the National Natural Science Foundation of ChinaProject Nos.ZR2013FM302 and ZR2017MF057 of the Natural Science Found of Shandong
文摘We consider the extraction of accurate silhouettes of foreground objects in combined color image and depth map data.This is of relevance for applications such as altering the contents of a scene,or changing the depths of contents for display purposes in 3DTV,object detection,or scene understanding.To
文摘视频卫星稳像是实现卫星视频高精度应用的前提和基础。由于卫星姿态指向精度不足以及平台姿态稳定度不足等原因,通常需要引入基于图像配准的稳像技术以实现视频凝视的效果;然而在观测海上目标时,由于没有控制点标校,帧间无法开展基于特征点的配准,所以天基凝视视频相机在观测时经常会出现目标在像面上反复跳变的问题。提出一种基于海上多目标舰船检测的全局前景视频稳像GFVS(global foreground video stabilization)方法,构建高斯误差模型,通过优化后位置和原始位置的偏差修正像面错位,最后进行稳像视频合成。实验结果表明,该方法能够有效解决海上控制点不足时抖动图像难以配准的问题,得到更加稳定的凝视视频效果,应用吉林一号卫星星座采集的两组卫星数据进行验证实验,最终稳像的误差能够控制在0.9个像素以内。
基金National Natural Science Foundations of China(Nos.61374097,61601108)the Fundamental Research Funds for the Central Universities,China(No.N130423006)the Foundation of Northeastern University at Qinhuangdao,China(No.XNK201403)
文摘Motionless foreground objects are key targets in applications of home care monitoring and abandoned object detection, and pose a great challenge to foreground detection. Most algorithms incorporate the motionless foreground objects into their background models because they have to adapt to environmental changes. To overcome this challenge, a foreground detection method based on nonlinear independent component analysis （ICA） was proposed. Considering that each video frame was actually a nonlinear mixture of the background image and the foreground image, the nonlinear ICA was employed to accurately separate the independent components from each frame. Then, the entropy of grayscale image was calculated to classify which resulting independent component was the foreground image. The proposed nonlinear ICA model was trained offiine and this model was not updated online, so the method can cope with the motionless foreground objects. Experimental results demonstrate that, the method achieves remarkable results and outperforms several advanced methods in dealing with the motionless foreground objects.
文摘移动主体获得准确的定位信息是构建稳定的混合现实(mixed reality,MR)系统的关键,然而MR中的前景对象对传统定位算法的精度影响较大.现阶段基于深度学习的定位算法可以通过识别前景对象来提升精度,但深度学习模型耗时过高,导致算法实时性下降.针对该问题,提出了一种MR中融合语义特征传播模型的前景对象感知定位算法.该算法依托语义分割网络与一种快速旋转的二进制独立稳定描述子特征(oriented fast and rotated binary robust independent elementary feature,ORB)提取算法构建了语义特征传播模型,实现高速语义特征提取;融合该模型和几何特征检测方法实现算法中的前景对象感知层,并依赖该感知层剔除MR中前景对象的特征点,构建了背景特征点集,实现高精度、高实时性的定位.实验结果表明:在慕尼黑工业大学(Technical University of Munich,TUM)公共数据集的高动态前景对象场景中,相比动态语义视觉同步定位与建图(dynamic semantic visual simultaneous localization and mapping,DS-SLAM)算法,该算法相对位姿误差降低了60.5%,定位实时性提升了39.5%,可见该算法在MR中具有较高的应用价值.
