修整格式.

Signed-off-by: ithink.chan <chenyang@autoai.com>

Algorithm/AI/Deep_Learning_Haar_Cascade_Explained.md

@@ -49,7 +49,7 @@ Haar 级联分类器最著名的应用是检测图像中的人脸或身体，但
 
 本文以人脸检测为例。首先需要大量包含人脸的 positive image 和不包含人脸的 negative image 来训练分类器。然后我们从其中提取特征。
 
-“First step is to collect the Haar Features. A Haar​ feature considers adjacent rectangular regions at a specific location in a detection window, sums up the pixel intensities in each region and calculates the difference between these sums. ”
+“First step is to collect the Haar Features. A Haar​ feature considers adjacent rectangular regions at a specific location in a detection window, sums up the pixel intensities in each region and calculates the difference between these sums.”
 
 第一步是收集 Haar 特征。可以将 Haar​ 特征看作检测窗口中相邻的举行区域，将各自区域的像素强度加合，然后计算它们的差值。
 
@@ -59,7 +59,7 @@ Haar 级联分类器最著名的应用是检测图像中的人脸或身体，但
 
 可以使用积分图加快这一运算过程。
 
-“But among all these features we calculated, most of them are irrelevant. For example, consider the image below. Top row shows two good features. The first feature selected seems to focus on the property that the region of the eyes is often darker than the region of the nose and cheeks. The second feature selected relies on the property that the eyes are darker than the bridge of the nose.  But the same windows applying on cheeks or any other place is irrelevant. ”
+“But among all these features we calculated, most of them are irrelevant. For example, consider the image below. Top row shows two good features. The first feature selected seems to focus on the property that the region of the eyes is often darker than the region of the nose and cheeks. The second feature selected relies on the property that the eyes are darker than the bridge of the nose. But the same windows applying on cheeks or any other place is irrelevant.”
 
 但是，这些特征中的很大一部分是与任务目标不相关的（没有意义的特征）。以下图为例，应用第一行的选择窗口可以得到两个好的特征：选取第一个特征，主要是由于眼部区域通常比鼻子和脸颊更黑；选取第二个特征则依赖于眼部比鼻梁更黑这一现象。但是，将同样的检测窗口应用于脸颊或其他部位则得不到有意义的特征。