【作者】 徐咏驰；

【导师】 周世生；

【作者基本信息】 西安理工大学 ， 制浆造纸工程， 2009， 硕士

【摘要】 网点增大是印刷品质量的主要控制因素之一,而网点面积覆盖率是计算网点增大的基础。长期以来印版网点面积率的检测一直是印刷复制过程中检测技术的难点,以至于业界只得将印品和胶片上的网点面积率之差定义为印刷过程中的网点扩大值。随着直接制版技术(CTP)的快速发展和在实际生产中广泛的应用,直接测量印版的网点面积率成了CTP过程中质量控制的关键,同时也是对印刷品质量进行有效控制的基础。针对印版图像的反差小、网点边缘模糊的特点,以及暗调和亮调层次的网点显微图像难于分割的情况,本课题在通用的图像分割算法中,有目的地选用善长于分割一般模糊图像的最大类间方差法、模糊c均值聚类算法(FCM)和脉冲耦合神经网络(PCNN)算法等三种方法,进行印版图像分割试验,以国际印刷行业认可的iCPlateⅡ印版检测仪测量数据作为测量标准,研究适于印版显微图像的分割算法。文中在最大类间方差法原始算法基础上,引进基于二维直方图的最大类间方差算法,并用一次和二次高斯平滑滤波值构成的二维直方图代替由像素灰度值和像素的邻域灰度均值构成的二维直方图,建立了基于改进二维直方图的最大类间方差法,较好地实现对印版图像进行自适应分割。研究中使用高斯平滑滤波器取代均值滤波器,改进了基于二维直方图加权的FCM算法。首次引入了平滑因子m的自适应算法。同时采用特定的预处理方法,建立了适于印版图像的基于改进二维直方图加权及平滑因子m自适应的FCM算法。本课题首次将PCNN算法引入到印版显微网点图像的分割中。为了更好地适应印版图像分割的应用要求,研究中对基本模型进行了简化和改进,并且摒弃了常用的收敛准则,结合点火频率思想建立了一种基于点火频率的改进脉冲耦合神经网络图像分割新方法。在课题研究中对三种典型算法分别作了改进,取得了令人满意的结果,获得了能够测量小反差印版图像并具有较高精度的印版网点图像自适应二值化分割算法,为开发出具有自主知识产权的印版网点图像检测仪进行了有实际意义的探索。更多还原

【Abstract】 Dot area coverage is the foundation of computing dot gain which is one of the major factors to control quality of printing. For long time, due to the difficulty of accuracy detection of dot area coverage on the plate image, only the dot aera coverage difference between printing material and film has been adopted by the industry instead of the real dot gain. With rapid development of the Computer-To-Plate (CTP) technology, the direct detection of dot area coverage on the plate become one of the key technologies to control quality during printing and copy processes.With regards to the characteristic of low contrast in plate image and fuzzy dot edge, three segmentation algorithms were applied in this paper, which are good at processing fuzzy images, to find the segmentation algorithms which are suitable for plate microscopic image. They are the Maximum Between-Cluster Variance algorithm, the Fuzzy C-Means clustering algorithm and the Pulse Coupled Neural Network algorithm. During the research, detecion data collected by iCPlate II which is recognized by international printing industry as detection standards.During the research, The Maximum Between-Cluster Variance algorithm based on 2D histogram is introduced. Moreover, an improved Maximum Between-Cluster Variance algorithm on the basis of the improved 2D histogram composed of the result of once and twice Guassian filtering is built up. It is proved by experiments that new algorithm can be used as an automatic segmentation algorithm for plate microscopic images.The Fuzzy C-Means clustering algorithm is improved by using a modified weighted 2D histograms, introduing an adaptive computaion of smoothing factor m which is the first time applied in processing plate image and obtaining a special pre-processing method designed for plate microscopic image.For Pulse Coupled Neural Network (PCNN) algorithm, it is the first time to be utilized for plate images. In order to better meet application requirement of image segmentation, the basic neuromime of PCNN is simplified and improved. At the same time, the segmentation method based on fired frequency is implemented instead of using common convergence criteria.The research improves three algorithms and attains satisfactory results. These adaptive binarization algorithms for plate image can produce dot image with higher accuracy with low contrast. The research explores realistically for the purpose of developing plate dot image detecting instrument with independent intellectual property rights.更多还原