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提出一种基于沃尔什特征的增强型AdaBoost 人脸快速检测算法,不仅具有很快的训练速度,而且利
用较少的非人脸样本进行训练就可以达到较好的检测效果。首先,提出用较少的沃尔什特征来代替大量的
Harr - Like 特征可以较大幅度的降低特征之间的冗余。然后提出一种双阈值增强型AdaBoost 算法,其中双阈
值的快速搜索方法大大节约了训练时间,并且在训练Cascaded 检测器过程中,前层分类器的训练结果对后层
分类器的训练具有指导作用,加强了总体检测器的性能,另外通过各层分类器阈值的调节,能够将人脸和非
人脸的训练结果尽量分离。最后,使用该算法训练的检测器对MIT + CMU 人脸测试库进行了测试,结果表明
该方法在训练速度、测试精度、检测时间等方面都优于相应的方法。 相似文献
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改进的 AdaBoost人脸检测方法 总被引:3,自引:0,他引:3
针对传统 AdaBoost算法检测速度快准确率低的问题,本文提出了一种改进的 AdaBoost算法以提高人脸的正确检测率,该算法首先利用快速积分图提取人脸的 Haar特征,然后使用阈值设定的方法对传统的 AdaBoost算法进行改进,并将每次检测的最优弱分类器级联形成最终的强分类器,通过强弱分类器对 Haar特征判别,从而检测图像中的人脸部分。采用本方法对多种实验图像集进行人脸检测实验, FERET彩色图像库的正确检测率为96.07%,视频图像的正确检测率为 96%。实验结果表明,本文所设计的人脸检测算法能够对静态图像以及视频图像中的人脸进行有效检测,为人脸的正确识别打下了基础,该算法也为计算机视觉领域的研究提供一种有效方法。 相似文献
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针对人脸图像超分辨率复原问题,提出了一种新的基于自样本学习的超分辨率复原算法.该算法从输入图像本身提取训练样本库,并采用矢量量化的方法对训练样本进行分类.再利用并行设计的多类预测器对每类样本进行学习训练,指导高频信息的估计重建.对来自输入图像本身的自样本训练集合和来自特定训练图像库的特定训练样本集合进行了对比研究.实验结果表明提出算法在图像重建质量和实现速度上都有很好的表现. 相似文献
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《计量与测试技术》2018,(10)
精确地检测蒙面人脸是鉴别和追踪罪犯或者恐怖分子的重要手段,因此,一个高效的蒙面人脸检测算法这对于打击犯罪,维护社会治安稳定有重要意义。然而,由于面具遮挡导致的人脸信息缺失,传统的人脸检测算法很难取得令人满意的结果。针对这一问题,在本论文中,我们提出了一种适用于蒙面人脸检测的卷积神经网络级联算法。该级联网络共有三级,在训练时,第一级采用整个训练样本集进行训练,之后逐级对前一级训练中分类错误的样本进行训练,以获得更强的辨别能力。这一策略也能避免第一级网络的过度拟合。为了进一步保证算法的检测精度,我们采用迁移学习的方法,利用大型的通用人脸数据集和蒙面人脸数据集来训练和微调分类网络模型。此外,我们还优化了每一级的网络结构,从而提高了计算效率。我们在蒙面人脸的测试数据集上对算法进行测试。实验结果表明,我们在87. 8%的召回率下取得了86. 6%的精确率。并且,相比于传统的卷积神经网络算法,我们的方法具有较高的检测精度和检测效率。 相似文献
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行人检测是计算机视觉中一个重要的研究方向,为了提高行人的识别精度,将支持向量机(Sup-port Vector Machine,SVM)和Adaboost算法结合起来,SVM是基于结构风险最小化准则的新型机器学习算法,适合小样本学习并且能够有效地抑制过拟合问题,Adaboost基于最小化训练错误率,一般使用易训练的分类器作为弱分类器.由于SVM比较难训练,因此将样本集划分形成多个训练集,然后利用正样本和不同的负样本组成不同训练集反复训练,最后通过Adaboost对训练集生成的SVM模型筛选出具有最小错误率的SVM分类器并且采用投票机制形成最终的强分类器.实验结果表明,在FPPW(false positive per window)为10-5时检测率能够达到30%,检测效果优于单个SVM算法训练出来的分类器模型,用行人测试库测试,该方法取得了较好的检测效果并且具有较强的鲁棒性. 相似文献
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基于人工鱼群的Gentle Adaboost快速训练算法 总被引:1,自引:0,他引:1
Gentle Adaboost算法训练弱分类器时,需要遍历特征空间,将分类结果最好的特征作为弱分类器,这将消耗大量的时间。本文提出了一种基于人工鱼群的Gentle Adaboost快速训练算法。人工鱼群算法能够模拟鱼群行为策略,有效的对特征空间快速搜索,减少需要计算的特征数,缩短训练时间。在保证检测效果的条件下,通过对MIT和FERET人脸数据库部分样本的训练,新方法的训练时间约能缩短至原始训练时间的1/4。 相似文献
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In this paper, a novel occlusion invariant face recognition algorithm based on Mean based weight matrix (MBWM) technique is proposed. The proposed algorithm is composed of two phases—the occlusion detection phase and the MBWM based face recognition phase. A feature based approach is used to effectively detect partial occlusions for a given input face image. The input face image is first divided into a finite number of disjointed local patches, and features are extracted for each patch, and the occlusion present is detected. Features obtained from the corresponding occlusion-free patches