Description: 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 Platform: |
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Author:黄笑 |
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Description: 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 Platform: |
Size: 257024 |
Author:黄笑 |
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Description: 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 are three
key contributions. The first is the introduction of a new image representation called the
“Integral Image” which allows the features used by our detector to be computed very
quickly. The second is a learning algorithm, based on AdaBoost, which selects a small
number of critical visual features and yields extremely efficient classifiers [4]. The
third contribution is a method for combining classifiers in a “cascade” which allows
background regions of the image to be quickly discarded while spending more computation
on promising object-like regions. A set of experiments in the domain of face
detection are presented. The system yields face detection performance comparable to
the best previous systems [16, 11, 14, 10, 1]. Implemented on a conventional desktop,
face detection proceeds at 15 frames per second Platform: |
Size: 784384 |
Author:lai |
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Description: 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 Platform: |
Size: 179200 |
Author:孙波 |
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Description: 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 best threshold in one of the data dimensions to separate the data into two classes-1 and 1
The boosting part calls the classifier iteratively, after every classification step it changes the weights of miss-classified examples. This creates a cascade of "weak classifiers" which behaves like a "strong classifier" 。 Platform: |
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Author:wj |
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Description: 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 Oriented Gradients, i.e. HOG, features. A K-mean clustering technique is used in a cascade of HOG feature classifiers to detect faces. The evaluation of the algorithm shows similar performance in terms of detection rate as state of the art algorithms. Moreover, unlike state of the art algorithms,our system can be quickly trained before detection is possible. Performance is considerably increased in terms of lower computational cost and lower false detection rate when combined with motion constraint given by moving objects in video sequences. The detected HOG features are integrated within a tracking framework and allow reliable face tracking results in several tested surveillance video sequences. Platform: |
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Author:linuszhao |
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Description: 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. Platform: |
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Author: |
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Description: 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 understandable code.
The function consist of two parts a simple weak classifier and a boosting part:
The weak classifier tries to find the best threshold in one of the data dimensions to separate the data into two classes-1 and 1
The boosting part calls the classifier iteratively, after every classification step it changes the weights of miss-classified examples. This creates a cascade of "weak classifiers" which behaves like a "strong classifier"
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Platform: |
Size: 4096 |
Author:文泽枫 |
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Description: [PR 2009 ]LLU_Face detection using simplified Gabor features and hierarchical regions in a cascade of classifiers.pdf Platform: |
Size: 1048576 |
Author:Felix |
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Description: 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 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.
Platform: |
Size: 420864 |
Author:fatemeh |
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Description: 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 Methods for Fast Pattern Recognition with Emphasis on Neural Networks and Cascade Classifiers ” PhD Thesis, University of Coimbra, Coimbra, 2012. The input arguments are a N x L matrix of L representative N-element input vectors, a row vector, y, whose elements are the respective target classes, which should be-1 or 1, and the number of hidden neurons, nneu. Similarly to SVMs, the SVNN has a punishing parameter, C, which can be set in the line 16 of the code. The algorithm outputs the MLP parameters, W1, W2, b1, b2, which are input arguments of the MLP simulator “sim_NN.m” that also requires the matrix of testing data, as well as the target vector (in case of target unavailable, a empty vector must be supplied). “sim_NN.m” outputs the estimated class and the accuracy, acc (when testing targets are available). The code Platform: |
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Author:孙园 |
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Description: 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 to represent the human face, and the "integral graph" is used to realize the fast calculation of feature values;
In the second part, the Adaboost algorithm is used to select some rectangular features (weak classifiers) that represent the human face, and the weak classifier is constructed into a strong classifier according to weighted voting;
In the third part, a series of strong classifiers are formed in series to form a cascade classifier with cascading structure, and the cascade structure can effectively improve the detection speed of classifier.) Platform: |
Size: 2048 |
Author:14024235
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Search - a Cascade of Classifiers