ID 原文 译文
22795 为免阈值增加而影响算法效率，将人工记忆原理引入分子动理论优化算法，设计了一种基于记忆分子动理论优化算法的多目标图像分割模型求解方法。 In order to improve the efficiency of the algorithm, a memory knetic-molecular theory optimization algorithm is proposed for the multi-objective cross section projection Otsu's method by introducing the artificial memory principles into knetic-molecular theory optimization algorithm.
22796 实验表明：该方法分割准确、抗噪性强、鲁棒性好，对含不同噪声的图像更具普适性。 The experimental results show that this method has significant advantages in segmentation accuracy, anti-noise capability and robustness, and is more universal applicability for images with different noises.
22797 为了自动确定多光谱遥感影像中地物目标类别数，该文提出一种基于可变类模糊 C 均值(Fuzzy C-Means, FCM)的多光谱遥感影像分割方法。 In order to automatically determine the number of clusters in multispectral remote sensing image segmentation, Fuzzy C-Means (FCM) algorithm with unknown number of clusters is proposed.
22798 首先定义像素与聚类的非相似性测度并据此构建目标函数，而后通过求解目标函数得到最优模糊隶属度和聚类中心。 First of all, a new dissimilarity measure between a pixel and a cluster is defined. The fuzzy membership function and cluster center are obtained through minimizing the objective function.
22799 其次，研究模糊因子与影像地物目标类别数的关系，并通过定义划分熵(Partition Entropy, PE)指数优选模糊因子，选择 PE 指数值稳定收敛后所对应的最小模糊因子值为最优模糊因子，根据模糊因子与类别数的关系得到最优类别数，从而实现了影像的可变类分割。 Then, the relationship between fuzzy factor and the number of clusters is studied. The optimal fuzzy factor is selected by defining the Partition Entropy (PE) index and corresponding to the minimum of fuzzy factor after the convergence of PE values. According to the relationship between the fuzzy factor and the number of clusters, the optimal number of clusters is obtained, and the variable cluster segmentation of the image is realized.
22800 最后，利用提出算法分别对合成和真实多光谱遥感影像进行分割实验，实验结果表明，提出算法不仅能自动确定影像的最优类别数，还能获得较好的分割结果，为实现自动确定遥感影像中地物目标类别数提供新方法。 The analysis based on segmentation results of synthesized image and real multispectral remote sensing images show that the proposed algorithm can automatically determine the number of clusters and obtain the ideal segmentation results simultaneously. It provides a new method for automatically determine the number of clusters of remote sensing image.
22801 针对无线网络中压缩编码及无线丢包等因素对移动终端视频的降质影响，在分析视频相邻帧差信号空-时感知统计特性的基础上，该文提出一种基于视频自然统计特性的无参考移动终端视频质量评价(NMVQA)算法。 Considering the influence of compression and wireless channel packet-loss on mobile video quality in wireless network, analyzing the space-time perceptual statistics of the differences between video adjacent frames, a No-reference Mobile Video Quality Assessment (NMVQA) algorithm is proposed based on video natural statistics.
22802 进行视频帧差空-时自然统计规律分析，确定移动终端视频失真类型对视频相邻帧差系数统计特性的影响； First, the influences of various video distortion type on the statistical characteristics of difference coefficients between video adjacent frames are analyzed in terms of the natural statistical regularities of video frame difference.
22803 计算水平、垂直、主对角线和副对角线方向的帧差相邻系数乘积分布参数的时域统计特性； Second, the temporal change of the distribution parameters with respect to the products of adjacent frame differences computed along horizontal, vertical and diagonal spatial orientations are calculated.
22804 以多尺度帧差相邻系数的时域统计特性相关程度来衡量移动终端视频失真程度。 Finally, the distortion degree of mobile video is measured by the correlation between the multi-scale temporal changes of statistical characteristics of difference coefficients between video adjacent frames.