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[Video Capture] ceph-0.13.tar DL : 0: 分布文件系统 Ceph是基于California大学存储系统研究中心研究成果的LGPL项目-v0.13 released We’ve made a v0.13 release. This mostly fixes bugs with v0.12 that have come up over the past couple weeks: * [ku]lcient: fix sync read vs eof, lseek(…, SEEK_END) * mds: misc bug fixes for multiclient file access But also a few other big things: * osd: stay active during backlog generation * osdmap: override mappings (pg_temp) * kclient: some improvements in kmalloc, memory preallocation The OSD changes mean that the storage cluster can temporarily delegate authority for a placement group to the node that has the complete data while an index is being generated for recovery (that can take a while). Once that’s ready, control will fall back to the new/correct node and the usual recovery will kick in. The disk format and wire protocols have changed with this version. We’re continuing to work on the security infrastructure… hopefully will be ready for v0.14. Here are the relevant URLs: * Git tree at git://ceph.newdream.n
Date : 2025-12-20 Size : 3.9mb User : whoami
[Video Capture] GMM DL : 0: 针对摄像机固定下的复杂背景环境，对采集到的视频图像的图像数据用混合高斯背景建模方法实现前景/背景分割，实现运动目标检测和跟踪。在进行前景检测前，先对背景进行训练，对图像中每个背景采用一个混合高斯模型进行模拟，每个背景的混合高斯的个数可以自适应。然后在测试阶段，对新来的像素进行GMM匹配，如果该像素值能够匹配其中一个高斯，则认为是背景，否则认为是前景。由于整个过程GMM模型在不断更新学习中，所以对动态背景有一定的鲁棒性。最后通过对一个有树枝摇摆的动态背景进行前景检测，取得了较好的效果。-For complex background environment under fixed camera, the image data captured video images using Gaussian mixture background modeling method implementation foreground/background segmentation, detecting and tracking moving objects. Before foreground detection, the first training background, the background of each image using a Gaussian mixture model to simulate the number of each Gaussian mixture background adaptively. Then in the testing phase, the pixels on the new GMM match, if the pixel value is able to match one of the Gaussian, then that is the background, otherwise considered a prospect. Since the whole process is continuously updated GMM model in the study, so the dynamic context of a certain robustness. Finally, on a tree swing dynamic background foreground detection, achieved good results.
Date : 2025-12-20 Size : 4.94mb User : axin
[Video Capture] Video-denoising-using-2D-and-3D-dual-tree-complex DL : 0: The denoising of video data should take into account both temporal and spatial dimensions, however, true 3D transforms are rarely used for video denoising. Separable 3-D transforms have artifacts that degrade their performance in applications. This paper describes the design and application of the non-separable oriented 3-D dual-tree wavelet transform for video denoising. This transform gives a motion-based multi-scale decomposition for video — it isolates in its subbands motion along different directions. In addition, we investigate the denoising of video using the 2-D and 3-D dual-tree oriented wavelet transforms, where the 2-D transform is applied to each frame individually.
Date : 2025-12-20 Size : 397kb User : bamerni