图像的局部特征尺度在进行特征提取和构造尺度不变量时非常重要。
Local characteristic scale of images plays an important role in feature extraction and local scale invariant construction.
针对遥感图像配准，基于尺度不变特征变换(SIFT)提出了一种在核空间中构建仿射不变描述子的方法。
A technique to construct an affine invariant descriptor for remote-sensing image registration based on the scale invariant features transform (SIFT) in a kernel space is proposed.
摘要 ：改进了传统的尺度不变特征变换（SIFT）算法，使其在进行图像匹配的同时，可以求取出物体的旋转角度。
Abstract : The Scale-invariant Feature Transform(SIFT) algorithm was improved in this paper, which could match two pictures and could also compute the object rotation angles in the pictures.
它最初作为一种关键点的特征提出来的，其主要思想是在尺度空间寻找极值点，提取位置，尺度，旋转不变量；
The main idea is to find extreme value in the scale space, extracting location, scale and rotation invariant.
它最初作为一种关键点的特征提出来的，其主要思想是在尺度空间寻找极值点，提取位置，尺度，旋转不变量；
The main idea is to find extreme value in the scale space, extracting location, scale and rotation invariant.
应用推荐