Attention Based Detection and Recognition of Hand Postures Against Complex Backgrounds

A system for the detection, segmentation and recognition of multi-class hand postures against complex natural backgrounds is presented. Visual attention, which is the cognitive process of selectively concentrating on a region of interest in the visual field, helps human to recognize objects in cluttered natural scenes. The proposed system utilizes a Bayesian model of visual attention to generate a saliency map, and to detect and identify the hand region. Feature based visual attention is implemented using a combination of high level (shape, texture) and low level (color) image features. The shape and texture features are extracted from a skin similarity map, using a computational model of the ventral stream of visual cortex. The skin similarity map, which represents the similarity of each pixel to the human skin color in HSI color space, enhanced the edges and shapes within the skin colored regions. The color features used are the discretized chrominance components in HSI, YCbCr color spaces, and the similarity to skin map. The hand postures are classified using the shape and texture features, with a support vector machines classifier. A new 10 class complex background hand posture dataset namely NUS hand posture dataset-II is developed for testing the proposed algorithm (40 subjects, different ethnicities, various hand sizes, 2750 hand postures and 2000 background images). The algorithm is tested for hand detection and hand posture recognition using 10 fold cross-validation. The experimental results show that the algorithm has a person independent performance, and is reliable against variations in hand sizes and complex backgrounds. The algorithm provided a recognition rate of 94.36 %. A comparison of the proposed algorithm with other existing methods evidences its better performance.     

选择分类器进行入侵检测

使用集成分类器的方法进行入侵检测,但差的个体分类器往往会对集成性能造成不良影响。因此,使用信息增益法评价各分类器性能,并剔除表现不好的若干个分类器。一方面,降低了分类器空间维数。另一方面,提高了集成效果。在公用的入侵检测数据集上的实验结果表明,本文方法具有较好的集成效果,优于单分类器性能。     

基于实例的强分类器快速集成方法

针对集成分类器由于基分类器过弱,需要牺牲大量训练时间才能取得高精度的问题,提出一种基于实例的强分类器快速集成方法——FSE。首先通过基分类器评价方法剔除不合格分类器,再对分类器进行精确度和差异性排序,从而得到一组精度最高、差异性最大的分类器;然后通过FSE集成算法打破已有的样本分布,重新采样使分类器更多地关注难学习的样本,并以此决定各分类器的权重并集成。实验通过与集成分类器Boosting在UCI数据库和真实数据集上进行比对,Boosting构造的集成分类器的识别精度最高分别能达到90.2%和90.4%,而使用FSE方法的集成分类器精度分别能达到95.6%和93.9%;而且两者在达到相同精度时,使用FSE方法的集成分类器分别缩短了75%和80%的训练时间。实验结果表明,FSE集成模型能有效提高识别精度、缩短训练时间。     

基于Kinect的指尖检测与手势识别方法

针对基于视频的弯曲指尖点识别难、识别率不高的问题,提出一种基于深度信息、骨骼信息和彩色信息的手势识别方法。该方法首先利用Kinect相机的深度信息和骨骼信息初步快速判定手势在彩色图像中所在的区域,在该区域运用YCrCb肤色模型分割出手势区域;然后计算手势轮廓点到掌心点的距离并生成距离曲线,设定曲线波峰与波谷的比值参数来判定指尖点;最后结合弯曲指尖点特征和最大内轮廓面积特征识别出常用的12个手势。实验结果验证阶段邀请了6位实验者在相对稳定的光照环境条件下来验证提出的方法,每个手势被实验120次,12种手势的平均识别率达到了97.92%。实验结果表明,该方法能快速定位手势并准确地识别出常用的12种手势,且识别率较高。     

基于手轮廓的深度图像手势识别方法

提出了一种新的手势识别方法,该方法从深度图像中提取手形轮廓,通过计算手形轮廓与轮廓形心点的距离,使用离散傅里叶变换获得手势的表观特征,引入径向基核的支持向量机识别手势。建立了一个常见的10种手势的数据集,测试获得了97.9%的识别率。     

根据混合选择策略的直觉模糊核匹配追踪集成算法

为了从分类器集成系统中选择出一组差异性大的子分类器,从而提高集成系统的泛化能力,提出了一种基于混合选择策略的直觉模糊核匹配追踪算法.基本思想是通过扰动训练集和特征空间生成一组子分类器;然后采用k均值聚类算法将对所得子分类器进行修剪,删去其中的冗余分类器;最后根据实际识别目标动态选择出较高识别率的分类器组合,使选择性集成规模能够随识别目标的复杂程度而自适应地变化,并基于预期识别精度实现循环集成.实验结果表明,与其他常用的分类器选择方法相比,本文方法灵活高效,具有更好的识别效果和泛化能力.     

