Haar-like特征双阈值Adaboost人脸检测

目的 针对基于Haar-like特征的Adaboost人脸检测算法,在应用于视频流时训练的时间较长,以及检测效率较低的问题,提出了一种基于区间阈值的Adaboost人脸检测算法。方法 通过运行传统的Adaboost算法对人脸图像Haar-like特征值进行提取分析后,对人脸样本与非人脸样本特征值进行比较,发现在某一特定的特征值区间内,人脸和非人脸区域能够得到准确区分,根据此特性,进行分类器的选择,在简化弱分类器计算步骤的同时,降低训练时间,提高对人脸的识别能力。除此之外,弱分类器的增强通过Adaboost算法的放大使得强分类器分类精度提高,与级联结构的配合使用也提升了最终模型检测人脸的准确率。结果 利用MIT（Massachusetts Institute of Technology）标准人脸库对改进Adaboost算法的性能进行验证,通过实验验证结果可知,改进后的Adaboost人脸检测算法训练速度提升为原来的1.44倍,检测率上升到94.93%,虚警率下降到6.03%。并且将改进算法在ORL（Olivetti Research Laboratory）、FERET（face recognition technology）以及CMU Multi-PIE（the CMU Multi-PIE face database）这3种标准人脸库中,分别与SVM（support vector machine）、DL（deep learning）、CNN（convolutional neural networks）以及肤色模型等4种算法进行了人脸检测对比实验,实验结果显示,改进后的Adaboost算法在进行人脸检测时,检测率提升了2.66%,训练所需时间减少至624.45 s,检测效果明显提升。结论 提出的基于区间阈值的Adaboost人脸检测算法,在分类器的训练和人脸检测方面都比传统的Adaboost算法性能更高,能够更好地满足人员较密集处（如球场等地）对多人脸同时检测的实际需求。     

基于Adaboost算法的人眼状态检测

人眼检测在表情识别和人脸识别中起着非常重要的作用,作为一种预处理的手段,人眼检测和定位可以有效地提高表情识别和人脸识别的识别率.提出了一种基于Adaboost算法的实时人眼状态检测的方法.Adaboost是一个构造准确分类器的学习方法.它将一簇弱分类器通过一定的规则结合成为一个强分类器,再把这些强分类器级联成为一个快速、准确的分类器.分析和讨论训练阶段不同的人眼特征选择对最终检测的影响,并实验测试各种特征方法对特定目标的检测率,给出一个理想的分类器.     

基于Adaboost和CART结合的优化分类算法

提出了一种基于Adaboost算法和CART算法结合的分类算法。以特征为节点生成CART二叉树,用CART二叉树代替传统Adaboost算法中的弱分类器,再由这些弱分类器生成强分类器。将强分类器对数字样本和人脸样本分类,与传统Adaboost算法相比,该方法的错误率分别减少20%和86.5%。将分类器应用于目标检测上,实现了对这两种目标的快速检测和定位。结果表明,改进算法既减小了对样本分类的错误率,又保持了传统Adboost算法对目标检测的快速性。     

动态权值预划分实值Adaboost人脸检测算法

提出了Real-Adaboost的一种改进算法。该算法采用预先计算类Haar特征所对应弱分类器在样本空间的划分，并动态更新人脸训练样本的权值。与以往的Real-Adaboost算法比较，该算法大大缩短了训练时间，算法训练时间复杂度降到O(T*M*N)，同时加速了强分类器的收敛性能，减少检测器的弱分类器数量，减少检测时间。     

基于Adaboost算法的人脸检测实现

人脸检测是指把人脸从一幅静止的图像或者动态视频中检测出来,并且指出人脸在图像或视频中的大小和位置.目前存在着大量的人脸检测算法,其中Adaboost算法是比较实用的人脸检测算法.Adaboost算法中人脸的特征采用的是矩形特征,在大量的样本集中,提取样本的矩形特征进行训练,生成多个弱分类器,然后合并多个弱分类器形成一个...     

多特征融合的道路车辆检测方法

通过改进基于Haar-like特征和Adaboost的级联分类器,提出一种融合Haar-like特征和HOG特征的道路车辆检测方法。在传统级联分类器的Harr-like特征基础上引入HOG特征;为Haar-like特征和HOG特征分别设计不同形式的弱分类器,对每一个特征进行弱分类器的训练,用Gentle Adaboost算法代替Discrete Adaboost算法进行强分类器的训练;在级联分类器的最后几层上使用Adaboost算法挑选出来的特征组成特征向量训练SVM分类器。实验结果表明所提出的方法能有效检测道路车辆。     

基于多尺度特征和嵌套级联Adaboost的车辆检测

为提高车辆检测的速度和准确性,提出了一种基于多尺度方向梯度直方图(HOG)和多尺度多块局部二进制模式(MB-LBP)两种特征与嵌套级联Gentle Adaboost的车辆检测算法.分别使用积分直方图和积分图像加速提取多尺度HOG和多尺度MB-LBP特征.基于两种特征为Gentle Adaboost构建两种弱分类器,并采用嵌套级联Gentle Adaboost分类器提高检测率和检测速度.仿真实验结果表明:相比于现有的几种车辆检测算法,提出的算法检测速度更快,且检测精度和召回率更高.     

