We discussed the techniques of image texture segmentation in details , and compared the differences . finally , we pointed out the main problems in existing methods 对遥感图像的纹理分割技术进行了详细讨论，然后对常用的纹理分割方法进行了分析比较，并提出了现有方法存在的主要问题。
By comparing different texture image segmentation method , this chapter presents a method of texture image segmentation using discrete wavelet transform . the chapter four is about segmentation algorithm - feature image method 介绍了常用的纹理分割方法。通过对不同的纹理图象分割方法的优劣比较，提出了使用离散的小波变换进行纹理图象的分割。
In this thesis , we improve the bemd method which is introduced by j . c . nunes et al . our approach has good effects in texture segmentation , image denoising and image decomposition without parameter 本文根据j . c . nunes等人提出的bemd的基本思想,进行了一定的改进,实现了基于bemd方法的图像无参数分解、纹理分割和图像降噪,取得了不错的效果。
3 . three texture segmentation models are analyzed thoroughly combined with basic characteristics of region target and a texture segmentation model is presented according to the character of residential areas . 4 结合遥感影像中面状目标的纹理特征，深入分析了前人提出的三种纹理分割模型，并结合居民地的特点提出了基于几何结构信息的纹理分割模型。
Secondly , texture segmentation based on local binary pattern ( lbp ) texture descriptor is very time consuming . for one thing , we adjust the computing method of lbp , for another , set two merging threshold instead of only one 第二,针对局部二进制模板纹理分割速度较慢的问题,本文一方面调整了模板算子的计算方式,另一方面将单一门限的区域合并改进为双门限合并。
( 3 ) the estimated feature is n ' t accurate if window size is small when the segmentation makes use of sar model . otherwise the border of segmentation is n ' t exact because of the transition of feature at the border ( 3 )利用sar模型进行纹理分割时，窗口取的过小，将使估计的特征值不准确；过大，将使分割图像边界的定位不精确，这是由于在边界处所提取的特征值存在过渡。
Texture analysis composes of texture description , texture segment , texture classification , texture retrieval , etc . in this paper , some approaches on texture feature extraction for purpose of classification and retrieval are discussed and studied 纹理分析的研究主要为纹理描述、纹理分割、纹理分类、纹理检索等方面。本论文概述了早期和近期的多种纹理分析方法，主要研究和讨论了基于特征的纹理特征提取、分类和检索方法。
The experiments on the images of triangle , rectangle and plane approve the correctness and validity of line segment method . 4 chapter four is devoted to summarize moment applications in the fields of image processing , computer vision , and pattern recognition . these applications mainly include scene matching , histogram matching , image reconstruction , image compression , symmetry detection , image normalization , texture segmentation , edge detection , aircraft recognition , and image indexing , etc . 4 、综述了矩技术在图像处理、计算机视觉、和模式识别领域内的应用，主要包括：景物匹配、直方图匹配、图像重建、图像压缩、对称性检测、图像规格化、纹理分割、边缘检测、飞机识别和图像检索等等。
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。
It first finds the best features that are extracted from glcm and explain the texture clearly in different resolution , and then segments on different level , at last , by combining the structure information of texture edge , extract the edge of different patterns to get a relatively accurate result of texture segmentation 该算法有效的利用了由灰度共现阵得到的、不同分辨率上最能表述纹理特性的统计特征,分层次对图像进行分割,然后结合纹理的结构信息对边缘区域进行边界定位,进而得到较准确的纹理分割图。
