本文研究一种新的彩色图像分色算法。该算法采用判决神经网络(Decision-Based Neural Net work),将感知机学习规则和分层非线性网络结构相结合,通过有监督的学习来调整和训练权重,获得最佳的分色判决函数。该算法分色效果理想,能够确保...本文研究一种新的彩色图像分色算法。该算法采用判决神经网络(Decision-Based Neural Net work),将感知机学习规则和分层非线性网络结构相结合,通过有监督的学习来调整和训练权重,获得最佳的分色判决函数。该算法分色效果理想,能够确保原图像的细节及边缘部分不失真或失真很小,并具有较好的抗噪能力。展开更多
To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital imag...To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital image processing and patter recognition. In this model, firstly data samples are collected from some color patches by the Fuzzy C-Means （FCM） method; then a classifier based on the Cerebellar Model Articulation Controller （CMAC） is constructed which is used to recognize color pattern of each pixel in a microscopic halftone image. The principle of color separation and the algorithm model are introduced and the experiments show the effectiveness of the CMAC-based classifier as opposed to the BP network.展开更多
文摘本文研究一种新的彩色图像分色算法。该算法采用判决神经网络(Decision-Based Neural Net work),将感知机学习规则和分层非线性网络结构相结合,通过有监督的学习来调整和训练权重,获得最佳的分色判决函数。该算法分色效果理想,能够确保原图像的细节及边缘部分不失真或失真很小,并具有较好的抗噪能力。
文摘To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital image processing and patter recognition. In this model, firstly data samples are collected from some color patches by the Fuzzy C-Means （FCM） method; then a classifier based on the Cerebellar Model Articulation Controller （CMAC） is constructed which is used to recognize color pattern of each pixel in a microscopic halftone image. The principle of color separation and the algorithm model are introduced and the experiments show the effectiveness of the CMAC-based classifier as opposed to the BP network.
