人体运动目标的检测与跟踪

人体运动目标的检测与跟踪技术是计算机视觉的主要研究方向之一,在生物医学,人机交互,虚拟现实,智能安全监控,机器人技术,图像压缩,计算机图形学等领域都有着广泛的应用。

运动目标的检测就是从视频流中去除静止的背景,检测出运动的目标及携带的运动信息,运动目标的检测对跟踪等后期处理非常关键[1]。目前,运动目标的检测方法主要有四种:背景差分法、帧间差分法、光流法、基于特征的方法。

现有的目标跟踪方法主要有两类:一类是基于相关的目标跟踪。这是一种先检测后跟踪的方法,它适用于目标之间相互作用较小和背景较简单的情况 ;另一类是基于特征的目标跟踪。这是一种先跟踪后检测的方法,跟踪的结果需要检测来校正[2]。 Continue reading “人体运动目标的检测与跟踪”

Paper proposals, and my first visit to USTC

It was Jan 11 to Jan 12, the day for my paper proposals. I had got a really bad cough, but managed to get my ass to Hefei, Anhui. Thanks lucy, it was she who accompanied me to the train station, 6 am, early before dawn. Maybe it was all because of that, I got my proposal passed, XDXD.

My proposal was about something I’d done in my previous company, which is OpenCV related. Revisions after revisions, I narrowed the topic to face detect (and related) algorithms only by my tutor’s suggestion. Continue reading “Paper proposals, and my first visit to USTC”

数据挖掘小论文 My draft version of data mining course thesis

分类挖掘在图像识别领域的应用

韦国华 (中国科技技术大学 软件工程硕士 上海四期班, 上海 200333)

朱  明 (中国科技技术大学 自动化系, 安徽 合肥 230051)

摘要:视频处理和识别系统是一个较为复杂的计算机软件系统。其处理和识别的结果需要有一个好的可信性分类方法和一个自动化分类工具。目前我们在一些系统上仍然需要人工干预来实现整个系统的完整运行和执行,然而人工的干预工作量大,其判别结果易受人为因素的影响很大,且存在视觉易疲劳和检测速度缓慢等问题,给最终的结果带来很大的干扰。这里我们介绍一种针对一些特定的图像段按色差自动分类的方法,使用从室外采集到的一些随机图像样本实例及其已知的特征数据,将各个图像段进行分类,并对其结果作出客观评估,为提升识别率提供依据。

关键字:数据挖掘;图像处理;分类挖掘

Classification mining in the field of image recognition

Wei Guo Hua1,  Zhu Ming2

(1.Department of Automation, University of Science and Technology of China, Shanghai, China; 2. Department of Automation, University of Science and Technology of China, Hefei, China;)

Abstract Imaging processing & identification is a kind of complex software system. It needs an effective way and a automatic classified tool to test it for keep it credibly. Today, in lots of image processing or intellegent systems, we still need some manual intervention to keep or assure that they can work exactly and perfectly in accuracy and integrity. however manual intervention also brings with plenty of malign influence on the final result, which may get things even worse. Here we introduce a way of automatic classification using some specified image segamentations which were drawn randomly from a database of 7 outdoor images, classify them with DM classifier, and try to evaluate the results, lets see how classifaction and relevant algorithms can help and improve the accuracy of image recognition.

Keywords: Data mining, Image processing, Classification

Continue reading “数据挖掘小论文 My draft version of data mining course thesis”