Dynamic Adaboost learning with feature selection based on parallel genetic algorithm for image annotation

Image annotation can be formulated as a classification problem. Recently, Adaboost learning with feature selection has been used for creating an accurate ensemble classifier. We propose dynamic Adaboost learning with feature selection based on parallel genetic algorithm for image annotation in MPEG-7 standard. In each iteration of Adaboost learning, genetic algorithm (GA) is used to dynamically generate and optimize a set of feature subsets on which the weak classifiers are constructed, so that an ensemble member is selected. We investigate two methods of GA feature selection: a binary-coded chromosome GA feature selection method used to perform optimal feature subset selection, and a bi-coded chromosome GA feature selection method used to perform optimal-weighted feature subset selection, i.e. simultaneously perform optimal feature subset selection and corresponding optimal weight subset selection. To improve the computational efficiency of our approach, master-slave GA, a parallel program of GA, is implemented. k-nearest neighbor classifier is used as the base classifier. The experiments are performed over 2000 classified Corel images to validate the performance of the approaches.     

Spectral entropy feature subset selection using SEPCOR to detect alcoholic impact on gamma sub band visual event related potentials of multichannel electroencephalograms (EEG)

The problem of analyzing and identifying regions of high discrimination between alcoholics and controls in a multichannel electroencephalogram (EEG) signal is modeled as a feature subset selection technique that can improve the recognition rate between both groups. Several studies have reported efficient detection of alcoholics by feature extraction and selection in gamma band visual event related potentials (ERP) of a multichannel EEG signal. However, in these studies the correlation between features and their class information is not considered for feature selection. This may lead to redundancy in the feature set and result in over fitting. Therefore in this study, a statistical feature selection technique based on Separability & Correlation analysis (SEPCOR) is proposed to select an optimal feature subset automatically that possesses minimum correlation between selected channels and maximum class separation. The optimal feature selection consists of a ranking method that assigns ranks to channels based on a variability measure (V-measure). From the ranked feature set of highly discriminative features, different subsets are automatically selected by heuristically applying a correlation threshold in steps from 0.02 to 0.1. These subsets are applied as input features to multilayer perceptron (MLP) neural network and k-nearest neighbor (k-NN) classifiers to discriminate alcoholic and control visual ERP. Prior to feature selection, spectral entropy features are computed in gamma sub band (30–55 Hz) interval of a 61-channel multi-trial EEG signal with multiple object recognition tasks. Independent Component Analysis (ICA) is performed on raw EEG data to remove eye blink, motion and muscle artifacts. Results indicate that both classifiers exhibit excellent classification accuracy of 99.6%, for a feature subset of 22 optimal channels with correlation threshold of 0.1. In terms of computation time, k-NN classifier outperforms multilayer perceptron-back propagation (MLP-BP) network with 7.93 s whereas MLP network takes 55 s to perform the recognition task with the same accuracy. Compared to feature section methods used in previous studies on the same EEG alcoholic database, there is a significant improvement in classification accuracy based on the proposed SEPCOR method.     

基于Gabor变换和Adaboost算法的人体目标检测分类器

针对在图像中检测人体目标,提出一种基于Gabor变换和Adaboost算法的检测方法.首先利用二维Gabor小波变换进行特征提取,然后利用Adaboost算法对Gabor特征进行选取并训练强分类器.为了提高检测精度,提出采用单一正样本集合与多个负样本集合分别进行训练,形成多个强分类器级联的层级检测分类器.实验结果表明了该方法的有效性,同时显示该方法须与其它辅助手段相结合,才能提高检测的实时性.     

Adaboost算法及其在实时视频流中的应用

针对Adaboost算法在实时视频流中的应用,本文基于Adaboost算法的人脸检测原理,即通过提取图像中的haar特征,在训练过程中选出最优特征,转换成弱分类器,优化组合于人脸检测。最终,利用opencv的开发包,通过VC++软件编程实现基于Adaboost算法实时视频流中的人脸检测。     

Adaboost算法及其在实时视频流中的应用

针对Adaboost算法在实时视频流中的应用,本文基于Adaboost算法的人脸检测原理,即通过提取图像中的haar特征,在训练过程中选出最优特征,转换成弱分类器,优化组合于人脸检测.最终,利用opencv的开发包,通过VC++软件编程实现基于Adaboost算法实时视频流中的人脸检测.     

A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients

In this study, we propose a set of new algorithms to enhance the effectiveness of classification for 5-year survivability of breast cancer patients from a massive data set with imbalanced property. The proposed classifier algorithms are a combination of synthetic minority oversampling technique (SMOTE) and particle swarm optimization (PSO), while integrating some well known classifiers, such as logistic regression, C5 decision tree (C5) model, and 1-nearest neighbor search. To justify the effectiveness for this new set of classifiers, the g-mean and accuracy indices are used as performance indexes; moreover, the proposed classifiers are compared with previous literatures. Experimental results show that the hybrid algorithm of SMOTE + PSO + C5 is the best one for 5-year survivability of breast cancer patient classification among all algorithm combinations. We conclude that, implementing SMOTE in appropriate searching algorithms such as PSO and classifiers such as C5 can significantly improve the effectiveness of classification for massive imbalanced data sets.     

