第七章特征提取

第七章特征提取7.1图像识别中常用的特征纹理,影调,直方图,边缘形状,目标所占的区域的面积的大小、区域的长轴、区域的短轴、空间位置（质心）等7.2纹理特征的提取(1)纹理特征的两要素:(A)形状与大小,(B)基元排列的稀密,周期性,方向性(2)纹理分析的方法:统计方法和结构方法7.2.1区域直方图方法:比较已知目标的直方图与图像中某个给定区域的灰度直方图,从而决定两目标是否相似.操作步骤:选取适当的区域大小对每一个像素,计算其领域的灰度直方图比较此直方图与基元的直方图,决定两者是否相似,若相似,则说明所给的图像中有我们所需的纹理,进行第4步.比较不同像素的直方图的相似性,决定纹理的稀密,周期性,方向性.衡量直方图相似的常见度量直方图的均直(2)直方图的方差(2)Kolmogorov_Smirnov

距离定义:问题:如下两个图有相同的直方图7.2.2图像的自相关(1)互相关(2)自相关特点:若纹理较粗,d=|x||+|y|增大时变化较慢.若上式的值呈周变化,同可获得纹理变化的情况.2.边缘方向直方图定义8个方向矢量

取最大值者对应此区域的方向,按此方式计算上图所示的9个区域的方向,统计方向数,方向数最多的方向就是此4X4区域的方向.7.2.3傅里叶特征作纹理测试函数:若作纹理测试函数为:7.3Thefeatureofform7.3.1ExtractionofcharacteristicsoftheobjectregionInpracticality,itisdefinedbythenumberofregionpixelsD)HU矩7.4其它常见的区域特征(1).边缘周长l定义为边缘上的像素个数(2)区域圆度其中S为区域面积(3)欧拉数一个区域中连通部数C分与吼数C的差E=C-H在一幅二值图像中,记:(5)链码特点:对于边缘,我们只存初始点的坐标,然后对每一个边缘点的边缘链码用3位二进制数编码.这样杰节约存储空间和模式的向量代表示.(5)颜色特征例子:运动员运动姿势分析BW1=imread('circles.tif');imshow(BW1)BW2=bwmorph(BW1,'remove');BW3=bwmorph(BW1,'skel',Inf);figure;imshow(BW2);figure;imshow(BW3);figure;imshow(I);title('original

image');DI=imadjust(I,[],[01]);figure,imshow(DI),title('scaled

image');BWs=edge(DI,'sobel',(graythresh(DI)*.1));figure,imshow(BWs),title('binarygradientmask');se90=strel('line',5,90);se0=strel('line',5,0);BWsdil=imdilate(BWs,[se90se0]);figure,imshow(BWsdil),title('dilatedgradientmask');BWdfill=imfill(BWsdil,'holes');figure,imshow(BWdfill);title('binaryimagewithfilledholes');BWnobord=imclearborder(BWdfill,6);figure,imshow(BWnobord),title('clearedborderimage');seD=strel('diamond',1);BWfinal=imerode(BWnobord,seD);BWfinal=imerode(BWfinal,seD);figure,imshow(BWfinal),title('segmented

image');BWoutline=bwperim(BWfinal);Segout=imadd(I,immultiply(BWoutline,255));figure,imshow(Segout),title('outlinedoriginalimage');LocalFeatureBasedObjectRecognitionGivenadatabaseofobjectmodels,andaninputimage,addressestwoproblems:Identification:Whichmodelsinthedatabasematchthedataintheimage?Location:Giventhatanobjectintheimagematchesagivenmodel,whatisthelocationinspaceofthe3Dobjectimage?Whatobjectsarewelookingat?ModelsearchandimageregionsearchIsthispartoftheimageaninstanceofX?Givenmodel,givenimageregionWhatisthispartoftheimage?Modelsearch,givenimageregionArethereanyinstancesofXintheimage?Givenmodel,imageregionsearchAccordingtothefeatureusedtorepresenttheobject,canbecategorizedintotwoclasses:Globalfeaturebased;Localfeaturebased;Characterizetheobjectbyitswholeimage.Examples:Handwrittendigitrecognition;Facedetection;Rockrecognition;Shapebasedrecognition.Pros:Fastcomputation.Cons:Notrobusttoocclusion,lackofinvariance;Onlyapplicabletorigidobjects;Requirepreliminarysegmentation;High-dimensional,manylearningtechniquescannotbeused.Interpretationtrees.Given:Thelistoffeaturedescriptorsfromagivenobjectmodel;Thelistoffeaturedescriptorsdetectedinanimage;Alistofgeometricconstraintsthat