【作者】 赵广鹏；

【导师】 段中兴；

【作者基本信息】 西安建筑科技大学 ， 计算机系统结构， 2010， 硕士

【摘要】 医用液体在生产过程中混入的杂质严重危害了使用者的生命健康。目前采用的人工检测方法受灯检工主观性影响而导致检测效果不够理想,研究医用液体杂质自动检测系统具有重要的理论意义和应用价值。本文首先介绍了液体杂质检测技术的研究背景和研究现状,并对涉及的理论与技术进行了探讨。在分析数字图像处理技术常用算法的基础上,针对杂质图像的特征,着重介绍了与液体杂质检测系统相关的图像增强、图像分割和二值数学形态学等图像处理技术。接着,介绍了脉冲耦合神经网络,分析了其基本模型和简化模型。针对模型中参数需要重新设定的缺陷,研究了自适应的脉冲耦合神经网络,并将最小二乘法准则和梯度下降法融合其中,解决了脉冲耦合神经网络点火时间序列对光照敏感度的需求。在此基础上,结合数字图像处理的相关理论,研究了基于脉冲耦合神经网络的图像增强、图像分割、区域标记和面积计算方法。最后,设计了以DM642为核心处理器的嵌入式杂质检测系统,并对系统中的硬件和软件分别进行了详细的分析和设计。系统硬件主要包括视频采集、图像处理和控制单元三个部分。软件实现了视频采集、图像增强、杂质检测、杂质识别和不合格品剔除等功能,并进行了实验测试,实验结果证明了所提算法和设计的系统满足在线检测要求。更多还原

【Abstract】 The impurities which mixed in the medical liquid in the production process has seriously harm to the health of patients. As the result of the manual detection methods which affected by the subjectivity is not satisfactory, therefore the research of automatic detection system for medical liquid impurity has great theoretical significance and application value.Firstly, the related theory and technology of the detection of liquid impurities are introduced. For the characteristic of impurities image, the digital image processing technology and commonly used algorithms which related to the detection system of liquid impurities were analyzed, such as image enhancement, image segmentation and binary image with mathematical morphology.Secondly, the basic model and the simplified model of the pulse coupled neural network were analyzed. As the shortcomings of parameters need to be re-set in the model, the least-squares approach and gradient descent algorithm have been integrated in the adaptive pulse coupled neural network, which solved the demand of ignition time series to the light sensitivity. On this basis, the image enhancement, image segmentation, region labeling algorithm and area calculation method based on pulse coupled neural network which combined with the relevant theory of digital image processing have been researched.Finally, the impurity detection embedded system has been designed based on the processor DM642, and the hardware and software analysis and design of the system have been given separately in detail. The system hardware includes three parts:video capture, image processing and control unit, and the following functions which include system video capture, image enhancement, impurity detection, impurity identification and substandard products removal have been implemented in software. The experimental results show that the proposed algorithm and designed system meets the online detection requirements.更多还原