基于Adaboost算法的行人检测

提出一种基于Adaboost算法的行人检测方法。Adaboost是将一组弱分类器通过一定的规则,结合成为一个强分类器,再把这些强分类器级联成为一个快速、准确的分类器。实验证明基于此算法的行人检测具有检测率高、速度快的特点,能够达到实时检测的要求。     

Genetic programming for epileptic pattern recognition in electroencephalographic signals

This paper reports how the genetic programming paradigm, in conjunction with pattern recognition principles, can be used to evolve classifiers capable of recognizing epileptic patterns in human electroencephalographic signals. The procedure for feature extraction from the raw signal is detailed, as well as the genetic programming system that properly selects the features and evolves the classifiers. Based on the data sets used, two different epileptic patterns were detected: 3 Hz spike-and-slow-wave-complex (SASWC) and spike-or-sharp-wave (SOSW). After training, classifiers for both patterns were tested with unseen instances, and achieved sensibility = 1.00 and specificity = 0.93 for SASWC patterns, and sensibility = 0.94 and specificity = 0.89 for SOSW patterns. Results are very promising and suggest that the methodology presented can be applied to other pattern recognition tasks in complex signals.     

Adaboost算法的脸谱识别系统应用设计

研究了灰度值、中值滤波的图像预处理方法和Haar特征提取思想计算多尺度下相同特征.本文基于Adaboost算法针对同一个训练集训练不同的分类器,并将弱分类器进行集合,构成一个更强的最终分类器,实现了脸谱识别系统.通过验证脸谱识别系统,实现了对视频流中脸谱的准确定位,达到了无拖影、噪声少及识别准确的预期.     

基于肤色和类Harr特征的人脸图像的人眼检测

人眼检测在表情识别和计算机视觉领域得到了广泛的关注和研究,但是在多数的人眼检测方法中,对于背景较复杂的图像,识别率急速下降,误检率急剧上升。经过研究,使用椭圆肤色模型预处理图像,分割出肤色区域和非肤色区域,检测算法只对肤色区域进行人眼检测,有效降低了复杂背景造成的高误检率。同时特征选取是决定检测算法识别率和误检率等性能标准的关键因素,选取类Harr特征训练Adaboost级联分类器,实验表明了类Harr特征的有效性。     

结合改进聚合通道特征和灰度共生矩阵的俯视行人检测算法

针对传统俯视行人检测方法提取的头部特征单一、检测错误率高的问题,提出了结合改进聚合通道特征（ACF）和灰度共生矩阵（GLCM）的俯视行人检测算法。首先,将提取到的HSV颜色特征、梯度幅值大小以及改进后的梯度方向直方图（HOG）特征组合成ACF描述子;然后,采用窗口法计算改进的GLCM参数描述子,提取纹理特征,串联每个窗口的特征向量得到共生矩阵特征描述子;最后,将聚合通道和共生矩阵特征分别输入Adaboost训练得到分类器,并进行检测得到最终结果。实验结果表明,所提算法能在干扰背景存在的情况下有效检测目标,提高了检测的准确率和召回率。     

Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in Heating Ventilation and Air Conditioning systems

Various sensory and control signals in a Heating Ventilation and Air Conditioning (HVAC) system are closely interrelated which give rise to severe redundancies between original signals. These redundancies may cripple the generalization capability of an automatic fault detection and diagnosis (AFDD) algorithm. This paper proposes an unsupervised feature selection approach and its application to AFDD in a HVAC system. Using Ensemble Rapid Centroid Estimation (ERCE), the important features are automatically selected from original measurements based on the relative entropy between the low- and high-frequency features. The materials used is the experimental HVAC fault data from the ASHRAE-1312-RP datasets containing a total of 49 days of various types of faults and corresponding severity. The features selected using ERCE (Median normalized mutual information (NMI) = 0.019) achieved the least redundancies compared to those selected using manual selection (Median NMI = 0.0199) Complete Linkage (Median NMI = 0.1305), Evidence Accumulation K-means (Median NMI = 0.04) and Weighted Evidence Accumulation K-means (Median NMI = 0.048). The effectiveness of the feature selection method is further investigated using two well-established time-sequence classification algorithms: (a) Nonlinear Auto-Regressive Neural Network with eXogenous inputs and distributed time delays (NARX-TDNN); and (b) Hidden Markov Models (HMM); where weighted average sensitivity and specificity of: (a) higher than 99% and 96% for NARX-TDNN; and (b) higher than 98% and 86% for HMM is observed. The proposed feature selection algorithm could potentially be applied to other model-based systems to improve the fault detection performance.