基于手势识别的工业机器人操作控制方法

针对目前操作工人与工业机器人之间的交互还是采用比较机械化的交互方式,设计使用Kinect传感器作为手势采集设备,并使用人的手势来对工业机器人进行控制的方法。首先,使用深度阈值法与手部骨骼点相结合的方法,从Kinect传感器获取的数据中准确地提取出手部图像。在提取过程中,操作员无需佩戴任何设备,对操作员所站位置没有要求,对背景环境也没要求。然后,用稀疏自编码网络与Softmax分类器结合的方法对手势图像进行识别,手势识别过程包含预训练和微调,预训练是用逐层贪婪训练法依次训练网络的每一层,微调是将整个神经网络看成一个整体微调整个网络的参数,手势识别的准确率达到99.846%。最后,在自主研发的工业机器人仿真平台上进行实验,在单手和双手手势下都取得了不错的效果,实验结果验证了手势控制工业机器人的可行性和可用性。     

Contact-free and pose-invariant hand-biometric-based personal identification system using RGB and depth data

Hand-biometric-based personal identification is considered to be an effective method for automatic recognition. However, existing systems require strict constraints during data acquisition, such as costly devices,specified postures, simple background, and stable illumination. In this paper, a contactless personal identification system is proposed based on matching hand geometry features and color features. An inexpensive Kinect sensor is used to acquire depth and color images of the hand. During image acquisition, no pegs or surfaces are used to constrain hand position or posture. We segment the hand from the background through depth images through a process which is insensitive to illumination and background. Then finger orientations and landmark points, like finger tips or finger valleys, are obtained by geodesic hand contour analysis. Geometric features are extracted from depth images and palmprint features from intensity images. In previous systems, hand features like finger length and width are normalized, which results in the loss of the original geometric features. In our system, we transform 2D image points into real world coordinates, so that the geometric features remain invariant to distance and perspective effects. Extensive experiments demonstrate that the proposed hand-biometric-based personal identification system is effective and robust in various practical situations.     

基于平均邻域最大化的手势识别方法研究

根据人机交互中手势控制系统的要求,提出一种基于平均邻域最大化(ANMM)算法的静态手势识别方法。将获得的二值化图像轮廓归一化到固定的解析度,构成多维向量,使用ANMM算法对同质相邻与异质相邻向量进行训练,计算出投影方向矩阵。将样本降维处理后,计算其在降维空间内与同质相邻和异质相邻向量的距离,判别样本所属分类。实验结果证明,该方法对静态手势的识别率可达90% 以上。     

Farsi/Arabic handwritten digit recognition based on ensemble of SVD classifiers and reliable multi-phase PSO combination rule

The problem of handwritten digit recognition has long been an open problem in the field of pattern classification and of great importance in industry. The heart of the problem lies within the ability to design an efficient algorithm that can recognize digits written and submitted by users via a tablet, scanner, and other digital devices. From an engineering point of view, it is desirable to achieve a good performance within limited resources. To this end, we have developed a new approach for handwritten digit recognition that uses a small number of patterns for training phase. To improve the overall performance achieved in classification task, the literature suggests combining the decision of multiple classifiers rather than using the output of the best classifier in the ensemble; so, in this new approach, an ensemble of classifiers is used for the recognition of handwritten digit. The classifiers used in proposed system are based on singular value decomposition (SVD) algorithm. The experimental results and the literature show that the SVD algorithm is suitable for solving sparse matrices such as handwritten digit. The decisions obtained by SVD classifiers are combined by a novel proposed combination rule which we named reliable multi-phase particle swarm optimization. We call the method “Reliable” because we have introduced a novel reliability parameter which is applied to tackle the problem of PSO being trapped in local minima. In comparison with previous methods, one of the significant advantages of the proposed method is that it is not sensitive to the size of training set. Unlike other methods, the proposed method uses just 15 % of the dataset as a training set, while other methods usually use (60–75) % of the whole dataset as the training set. To evaluate the proposed method, we tested our algorithm on Farsi/Arabic handwritten digit dataset. What makes the recognition of the handwritten Farsi/Arabic digits more challenging is that some of the digits can be legally written in different shapes. Therefore, 6000 hard samples (600 samples per class) are chosen by K-nearest neighbor algorithm from the HODA dataset which is a standard Farsi/Arabic digit dataset. Experimental results have shown that the proposed method is fast, accurate, and robust against the local minima of PSO. Finally, the proposed method is compared with state of the art methods and some ensemble classifier based on MLP, RBF, and ANFIS with various combination rules.     