of training images are used for face image recognition. The SVM classifier is used for occlusion detection for each patch. In the recognition phase, the MBWM bases of occlusion-free image patches are used for face recognition. Euclidean nearest neighbour rule is applied for the matching. GTAV face database that includes many occluded face images by sunglasses and hand are used for the experiment. The experimental results demonstrate that the proposed local patch-based occlusion detection technique works well and the MBWM based method shows superior performance to other conventional approaches. 相似文献
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基于改进的LBP人脸识别算法 总被引:3,自引:1,他引:3
针对基本LBP算子提取的特征不够完整,不能全面地表达出人脸局部特征的问题,提出了基于分块的完备局部二值模式(CLBP)人脸识别算法。首先对原始人脸图像进行分块处理,对每一分块的图像进行局部差异值和中心像素灰度值分析,用Su2CLBP(8,2)、Mu2CLBP(8,2)和CCLBP(8,2)算子分别提取每一分块的直方图统计特征。然后将所有分块的CLBP直方图序列连接起来,得到人脸图像的CLBP特征,将其作为人脸的鉴别特征用于分类识别。最后利用Chi平方统计法计算直方图的不相似度,用最近邻准则进行分类。所提出的算法分别在ORL、FERET、YALE数据库中进行实验,分别取得了高达99.5%、92%、98.67%的识别率,与分块LBP算法相比识别率分别有2.5%、8%、2.67%的提高。实验结果表明,完备LBP提取的特征比较全面且具有较强的鉴别能力,在ORL、FERET、YALE人脸库中均能获得较好的识别率。 相似文献
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在纹理分类中采用谱直方图表示(SHR),每个图像窗表示一个包含滤波后图像直方图的特征向量,而直方图是图像谱表示的连接桥梁.在滤波器选择算法之前,结合每个图像分块和滤波器的独立谱表示和直方图,可以获得更加低层的局部特征.最后,时所有独立滤波器采用滤波器选择算法来得到所需的少量滤波器.为了保证分类的可靠性,选择高斯径向基函数(RBF)进行谱直方图表示,采用支持向量机(SVMs)作为分类函数.对本文方法和其它两种方法:Gabor滤波和独立成分分析(ICA)进行了纹理分类和脸部识别的比较实验.实验结果表明,本文方法具有更高的分类准确性,也证明了SVMs优秀的泛化能力. 相似文献
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行人检测系统涉及交通安全问题,需要很高的鲁棒性,基于单特征结合单核支持向量机的方法效果有限,为解决这一问题,提出采用多特征和多核学习的方法来提升系统的鲁棒性,通过将积分信道特征、多层次导向边缘能量特征和CENTRIST特征分别与直方图交叉核、高斯核和多项式核进行线性组合,采用简单多核学习(Simple MKL)来分别计算核函数的权重系数,将多核学习方法与经典的梯度直方图特征/支持向量机、多尺度梯度直方图特征/直方图交叉核支持向量机和特征融合/直方图交叉核支持向量机的行人检测方法进行比较,实验表明所提出的行人检测算法的鲁棒性有明显提升。 相似文献
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Yi‐Chih Liu Sheng‐De Wang 《International journal of imaging systems and technology》2010,20(4):323-332
In this article, we proposed a novel teleconferencing system that combines a facial muscle model and the techniques of face detection and facial feature extraction to synthesize a sequence of life‐like face animation. The proposed system can animate realistic 3D face images in a low‐bandwidth environment to support virtual videoconferencing. Based on the technique of feature extraction, a face detection algorithm for the virtual conferencing system is proposed in this article. In the proposed face detection algorithm, the YCbCr skin color model is used to detect the possible face area of the image; the feature points of the face is determined by using the symmetry property of the face and the gray level characteristics of the eyes and the mouth. According to the positions of the feature points on a facial image, we can compute the transformation values of the feature points. These values will then be sent via a network from the sender's side to the receiver's side frame by frame. We can synthesize the realistic facial animations on the receiver's side based on these. Experimental results show that the proposed system can achieve a practical animated face‐to‐face virtual conference with good facial expressions and a low‐bandwidth requirement. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 323–332, 2010 相似文献
