co-occurrence中文是什么意思

• 共现

• co     Co ＝ 1.cobalt 【化学】钴。 2 ...
• occurrence    n. 1.(事件的)发生，出现，有；【矿物】存象，(矿床 ...
• co-occurrence constraint    共现约束
• co-occurrence matrix    共生矩阵
• co-occurrence rule    同现规则
• co-occurrence score    共现计分
• co-occurrence value    同现值
• feature co-occurrence restriction    特征同现限制 [特性同现限制; 特徵同现限制
• term co-occurrence    词共现方法
• occurrence    n. 1.(事件的)发生，出现，有；【矿物】存象，(矿床等的)埋藏；产地。 2.遭遇，事件，事故。 daily occurrences 日常发生的事。 oscillatory occurrence 振荡现象。 be of frequent [rare] occurrence 是常[少]有的。 make allowance for unfavourable occurrences 留有余地，以防意外。
• a common occurrence    司空见惯
• a ormal occurrence    异常现象
• a rare occurrence    罕见的事件
• abnormal occurrence    异常事件; 异常现象
• accident occurrence    飞行事故
• alleged occurrence    证实存在
• anomalous occurrence    异常产状
• applied occurrence    应用出现; 应用力学; 应用值
• area of occurrence    出事地区; 显示区
• attribute occurrence    属性出现
• be of frequent occurrence    经常发生
• be of rare occurrence    很少发生
• binding occurrence    联编出现
• bound occurrence    界限出现
• chance of occurrence    出现机率

• Among them the gray level co - occurrence matrix ( glcm ) and gray gradient co - occurrence matrix ( ggcm ) methods , which attributed to the statistic textural analysis scheme were then chosen to extract the textural features of five kind areas on satellite images . in the second part the principle of classification and bp neural network were introduced . combined with textural features , the improved bp neural network successfully performed on the classification of the satellite images
  论文的第一部分介绍了进行纹理特征研究的一些典型的方法，利用其中的基于统计的纹理分析法中的灰度共生矩阵以及灰度一梯度共生矩阵法，分析了卫星云图上五类区域的纹理特性；第二部分主要介绍了遥感图像分类原理以及神经网络中的bp算法，在对算法原理进行深入理解的基础上，把纹理特征与神经网络进行组合，实现对卫星云图进行分类分析；第三部分内容是在前面图像分类结果的基础上，对序列图像用相关匹配法进行运动分析，反演云迹风风场。
• First the sampled image is preprocessed , then five features are extracted from the image preprocessed based on spatial gray level co - occurrence matrix , at last the method of measuring and analyzing of skin texture is proved valid through the result of test of training , classifying and recognizing for skin texture images based on tfbp network
  首先对采集到图像进行预处理，然后采用空间灰度共生矩阵法提取纹理图像的5个特征，最后通过tfbp网络对皮肤纹理图像的训练与分类识别实验结果很好的证明了这种纹理分析与测量方法的有效性。
• In this paper , we made an investigation into texture feature extraction and classification based on statistic method and its application in multi - spectral image classification . the research works of this paper have been done as follows : firstly , in order to overcome the weakness of gray level co - occurrence matrix ( glcm ) , a new unsupervised texture segment algorithm , based on multi - resolution model , is presented in this thesis
  本文主要研究了基于纹理统计特性的特征提取与分割方法,并将其用于实际的多光谱图像分类,具体工作如下:第一,针对传统灰度共现阵方法中特征提取的尺度单一问题,本文提出了一种多分辨无监督纹理分割算法。
• Discovery of association rules is an important class of data mining whose aim is to capture the co - occurrences of itemsets , the most important thing to do is to find the large itemsets effectively , because this is time consuming and will finally decide the efficiency of algorithms . so now the main study is emphasized on how to find the large itemsets with more and more few time
  关联规则是从历史的大规模的数据中获得项集之间的相互关联关系，抽取出有用的和感兴趣的模式，主要任务是发现数据库中的大项集，因为这个任务在大规模数据库基础上是耗时的操作，所以现在的主要研究方向都集中在大项集的有效生成上。
• Discovery of association rules is an important class of data mining whose aim is to capture the co - occurrences of itemsets , the most important thing to do is to find the large itemsets effectively , because this is time - consuming and will finally decide the efficiency of algorithms . so now the main study is emphasized on how to find the large itemsets with more and more few times
  关联规则是从历史的大规模的数据中获得项集之间的相互关联关系，抽取出有用的和感兴趣的模式，主要任务是发现数据库中的大项集，因为这个任务在大规模数据库基础上是耗时的操作，所以现在的主要研究方向都集中在大项集的有效生成上。
• Firstly , for the errors of text ’ character and word , utilizing neighborship of character or word , check character and word errors by character string co - occurrence probability . secondly , for the errors of syntax of text , according to statistic and analysis of a large - scale contemporary chinese corpus , recognize the predicate focus word and the others sentence ingredient , check the syntax errors . thirdly , for the errors of text ’ semanteme , establishing semantic dependency relationship tree based on hownet knowledge , presents a method that based on semantic dependency relationship analysis to compute sentence similarity , check the semantic errors
  对于文本字词错误的检查,本文主要利用了字词二元接续关系,根据同现概率检查文本字词错误;对于文本语法错误的检查,本文利用教研室已有的一个大规模语料库,通过对语料库进行统计分析,获得语法查错所需要的语言规律和知识,利用谓语中心词识别和其他句子成分识别的方法,检查文本语法结构上的错误;对于文本语义错误的检查,本文主要利用知网知识得到语义依存树,通过对句子的有效搭配对的相似度计算检查语义错误。
• Because this algorithm only include the first order statistical property , but not take the locations of the modulus extrema into account , the second scheme based on the co - occurrence matrix derived form the discrete wavelet frame modulus extrema is proposed , which includes the partial location information extracted from the co - occurrence matrix of the modulus extrema , and so improves the classification performance
  由于此算法只考虑小波框架模极值的一阶统计特性,并没有考虑模极值的位置关系,所以又提出基于离散小波框架模极值共生距阵的分类算法,新算法中增加了通过共生距阵提取的小波框架模极值的部分位置信息,从而提高了分类算法的性能。
• Based on data of sar images which have been pretreated , we apply the gray - level co - occurrence matrix method , and particularly study some texture features used for the classification of sar images , including difference variance difference averages difference entropy contrasts energy s variance sum variances inverse difference moment and correlation etc . furthermore we have abstracted features of sar images
  文中基于已经过图像预处理的sar图像数据，应用灰度共生矩阵法，研究了常用于sar图像分类的几种纹理特征量，包括差方差、差平均、差熵、对比度、能量、方差、和方差、逆差矩、相关等，进行了特征提取。
• This dissertation deals with the content - based image retrieval ( cbir ) theory and technique ; some new features and tools for more concisely and discriminatingly charactering the content of an image are proposed , such as region - based color histogram , grey - primitive co - occurrence matrix , ratio of centripetal moment , ratio of eccentric moment and ratio of inertial moment . a new modified genetic algorithm is also described in this dissertation , which can upgrade the performance of standard genetic algorithm ( sga ) while used in image segmentation
  本文以图像数据库检索为主线，讨论了基于视觉内容的图像检索方法，提出包括基于区域颜色直方图、灰度?基元共生矩阵及向心矩比、偏心矩比、惯性矩比的特征描述方式；对遗传算法存在的早熟、收敛到最优解慢等问题提出了解决方法，并将改进遗传算法应用到图像分割中，编制了相应程序。

• co-occurrenceとは意味：<→COOCCURRENCE>