[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f-DKuz-9SJWPSmg9sLKdaJchhEvEb1zDG_NOorkstcfc":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":10,"question":16,"related":17,"source":27,"type":34},[],"2024-01-19 13:28:46",982298463,[8,9],"对","错",{"courseId":11,"courseImg":12,"courseName":13,"workId":14,"workName":15},"1000008418","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F8e391dadee7a948b8ae0df014f53e38f.jpg","人工智能原理与方法","56089733","第六章单元测试","Prewitte算子是一种一阶微分算子的边缘检测,其检测水平与竖直方向的模板与Robert算子模板的相似,都是3*3的矩阵",[18,29,35,45,54,57,66,75,80,89],{"answer":19,"createTime":5,"id":20,"options":21,"question":26,"source":27,"type":28},[],982296220,[22,23,24,25],"像平面坐标系","图像坐标系","世界坐标系","摄像机坐标系","摄像机标定利用机器视觉进行物体测量时必须进行的一个关键性步骤,其标定精度会直接影响到测量精度.其中,摄像机的标定一般会涉及到物点坐标在几个坐标系中的相互转换.那么,此处所说的"几个坐标系"指的是哪几个坐标系?( )","v2",1,{"answer":30,"createTime":5,"id":31,"options":32,"question":33,"source":27,"type":34},[],982296567,[8,9],"摄像机标定的目的是得到摄像机的内外参数,其中内参表示的是旋转矩阵,外参表示的是平移矩阵",3,{"answer":36,"createTime":5,"id":37,"options":38,"question":43,"source":27,"type":44},[],982296596,[39,40,41,42],"常用的摄像机标定方法有传统摄像机标定方法、摄像机自标定法、张正友标定法","张正友标定法介于传统摄像机标定法和自标定法之间,具有高鲁棒性","传统摄像机标定法的标定精度高,但标定过程复杂,且需要高精度的已知结构信息","摄像机自标定法标定灵活,具有高鲁棒性,属于线性标定","下列关于摄像机标定的常用方法的说法中,错误的是( )",0,{"answer":46,"createTime":5,"id":47,"options":48,"question":53,"source":27,"type":44},[],982298381,[49,50,51,52],"中值滤波、均值滤波、高斯滤波和双边滤波器都会在一定程度上使原图变得模糊","双边滤波器和高斯滤波器是完全不同的两个滤波器,没有一点联系","若高斯滤波器的高斯核(n*n)的所有参数都为\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Fa02baa57d4436bce340f984d31173fa3.webp\">,其作用等同于中值滤波器","均值滤波器可以降低图像中的"尖锐"变化,适用于对服从正态分布的噪声进行抑制","下列关于常用滤波器的说法中正确的是( )",{"answer":55,"createTime":5,"id":6,"options":56,"question":16,"source":27,"type":34},[],[8,9],{"answer":58,"createTime":5,"id":59,"options":60,"question":65,"source":27,"type":44},[],982298491,[61,62,63,64],"分水岭算法、MeanShift分割、区域生长和Ostu阈值分割都可以完成对图像的分割","分水岭算法常用来对图像中连在一起目标物体进行分割","区域生长法是通过计算偏移的均值向量来完成分割的","Otsu阈值分割又名最大类间差方法,通过统计整个图像的直方图特性来实现全局阈值T的自动选取","图像分割就是把图像分成若干个特定的、具有独特性质的区域并提出感兴趣目标的技术和过程.下面关于图像分割算法的说法中,错误的是( )",{"answer":67,"createTime":5,"id":68,"options":69,"question":74,"source":27,"type":28},[],982298518,[70,71,72,73],"Sobel算子、Robert算子都属于基于图像灰度的一阶导数边缘检测算子","\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F82ccab9b2043dce430cdb50536c5eb5d.PNG\">","Robert算子定位精度高,但对噪声敏感","Sobel算子检测垂直边缘的效果好于斜向边缘","Sobel算子和Robert算子都是常见的边缘检测算子.下列关于这两个边缘检测算子的说法中正确的有( )",{"answer":76,"createTime":5,"id":77,"options":78,"question":79,"source":27,"type":34},[],982298542,[8,9],"均值滤波器对图像进行滤波会模糊图像的边缘,双边滤波器则可以保护图像边界",{"answer":81,"createTime":5,"id":82,"options":83,"question":88,"source":27,"type":28},[],982298577,[84,85,86,87],"Harris利用一个滑动小窗在图像上进行滑动来检测角点","若滑动小窗在任意方向上平移都会引起窗口内的灰度值剧烈变化,则此时滑动小窗可能在角点上","小窗在任意方向上移动,窗内的灰度值变化都不大,则此处可能不存在角点","小窗在一些方向上移动时,窗内灰度值剧烈变化,在其余方向移动时,灰度值变化不大,则该处可能是边缘","Harris角点检测是一种常用的角点检测方法.下列关于Harris角点检测的说法中正确的有( )",{"answer":90,"createTime":5,"id":91,"options":92,"question":97,"source":27,"type":44},[],982299048,[93,94,95,96],"常用到的图像滤波方法有中值滤波、双边滤波、均值滤波、高斯滤波等","对图像的滤波,即在尽量保留图像细节特征的条件下对目标图像的噪声进行抑制","中值滤波器和均值滤波器的滤波核一般都是n*n的大小,不同的是,均值滤波器取相邻内像素灰度的均值作为中心像素的灰度值,而中值滤波器则用的是领域内像素灰度的中值","如果图像中存在大量的椒盐噪声,则可以使用均值滤波器进行降噪","图像滤波是图像预处理中不可或缺的操作,对后续图像的处理和分析有着直接的影响.下列关于图像滤波的说法中,错误的是( )"]