杨兆选
数字视频处理与传输技术;微处理器(DSP、嵌入式系统、单片机)应用技术;信息融合技术及其应用。
个性化签名
- 姓名:杨兆选
- 目前身份:
- 担任导师情况:
- 学位:
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学术头衔:
博士生导师
- 职称:-
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学科领域:
信息处理技术
- 研究兴趣:数字视频处理与传输技术;微处理器(DSP、嵌入式系统、单片机)应用技术;信息融合技术及其应用。
杨兆选,男,天津大学电子信息工程学院教授,博士研究生导师。平板显示协会理事。
研究方向
数字视频处理与传输技术
微处理器(DSP、嵌入式系统、单片机)应用技术
信息融合技术及其应用
学习与工作简历
1969年7月天津大学本科毕业后留校任教;
1983年7月~1998年9月 天津大学讲师、副教授;
1998年10月至今 天津大学电信学院信号与信息处理学科教授、博士生导师
研究成果
1998-2000年"863"项目"轿车发动机电喷系统用新型传感器的开发",通过教育部鉴定;
1999--2001年,负责完成公司委托"42吋等离子电视机研制" 、"17吋液晶电视开发研究" 、"平板显示器关键技术研究"项目,成果应用于产品设计;
2003--2005年负责完成天津交管局系统协作项目"交通信息视频检测系统",通过市科委鉴定。
荣誉和奖励
1993年获教育部科技进步三等奖;
2002年获教育部技术发明二等奖。
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主页访问
1038
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关注数
0
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成果阅读
111
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成果数
3
【期刊论文】Digital Signal Processing Based Real Time Vehicular Detection System*
杨兆选, (杨兆选), (林涛), (李香萍), (刘春义), (高健)
Iransactions of Jianjun University, Vol. 11, No.2, Apr.2005, 119-124,-0001,():
-1年11月30日
Traffic monitoring is of major importance for enforcing traffic management policies. To ac-complish this task, the detection of vehicle can be achieved by exploiting image analysis techniques. In this paper, a solution is presented to obtain various traffic parameters through vehicular video de-tection system(VVDS). VVDS exploits the algorithm based on virtual loops to detect moving vehicle in real time. This algorithm uses the background differencing method, and vehicles can be detected through luminance difference of pixels between background image and current image. Furthermore a novel technology named as spatio-temporal image sequences analysis is applied to background differencing to improve detection accuracy. Then a hardware implementation of a digital signal pro-cessing (DSP) based board is described in detail and the board can simultaneously process four-channel video from different cameras. The benefit of usage of DSP is that images of a roadway can be processed at frame rate due to DSP's high performance. In the end, VVDS is tested on real-world scenes and experiment results show that the system is both fast and robust to the surveillance of transportation.
intelligent transportation system, vehicular detection, digital signal processing, loop em-ulation, background differencing
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【期刊论文】Automatic Vehicle License Recognition Based on Video Vehicular Detection System*
杨兆选, (杨兆选), (陈杨), (何英华), (吴骏)
Transactions of Tianjin University Vol. 12, No.3, Jun. 2006, 199-203,-0001,():
-1年11月30日
Traditional methods of license character extraction cannot meet the requirements of recog-nition accuracy and speed rendered by the video vehicular detection system. Therefore, a license plate localization method based on multi-scale edge detection and a character segmentation algo-rithm based on Markov random field model is presented. Results of experiments demonstrate that the method yields more accurate license character extraction in contrast to traditional localization method based on edge detection by difference operator and character segmentation based on threshold. The accuracy increases from 90% to 94% under preferable illumination, while under poor condition, it increases more than 5%. When the two improved algorithms are used, the accuracy and speed of automatic license recognition meet the system's requirement even under the noisy cir-cumstance or uneven illumination.
vehicle license recognition, license plate localization, character segmentation
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【期刊论文】The Application of Wavelet Neural Network with Orthonormal Bases in Digital Image Denoising
杨兆选, Deng-Chao Feng, , Zhao-Xuan Yang, and Xiao-Jun Qiao
,-0001,():
-1年11月30日
The resource of image noise is analysized in this paper. Considering the image fuzzy generated in the process of image denoising in spatial field, the image denoising method based on wavelet neural network with orthonormal bases is elaborated. The denoising principle and construction method of orthonormal wavelet network is described. In the simulation experiment, median filtering, adaptive median filtering and sym wavelet neural network with orthonormal bases were used separately in the denoising for contaminated images. The experiment shows that, compared with traditional denoising method, image denoising method based on orthonormal wavelet neural network improves greatly the image quality and decreases the image ambiguity.
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