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《NDT International》1986,19(3):145-153
This paper is concerned with the problem of automatically discriminating both smooth and rough cracks from more benign volumetric flaws such as porosity and slag, using pulse-echo ultrasound. Unlike many previous approaches, digital ultrasonic data were collected from transducers scanned over the whole of each reflector. Scans were also made using different angles of ultrasound.Qualitative physical models for the interaction of ultrasound with these defects are developed to identify three independent effects that, together, could be used to distinguish between these four classes of defect. Each effect is quantified by numerical features computed from the ultrasonic data and criteria are developed to select one feature for each effect. Automated defect classification is then achieved by a weighted minimum distance pattern recognition algorithm. The preliminary application of this approach to a database containing feature values from 40 buried defects in ferritic steel welds gave a classification success rate of 100%. 相似文献
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基于统计肤色模型的敏感图像检测 总被引:2,自引:1,他引:1
针对敏感图像的特征,提出了一种基于肤色分布统计特征的敏感图像检测算法。首先,扫描由小波变换系数构造的零树得到图像的显著点,选择显著强的点作为初始检测集,根据检测集的邻接区颜色梯度特征直方图,采用最大熵模型检测显著性点邻接区肤色信息,利用置信传播算法估计模型参数检测肤色值。其次,由视觉感知的封闭轮廓获得肤色区域解决肤色特征光照敏感性问题。最后,采用多超球一类支持向量机进行分类。实验表明:算法分类准确率达96.32%,同时具有较快的分类速度,平均每秒处理7幅图像。 相似文献
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A methodology is developed to detect defects in NDT of materials using an Artificial Neural Network and signal processing
technique. This technique is proposed to improve the sensibility of flaw detection and to classify defects in Ultrasonic testing.
Wavelet transform is used to derive a feature vector which contains two-dimensional information on various types of defects.
These vectors are then classified using an ANN trained with the back propagation algorithm. The inputs of the ANN are the
features extracted from each ultrasonic oscillogram. Four different types of defect are considered namely porosity, lack of
fusion, and tungsten inclusion and non defect. The training of the ANN uses supervised learning mechanism and therefore each
input has the respective desired output. The available dataset is randomly split into a training subset (to update the weight
values) and a validation subset. With the wavelet features and ANN, good classification at the rate of 94% is obtained. According
to the results, the algorithms developed and applied to ultrasonic signals are highly reliable and precise for online quality
monitoring. 相似文献