补充翻译内容;

增加 译注.

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

Algorithm/AI/Deep_Learning_Haar_Cascade_Explained.md

@@ -36,9 +36,9 @@ Haar 级联分类器是一种机器学习算法，最初由 Paul Viola 和 Micha
 
 Haar 级联分类器主要有四个步骤：
 
-1. Haar 特征选择
-2. 创建积分图
-3. Adaboost 训练
+1. Haar[1] 特征选择
+2. 创建积分图[2]
+3. Adaboost[3] 训练
 4. 级联各个分类器
 
 “It is well known for being able to detect faces and body parts in an image, but can be trained to identify almost any object.”
@@ -73,11 +73,13 @@ 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
 
 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。
 
+## Cascade Classifier​
+
 ![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.”
+“The cascade classifier[a] 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.”
 
-级联分类器由一系列小的阶段组成，每个阶段就是一个弱学习过程。弱学习过程是一个被称作单层分类器的简单分类器。每个阶段由 boosting 算法进行训练。通过对每个阶段的决策进行加权，Boosting 提供了训练高准确度分类器的方法。
+级联分类器[a]由一系列小的阶段组成，每个阶段就是一个弱学习过程。弱学习过程是一个被称作单层分类器的简单分类器。每个阶段由 boosting 算法进行训练。通过对每个阶段的决策进行加权，Boosting 提供了训练高准确度分类器的方法。
 
 “Each stage of the classifier labels the region defined by the current location of the sliding window as either positive or negative. Positive indicates that an object was found and negative indicates no objects were found. If the label is negative, the classification of this region is complete, and the detector slides the window to the next location. If the label is positive, the classifier passes the region to the next stage. The detector reports an object found at the current window location when the final stage classifies the region as positive.”
 
@@ -85,7 +87,7 @@ 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
 
 “The stages are designed to reject negative samples as fast as possible. The assumption is that the vast majority of windows do not contain the object of interest. Conversely, true positives are rare and worth taking the time to verify.”
 
-## 标注
+## 原文标注
 
 [1] Haar
 
@@ -97,14 +99,28 @@ 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
 
 “An integral image is summed-area table is a data structure and algorithm for quickly and efficiently generating the sum of values in a rectangular subset of a grid. To understand this look at image 1 and image 2. Image 1 is the source table, Image 2 is the summation table. Notice in Image 2 row 1, col 2 value 33 is sum of Image 1 row 1 (col 1 + col 2).“
 
-![Integral Images](./img/Deep_Learning_Haar_Cascade_Explained/004.png)
+![Integral Images](./img/Deep_Learning_Haar_Cascade_Explained/006.png)
 
 [3] Adaboost
 
 “Problems in machine learning often suffer from the curse of dimensionality — each sample may consist of a huge number of potential features (for instance, there can be 162,336 Haar features, as used by the Viola–Jones object detection framework, in a 24×24 pixel image window), and evaluating every feature can reduce not only the speed of classifier training and execution, but in fact reduce predictive power, per the Hughes Effect.[3] Unlike neural networks and SVMs, the AdaBoost training process selects only those features known to improve the predictive power of the model, reducing dimensionality and potentially improving execution time as irrelevant features need not be computed.“
 
+## 译注
+
+[4] Cascade Classifier
+
+级联分类器是将一些小的分类过程组合成完整分类器的方法。以下图为例，需要将图中的“+”和“-”数据分开，仅靠一个分类过程很难实现。
+
+![Integral Images](./img/Deep_Learning_Haar_Cascade_Explained/007.png)
+
+但将下图中的三种分类方法组合到一起就可以得到一个非常好的分类器。其中每个分类方法都是级联分类器中的一个弱分类器，它们组合成了一个强分类器。注意，每个弱分类器都只对部分数据进行了分类。
+
+![Integral Images](./img/Deep_Learning_Haar_Cascade_Explained/008.png)
+
 ## 参考资料
 
+以下是译者本人提供的一些额外参考资料：
+
 [人脸检测之Haar分类器](https://www.cnblogs.com/zyly/p/9410563.html)  
 [What’s the Difference Between Haar-Feature Classifiers and Convolutional Neural Networks?](https://towardsdatascience.com/whats-the-difference-between-haar-feature-classifiers-and-convolutional-neural-networks-ce6828343aeb)  
 [Computer Vision — Detecting objects using Haar Cascade Classifier](https://towardsdatascience.com/computer-vision-detecting-objects-using-haar-cascade-classifier-4585472829a9)