补充翻译内容.

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

Algorithm/AI/Deep_Learning_Haar_Cascade_Explained.md

@@ -53,7 +53,7 @@ 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
 
 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。
 
-![Integral Images](/img/Deep_Learning_Haar_Cascade_Explained/001.jpg)
+![Integral Images](./img/Deep_Learning_Haar_Cascade_Explained/001.jpg)
 
 “Integral Images are used to make this super fast.”
 
@@ -63,7 +63,7 @@ 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
 
 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。
 
-![Integral Images](/img/Deep_Learning_Haar_Cascade_Explained/002.png)
+![Integral Images](./img/Deep_Learning_Haar_Cascade_Explained/002.png)
 
 “So how do we select the best features out of 160000+ features? This is accomplished using a concept called Adaboost which both selects the best features and trains the classifiers that use them. This algorithm constructs a “strong” classifier as a linear combination of weighted simple “weak” classifiers. The process is as follows.”
 
@@ -71,6 +71,12 @@ 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
 
 “During the detection phase, a window of the target size is moved over the input image, and for each subsection of the image and Haar features are calculated. You can see this in action in the video below. This difference is then compared to a learned threshold that separates non-objects from objects. Because each Haar feature is only a "weak classifier" (its detection quality is slightly better than random guessing) a large number of Haar features are necessary to describe an object with sufficient accuracy and are therefore organized into cascade classifiers to form a strong classifier.”
 
+在检测阶段,一个特定的窗口在输入图像上移动，并在每个位置上计算 Haar 特征。下面的视频演示了这一过程。计算结果将与阈值进行比较以区分是否为检测目标，这个阈值是通过学习得到的。由于每个 Haar 特征只是一个“弱分类器”（弱分类器的检测质量仅比随即猜测好一点儿），我们需要大量的 Haar 特征才能够对目标进行精确的检测，因此这些弱分类器被级联成了强分类器。
+
+![Integral Images](./img/Deep_Learning_Haar_Cascade_Explained/003.png)
+
+“The cascade classifier consists of a collection of stages, where each stage is an ensemble of weak learners. The weak learners are simple classifiers called decision stumps. Each stage is trained using a technique called boosting. Boosting provides the ability to train a highly accurate classifier by taking a weighted average of the decisions made by the weak learners.”
+
 ## 标注
 
 [1] Haar