毕业设计题目来源

毕业设计题目来源

: The first and second features selected by AdaBoost. The two features are shown in the top row and then overlayed on a typical training face in the bottom row. The first feature measures the differencein intensity between the regionof the eyes and a region across the upper cheeks. The feature capitalizes on the observation that the eye region is often darker than the cheeks. The second feature compares the intensities in the eye regions to the intensity across the bridge of the nose. positive instances. Simpler classifiers are used to reject the majority of sub-windows before more complex classifiers are called upon to achieve low false positive rates.

Stages in the cascade are constructed by training classifiers using AdaBoost. Starting with a two-feature strong classifier, an effective face filter can be obtained by adjusting the strong classifier threshold to minimize false negatives. The initial AdaBoost threshold, is designedto yield a low errorrate on the training data. A lower threshold yields higher detection rates and higher false positive rates. Based on performance measured using a validation training set, the two-feature classifier can be adjusted to detect 100% of the faces with a false positive rate of 40%. See Figure 5 for a description of the two features used in this classifier.

The detection performanceof the two-feature classifier is far from acceptable as an object detection system. Nevertheless the classifier can significantly reduce the number of sub-windows that need further processing with very few operations:

1. Evaluate the rectangle features (requires between 6 and 9 array references per

feature).

2. Compute the weak classifier for each feature (requires one threshold operation

per feature).

3. Combine the weak classifiers (requires one multiply per feature, an addition, and

finally a threshold).

A two feature classifier amounts to about 60 microprocessor instructions. It seems

hard to imagine that any simpler filter could achieve higher rejection rates. By comparison, scanning a simple image template, or a single layer perceptron, would require at least 20 times as many operations per sub-window.

The overall form of the detection process is that of a degenerate decision tree, what we call a “cascade”[9] (see Figure 6). A positive result from the first classifier triggers the evaluation of a second classifier which has also been adjusted to achieve very high detection rates. A positive result from the second classifier triggers a third classifier, and so on. A negative outcome at any point leads to the immediate rejection of the subwindow.

：选定的 AdaBoost 第一和第二个特点。这两个功能是显示在顶行和底部行中的典型培训脸上然后重叠。第一个特征措施区的大不相同强度的眼睛和一个区域跨上部的双颊。该功能将大写观察眼区域往往是比双颊暗。第二个特点比较眼地区强度间的鼻梁。积极的实例。简单的分类器用来拒绝，绝大多数的子窗口之前，呼吁更加复杂的分类器，实现低的假阳性率。

级联反应阶段是通过使用 AdaBoost 训练分类器构成的。入手两功能强分类器，可以通过调整强分类器阈值，以尽量减少漏报获得有效的脸筛选器。初始的

AdaBoost 阈值，是为了收益率低的出错率的培训资料。较高的检测率和假阳性率较高，收益率较低的门槛。基于测量使用验证训练集的性能，可以调整两个特征分类器，以检测 100%的面孔与假阳性率为 40%。在此分类器中使用的两种功能的说明，请参见图 5。

检测性能的两个特征分类器是远从接受作为一种对象检测系统。然而，分类器可以显著减少需要进一步处理很少操作的子窗口的数：

1.评价矩形特征（需要 6 和 9 的数组引用，每个之间

功能）。

2.计算的弱分类器（需要一个阈值操作每个功能

每个功能）。

3.结合弱分类器（需要一个乘以每个功能，另外，和

最后阈值）。

两篇专题分类共计约 60 的微处理器指令。它似乎

很难想象任何简单的筛选器可以实现更高的抑制率。相比之下，扫描一个简单的图像模板或单层感知器，将需要多出至少 20 倍每子窗口的操作。

检测过程的总体形式是简并的决策树，我们称之为"级联"[9] （见图 6）。一个积极的结果，从第一个分类器触发第二个分类器也作了调整，以达到很高的检测率的评价。一个积极的结果，从第二个分类触发第三个分类器，等等。在任何时候一个消极的结果会导致子窗口中立即拒绝。