April 2019
Intermediate to advanced
172 pages
4h 39m
Chinese
Content preview from 精通特征工程
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110
｜
第
8
章
8.2
人工特征提取
：
SIFT
和
HOG
1999
年，计算机视觉研究者找到了一种更好的图像表示方法，它使用的是小块图像的统计
量，这种方法称为
尺度不变特征转换
（
scale invariant feature transform
，
SIFT
）
[Lowe, 1999]
。
SIFT
最初是为对象识别任务而开发的。对象识别不仅要正确地标注出图像中包含某种对
象，还要确定对象在图像中的位置。对象识别过程包括在各种可能的尺度层次上分析图
像，检测能够表示对象存在的有趣的点，提取这些有趣的点的相关特征（在计算机视觉中
通常称为
图像描述符
），以及确定对象的姿态。
近年来，
SIFT
的使用范围扩大了，不仅仅用于我们感兴趣的点，还可以提取整个图像的
特征。
SIFT
特征提取过程与另一种技术非常相似，那就是梯度方向直方图（
histogram of
oriented gradients
，
HOG
）
[Dalal and Triggs, 2005]
。这两种技术本质上都是计算梯度方向上
的直方图，下面进行详细的介绍。
8.2.1
图像梯度
要想比原始像素值做得更好，必须以某种方式将像素“组织”成信息量更大的单位。相邻
像素之间的差别通常是非常有用的。像素值一般在对象的边界发生改变，比如一片阴影，
一个模式之中，或者一个纹理表面。相邻像素在值上的差异称为
图像梯度
。
计算图像梯度的最简单方法是先沿着图像的横轴（
x
）和纵轴（
y
）分别计算像素值的差异，
然后将计算结果组合到一个二维向量中。这需要两个一维差分操作，可以用向量掩膜或滤
波器方便地表示 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.