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Rapid Object Detection With A Cascade of Boosted Classifiers Based on Haar-like Features-Rapid Object Detection With A Cascade of Bo osted Classifiers Based on Haar - like Features
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如何利用opencv训练自己的分类器，内有多篇资料，本人用过一次，可能样本太少，效果不太好-How-to build a cascade of boosted classifiers based on Haar-like features.
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How-to build a cascade of boosted classifiers based on
Haar-like features
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这片论文描述了动态物体的特征跟踪，用到了15个框架。拥有很强的适应性和跟踪能力。作为人脸识别，模式识别，动态跟踪的开发人员，有很好的参考价值。用c++编写，如果用OpenCV更好-This paper describes a visual object detection framework that is capable of processing
images extremely rapidly while achieving high detection rates. There ar
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利用针对某目标物体训练级联的Harry分类器寻找在图像中找到包含目标物体的矩形区域-Training against a target object using a cascade of classifiers for Harry to find in the image of the rectangle that contains the object
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最经典AdaBoost实现，适合初学，有大量详细的注释，容易理解-This a classic AdaBoost implementation, in one single file with easy understandable code.
The function consist of two parts a simple weak classifier and a boosting part:
The weak classifier tries to find the b
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How-to build a cascade of boosted classifiers based on Haar-like features
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Face detection algorithms are widely used in computer vision as they provide fast and reliable results depending on the application
domain. A multi view approach is here presented to detect frontal and profile pose of people face using Histogram of
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这是一个经典的形变模型实施,在一个单一的文件用简单的可以理解的代码。
功能包括两部分一个简单的弱分类器和一个促进部分:
弱分类器试图找到最佳阈值的数据维数对数据进行分离成两个阶级1和1
要求的进一步提高分类器部分迭代,每一步是变化分类权重miss-classified例子。这造成了一连串的“弱分类器”,表现得像一个“强大分类器”
-This a classic AdaBoost implementation, in one single file with easy unders
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[PR 2009 ]LLU_Face detection using simplified Gabor features and hierarchical regions in a cascade of classifiers.pdf
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How-to build a cascade of boosted classifiers based on Haar-like features
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this codes is Source code for face detection of viola paper.of its Features is:
Feature Computation: The “Integral” image representation
Feature Selection: The AdaBoost training algorithm .
Real-timeliness: A cascade of classifiers.-this code
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这段代码实现了一个新的MLP神经网络训练方法,来自论文A new method for neural network regularization（内附）-This code implements a new training method for MLP neural networks, named Support Vector Neural Network (SVNN), proposed in the work: O. Ludwig “Study on Non-parametric Me
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人脸检测：
第一部分，使用Harr-like特征表示人脸，使用“ 积分图”实现特征数值的快速计算;
第二部分， 使用Adaboost算法挑选出一些最能代表人脸的矩形特征( 弱分类器)，按照加权投票的方式将弱分类器构造为一个强分类器;
第三部分， 将训练得到的若干强分类器串联组成一个级联结构的层叠分类器，级联结构能有效地提高分类器的检测速度。(Face detection:
In the first part, the Harr-like feature is used t
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