机器视觉chapter08.ppt

1、第八章 纹 理,A typical textured image.For materials such as brush, grass, foliage and water, our perception of what the material is is quite intimately related to the texture.,Brick Brick Stone,8.1 概 述 (1) 纹理定义： 纹理是指图像强度局部变化的重复模式。,Texture tells us information about spatial arrangement of the colors or in

2、tensities in an image.,描述性定义： Firstly, views of large numbers of small objects are often best thought of as textures. Secondly, many surfaces are marked with orderly patterns that look like large numbers of small objects. 纹理元（texel）,由地板砖构成的地板纹理示意图 (a)远距离观察时的纹理图像；(b)近距离观察时的纹理图像,Texture is a phenomeno

3、n that is widespread, easy to recognize and hard to define.,(2) 纹理尺度 Typically, whether an effect is referred to as texture or not depends on the scale at which it is viewed. A leaf that occupies most of an image is an object,but the foliage of a tree is a texture.,(3) 纹理问题 对机器视觉来说，纹理是为了分割和识别场景或物体表面

4、类型而产生的一种视觉标记. 纹理分析包含有三个主要问题： 纹理分类 从给定的一组纹理集中识别给定的纹理区域。 纹理分割 自动确定图像中各种纹理区域之间的边界。 从纹理恢复形状 透视投影产生的纹理模式来确定物体的三维形状。,Texture synthesis seeks to construct large regions of texture from small example mages.We do this by using the example images to build probability models of the texture,and then drawing on

5、the probability model to obtain textured images.,(4) 纹理分析算法分为两大类: 统计分析纹理基元小/微纹理 Statistical analysis: Texture is a quantitative measure of the arrangement of intensities in a region. 结构分析大纹理基元 Structural analysis : Texture is a set of primitive texels in some regular or repeated relationship.,一幅具有三个

6、灰度级的图像,(1) 灰度级同现矩阵(gray-level co-occurrence matrix)Pi,j 一个二维相关矩阵: 规定一个位移矢量d=(dx,dy) 计算被d分开且具有灰度级i和j的所有像素对数 举例,灰度级同现矩阵，距离向量为d(1,1),8.2 纹理分析统计方法,0 1 2 i,(a)棋格图像 (b)距离为d=(1,1)的灰度级同现矩阵 (c)距离为d=(1,0)的灰度级同现矩阵,(a) (b) (c),规范化同现矩阵Ni,j,用于测量灰度级分布随机性的一种特征参数叫做熵，定义为： 注意：当矩阵的所有项皆为零时熵值最高这样的矩阵对应 的图像不存在任何规定位移向量的优先灰度

7、级 用灰度级同现矩阵定义能量特征、对比度特征和均匀度特征：,(2) 自相关法,一幅图像的自相关（Auto-correlation）函数定义为：,测量不同粗细纹理示意图,(3) 用于纹理测量的Law能量法 使用局部模板来检测各类纹理，比如,8.3 纹理的结构分析,纹理基元大. 纹理的结构分析法分为三步：图像增强；基元提取；计算纹理基元的特征参数及构成纹理的结构参数,纹理基元特征参数及纹理基元参数包括基元的尺寸、偏心、矩量、位置和姿态等。,由等间距排列的圆点形成的纹理图 (a)原始纹理图 (b)图像受到噪音的污染导致的随机线条,(1) 纹理基元的提取 二值化方法,（2）Extracting Ima

8、ge Structure with Filter Banks There is a strong response when the image pattern in a neighbourhood looks similar to the filter kernel, and a weak response when it doesnt.,A set of eight filters used for expanding images into a series of responses. These filters are shown at a fixed scale, with zero

9、 represented by a mid-grey level, lighter values being positive and darker values being negative.They represent t o distinct spots, and six bars.,(a) Spots and Bars by Weighted Sums of Gaussians A spot: given by a weighted sum of three concentric, symmetric Gaussians( with weights 1, -2 and 1, and s

10、igmas 0.62, 1 and 1.6). Another spot: given by a weighted sum of two concentric, symmetric Gaussians, with weights 1 and -1, and corresponding sigmas 0.71 and 1.14. A series of oriented bars, consisting of a weighted sum of three oriented Gaussians, which are offset with respect to one another. Ther

11、e are six versions of these bars; each is a rotated version of a horizontal bar. The Gaussians in the horizontal bar have weights -1, 2, and -1.They have different sigmas in the x and in the y directions; the x values are all 2, and the y values are all 1. The centers are offset along the y axis, ly

12、ing at (0, 1), (0,0) and (0, -1).,Generally, spot filters are useful because they respond strongly to small regions that differ from their neighbours (for example, on either side of an edge, or at a spot). The other attraction is that they detect non-oriented structure. Bar filters, on the other han

13、d, are oriented,and tend to respond to oriented structure (this property is sometimes, rather loosely, described as analysing orientation or representing orientation ).,At the top, an image of a butterfly at a fine scale, and below, the result of applying each of the filters to that image.The result

14、s are shown as absolute values of the output, lighter pixels representing stronger responses, and the images are laid out corresponding to the filter position in the top row.,The input image of a butterfly and responses of the filters at a coarser scale,(b) Spots and Bars by Gabor Filters The kernel

15、s look like Fourier basis elements that are multiplied by Gaussians, meaning that a Gabor filter responds strongly at points in an mage where there are components that locally have a particular spatial frequency and orientation. Gabor filters come in pairs, often referred to as quadrature pairs; one of the pair recovers symmetric components in a particular direct on, and the other recovers ant symmetric components. The mathematical forms,These filters are s