基于卷积词袋网络的视觉识别

近年来,卷积神经网络（CNN）凭借其强大的特征学习能力在视觉识别领域取得重要进展。针对CNN全连接层对图像平移、旋转、缩放等变换比较敏感的问题,提出了一种混合模型--卷积词袋网络（BoCW-Net）。它将BoW模型嵌入CNN结构中并代替全连接层,通过端到端的方式学习特征、字典和分类器。为实现BoCW-Net整个网络的有监督学习,提出基于方向相似度的BoCW编码。同时,为充分利用中层特征和高层特征的鉴别性,将中层辅助分类器与高层分类器集成,形成主-辅集成分类器。实验结果表明：相比全连接层,BoCW表示对各种变换具有更强的不变性;主-辅集成分类器能有效融合中层、高层特征,提高BoCW-Net的识别性能;相比新近发展的CNN模型,BoCW-Net在CIFAR-10、CIFAR-100和MNIST数据库上均取得了改进的识别性能,最终分别获得4.88%、22.48%和0.21%的测试错误率。     

A new classifier ensemble methodology based on subspace learning

Different classifiers with different characteristics and methodologies can complement each other and cover their internal weaknesses; so classifier ensemble is an important approach to handle the weakness of single classifier based systems. In this article we explore an automatic and fast function to approximate the accuracy of a given classifier on a typical dataset. Then employing the function, we can convert the ensemble learning to an optimisation problem. So, in this article, the target is to achieve a model to approximate the performance of a predetermined classifier over each arbitrary dataset. According to this model, an optimisation problem is designed and a genetic algorithm is employed as an optimiser to explore the best classifier set in each subspace. The proposed ensemble methodology is called classifier ensemble based on subspace learning (CEBSL). CEBSL is examined on some datasets and it shows considerable improvements.     

基于聚类和随机参考分类器的对海雷达目标识别算法*

为了进一步提高复杂干扰环境下对海雷达目标识别的泛化能力,提出基于k-medoids聚类和随机参考分类器(RRC)的动态选择集成算法(KMRRC).主要利用重采样技术生成多个基分类器,然后基于成对多样性度量准则将基分类器划分为多个簇,并基于校验数据集为每个基分类器构建相应的RRC模型,最后利用RRC从各个簇中动态选择竞争力最强的部分基分类器进行集成决策.通过寻优实验确定KMRRC的参数设置,随后利用Java调用Weka API在自建的目标全极化高分辨距离像(HRRP)样本库及17个UCI数据集上进行KMRRC与常用的9种集成算法和基分类算法的对比实验,并进一步研究多样性度量方法的选取对KMRRC性能的影响.实验验证文中算法在对海雷达目标识别领域的有效性.     

融合关节旋转特征和指尖距离特征的手势识别

作为人机交互的重要方式,手势交互和识别由于其具有的高自由度而成为计算机图形学、虚拟现实与人机交互等领域的研究热点.传统直接提取手势轮廓或手部关节点位置信息的手势识别方法,其提取的特征通常难以准确表示手势之间的区别.针对手势识别中不同手势具有的高自由度以及由于手势图像分辨率低、背景杂乱、手被遮挡、手指形状尺寸不同、个体差异性导致手势特征表示不准确等问题,本文提出了一种新的融合关节旋转特征和指尖距离特征的手势特征表示与手势识别方法.首先从手势深度图中利用手部模板并将手部看成链段结构提取手部20个关节点的3D位置信息;然后利用手部关节点位置信息提取四元数关节旋转特征和指尖距离特征,该表示构成了手势特征的内在表示;最后利用一对一支持向量机对手势进行有效识别分类.本文不仅提出了一种新的手势特征表示与提取方法,该表示融合了关节旋转信息和指尖距离特征;而且从理论上证明了该特征表示能唯一地表征手势关节点的位置信息;同时提出了基于一对一SVM多分类策略进行手势分类与识别.对ASTAR静态手势深度图数据集中8类中国数字手势和21类美国字母手势数据集分别进行了实验验证,其分类识别准确率分别为99.71%和85.24%.实验结果表明,本文提出的基于关节旋转特征和指尖距离特征的融合特征能很好地表示不同手势的几何特征,能准确地表征静态手势并进行手势识别.     

