我已经阅读了很多关于Viola Jones方法但我仍然不了解“弱分类器”，“强分类器”，“矩形”中的“子窗口”，它们的定义是什么。那么“门槛”怎么样？我怎么知道门槛值？ 谁能帮我？
之前谢谢答案 0 :(得分：0)
Aim of Viola-Jones algorithm: Detection of faces in an image. This algorithm
uses frontal upright faces, thus in order to be detected, the entire face must
point towards the camera and should not be tilted to either side. Algorithm is
face image partition based on physical estimation of position of eyes, nose
and mouth on face. Stages of the algorithm: This algorithm works in following four stages: 1. Haar features 2. Integral image 3. AdaBoost 4. Cascading All these stages are discussed below. Before that i will answer a simple
question that **why haar** ? Haar wavelets are preferred because it is better than fourier for feature
extraction. Now, we will discuss about the stages involved in this algorithm. Haar features: Over the given input image, a 24 x 24 base window will
slide while passing haar as an argument and computation will take place using
convolution theorem.What are different haar features, you can study about
them here The output of this phase will be the detection of light and dark parts
of an image. Integral Image:The haar features extracted in above phase will be very
large which will make computation very complex. To make this computation
simple and short, these extracted haar features are passed to integral image.
It calculates the pixel values using simple mathematics. You can learn this
calculation in the link provided above. AdaBoost: As there will be so many features, all of them will not include
face in it. From, integral image we will get two possible things: features
containing face and features containing no face. We need only those features
which contains face. This job will be done by Adaboost. It will help to sample
face from rest of the body parts using weak classifiers and cascade. The
overall process used is ensemble method. A weighted arrangement of all these
features are used in evaluating and deciding any given window has face or not.
It will eliminate all redundant features Cascading: Weak classifiers will be cascaded to make a one strong single
classifier while window sliding over the whole image.This process is also
known as boosting up the weak classifiers. A sub-window classified as a face
is passed on to the next stage in the cascade.It follows that the additional
stages a given sub-window passes, the higher chances that the sub-window
really contains a face. Next what: This model will be tested on real images and faces will be
detected. Use-case of Viola-Jones: This model can be run on CPU, hence can be
experimented for learning purpose. With Regards, Ekta Smothra