中国科学院机构知识库网格系统: 检索

中国科学院机构知识库网格

Chinese Academy of Sciences Institutional Repositories Grid

机构

采集方式

内容类型

发表日期

学科主题

筛选

浏览/检索结果: 共3条，第1-3条

帮助

条数/页：排序方式：
	Real-time video compressing under DSP/BIOS (EI CONFERENCE) 会议论文 OAI收割 MIPPR 2009 - Medical Imaging, Parallel Processing of Images, and Optimization Techniques: 6th International Symposium on Multispectral Image Processing and Pattern Recognition, October 30, 2009 - November 1, 2009, Yichang, China Chen Q.-P.; Li G.-J. 收藏 \| 浏览/下载：31/0 \| 提交时间：2013/03/25 This paper presents real-time MPEG-4 Simple Profile video compressing based on the DSP processor. The programming framework of video compressing is constructed using TMS320C6416 Microprocessor the architecture level optimizations are used to improve software pipeline. The system used DSP/BIOS to realize multi-thread scheduling. The whole system realizes high speed transition of a great deal of data. Experimental results show the encoder can realize real-time encoding of 768*576 TDS510 simulator and PC. It uses embedded real-time operating system DSP/BIOS and the API functions to build periodic function 25 frame/s video images. 2009 Copyright SPIE - The International Society for Optical Engineering. tasks and interruptions etcs. Realize real-time video compressing. To the questions of data transferring among the system. Based on the architecture of the C64x DSP utilized double buffer switched and EDMA data transfer controller to transit data from external memory to internal and realize data transition and processing at the same time
	Real-time quality control on a smart camera (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China Xiao C.; Zhou H.; Li G.; Hao Z. 收藏 \| 浏览/下载：40/0 \| 提交时间：2013/03/25 A smart camera is composed of a video sensing high-level video processing communication and other affiliations within a single device. Such cameras are very important devices in quality control systems. This paper presents a prototyping development of a smart camera for quality control. The smart camera is divided to four parts: a CMOS sensor a digital signal processor (DSP) a CPLD and a display device. In order to improving the processing speed low-level and high-level video processing algorithms are discussed to the embedded DSP-based platforms. The algorithms can quickly and automatic detect productions' quality defaults. All algorithms are tested under a Matlab-based prototyping implementation and migrated to the smart camera. The smart camera prototype automatic processes the video data and streams the results of the video data to the display devices and control devices. Control signals are send to produce-line to adjust the producing state within the required real-time constrains.
	High-accuracy real-time automatic thresholding for centroid tracker (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Zhang Y. 收藏 \| 浏览/下载：44/0 \| 提交时间：2013/03/25 Many of the video image trackers today use the centroid as the tracking point. In engineering we can get several key pairs of peaks which can include the target and the background around it and use the method of Otsu to get intensity thresholds from them. According to the thresholds it give a great help for us to get a glancing size a target's centroid is computed from a binary image to reduce the processing time. Hence thresholding of gray level image to binary image is a decisive step in centroid tracking. How to choose the feat thresholds in clutter is still an intractability problem unsolved today. This paper introduces a high-accuracy real-time automatic thresholding method for centroid tracker. It works well for variety types of target tracking in clutter. The core of this method is to get the entire information contained in the histogram we can gain the binary image and get the centroid from it. To track the target so that we can compare the size of the object in the current frame with the former. If the change is little such as the number of the peaks the paper also suggests subjoining an eyeshot-window we consider the object has been tracked well. Otherwise their height just like our eyes focus on a target if the change is bigger than usual position and other properties in the histogram. Combine with this histogram analysis we will not miss it unless it is out of our eyeshot we should analyze the inflection in the histogram to find out what happened to the object. In general the impression will help us to extract the target in clutter and track it and we will wait its emergence since it has been covered. To obtain the impression what we have to do is turning the analysis into codes for the tracker to determine a feat threshold. The paper will show the steps in detail. The paper also discusses the hardware architecture which can meet the speed requirement. the paper offers a idea comes from the method of Snakes