一种基于样本空间动态划分的中文情感识别方法

为了提高中文文本情感识别的精度, 从集成学习的角度出发, 提出了一种基于样本空间动态划分的机制构建文本情感分类器。该算法充分利用训练样本空间内的鉴别信息, 通过引入核平滑方法对样本空间进行自适应划分, 形成若干个具有差异性的多粒度样本子集, 然后分别在每个子集上构造基分类器, 最后将所有基分类器的输出进行融合以产生最终的预测结果。实验结果表明, 该算法在查准率和查全率等方面均优于Bagging、AdaBoost等算法, 并且在大规模样本集的情感识别中具有良好的应用前景。     

分类器动态集成的入侵数据流检测算法

入侵数据流具有快速更新以及概念漂移的特点,静态集成分类器无法及时反映整个空间的数据分布,入侵检测正确率不高,对此,文中提出了一种单分类器动态集成的入侵检测方法,该方法动态分配各分类器权值并用区间估计检查概念漂移并更新分类器。实验结果表明,在处理超平面构造的数据流上,分类效果优于多数投票、加权投票两种静态分类方法,在真实入侵实数据集上有高检测率。     

基于卷积神经网络与集成学习的交通标志识别

为提高复杂情况（如遮挡、透视畸变等）下交通标志识别的精度,提出一种有效的基于卷积神经网络(Convolutional Neural Network, CNN)与集成学习的交通标志识别方法。首先通过融合颜色分割、形态学处理、形状检测等多种方法分割出交通标志,然后利用卷积神经网络对其特征进行提取并分别采用支持向量机(Support Vector Machine, SVM)和Softmax多类分类器对其进行识别,最后将2种分类结果进行集成作为最终的识别结果。实验结果表明,本文算法可有效提高复杂情况下交通标志识别精度,整体上具有较高的性能。     

Human action recognition using an ensemble of body‐part detectors

This paper describes an approach to human action recognition based on a probabilistic optimization model of body parts using hidden Markov model (HMM). Our method is able to distinguish between similar actions by only considering the body parts having major contribution to the actions, for example, legs for walking, jogging and running; arms for boxing, waving and clapping. We apply HMMs to model the stochastic movement of the body parts for action recognition. The HMM construction uses an ensemble of body‐part detectors, followed by grouping of part detections, to perform human identification. Three example‐based body‐part detectors are trained to detect three components of the human body: the head, legs and arms. These detectors cope with viewpoint changes and self‐occlusions through the use of ten sub‐classifiers that detect body parts over a specific range of viewpoints. Each sub‐classifier is a support vector machine trained on features selected for the discriminative power for each particular part/viewpoint combination. Grouping of these detections is performed using a simple geometric constraint model that yields a viewpoint‐invariant human detector. We test our approach on three publicly available action datasets: the KTH dataset, Weizmann dataset and HumanEva dataset. Our results illustrate that with a simple and compact representation we can achieve robust recognition of human actions comparable to the most complex, state‐of‐the‐art methods.     

New hybrid data mining model for credit scoring based on feature selection algorithm and ensemble classifiers

The aim of this paper is to propose a new hybrid data mining model based on combination of various feature selection and ensemble learning classification algorithms, in order to support decision making process. The model is built through several stages. In the first stage, initial dataset is preprocessed and apart of applying different preprocessing techniques, we paid a great attention to the feature selection. Five different feature selection algorithms were applied and their results, based on ROC and accuracy measures of logistic regression algorithm, were combined based on different voting types. We also proposed a new voting method, called if_any, that outperformed all other voting methods, as well as a single feature selection algorithm's results. In the next stage, a four different classification algorithms, including generalized linear model, support vector machine, naive Bayes and decision tree, were performed based on dataset obtained in the feature selection process. These classifiers were combined in eight different ensemble models using soft voting method. Using the real dataset, the experimental results show that hybrid model that is based on features selected by if_any voting method and ensemble GLM + DT model performs the highest performance and outperforms all other ensemble and single classifier models.