标题: | 适用于静态背景视讯并以型态学处理物件边缘之即时视讯切割 Real-time Segmentation of Video with Stationary Background Based on Morphological Edge Processing |
作者: | 黄奕善 I-Shan Huang 林大卫 David W. Lin 电机学院电子与光电学程 |
关键字: | 型态学;边缘侦测;Morphological;Canny operator |
公开日期: | 2006 |
摘要: | 在本论文中,我们设计并实现一个在个人电脑上的视讯影像切割系统。此系统可以应用于静态背景的视讯电话及视讯会议。 此系统的基本概念是利用物件的边缘锐化来精准得到移动的物件。首先,我们使用一个两级的杂讯估计方法来估计摄影机的杂讯,并且把此结果拿来当做往后参数调整的参考。为了取得一个较佳的物件遮罩,我们观察六张连续画面的变化情形来取得一个初步的前景以及利用连续两张画面的差异选择一适合的临界值将移动的物件萃取出来。接着,我们利用Canny edge detector侦测出整张画面的边界资讯,利用影像变动侦测所得之遮罩及前一张画面所求得之物件遮罩可以将属于背景的边界移除,然后使用收缩的技巧来取得一个粗略的物件遮罩,并利用此资讯来取得移动物件最外围之边界资讯。为了得到更精确的物件遮罩,我们使用Dijkstra所提之最短路径搜寻演算法,将物件的边缘连接并利用此封闭的物件轮廓将移动的物件给萃取出来。最后的模拟结果显示我们所提的方法,切割出来的物体边界是相当精确的,且在经过我们的加速之后,整个系统能快速且精准的切割出移动的物件。 在1.733-GHz CPU 及1024-MB RAM的个人电脑上且摄影机不移动时,目前的执行速度是每秒约四张CIF Frame及每秒约二十张QCIF Frame,在格式影片的应用上,我们有提出一简化的方法,则每秒约十二张。 In the thesis, we consider the design and implementation of video segmentation on a personal computer. The system can be applied on video conference and videophone with stationary background. The basic idea of the system is a graph-based edge linking technique. At first, we adopt a two staged method for camera noise estimation and those thresholds are adjusted based on the estimated camera noise. To get the change detection mask, we consider six consecutive frames as time of observation to estimate the background and select a suitable threshold to estimate the foreground by two consecutive frames. Next, we use Canny edge detector to get the edge information of entire frame. We use the change detection mask and the moving object mask of previous frame to remove the edges of background. Then, we shrink the object mask to edge map. To refine the object mask, we use Dijkstra’s shortest path search algorithm to link the boundary of moving object and extract the object by the closed contour. Simulation results show that our method can give correct segmentation results. After optimization, the proposed segmentation system can get the moving object accurately and quickly. With a Intel Pentium M 1.733 GHz CPU and 1024-MB RAM, the system can achieve 20 QCIF frames per second and 4 CIF frames per second. We also propose a simple method and it can achieve 12 CIF frames per second. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT009367502 http://hdl.handle.net/11536/80064 |
显示于类别: | Thesis |
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