Shape recognition based on Kernel-edit distance

In this paper a kernel method for shape recognition is proposed. The approach is based on the edit distance between pairs of shapes after transforming them into symbol strings. The transformation of shapes into symbol strings is invariant to similarity transforms and can handle partial occlusions. Representation of shape contours uses the shape contexts and applies dynamic programming for finding the correspondence between points over shape contours. Corresponding points are then transformed into symbolic representation and the normalized edit distance computes the dissimilarity between pairs of strings in the database. Obtained distances are then transformed into suitable kernels which are classified using support vector machines. Experimental results over a variety of shape databases show that the proposed approach is suitable for shape recognition.     

Face recognition using common faces method

In this paper, we propose a face recognition method called the commonface by using the common vector approach. A face image is regarded as a summation of a common vector which represents the invariant properties of the corresponding face class, and a difference vector which presents the specific properties of the corresponding face image such as face appearance, pose and expression. Thus, by deriving the common vector of each face class, the common feature of each person is obtained which removes the differences of face images belonging to the same person. For test face image, the remaining vector with each face class is derived with the similar procedure to the common vector, which is then compared with the common vector of each face class to predict the class label of query face by finding the minimum distance between the remaining vector and the common vector. Furthermore, we extend the common vector approach (CVP) to kernel CVP to improve the performance of CVP. The experimental results suggest that the proposed commonface approach provides a better representation of individual common feature and achieves lower error rates in face recognition.     

Face recognition using difference vector plus KPCA

In this paper, a novel approach for face recognition based on the difference vector plus kernel PCA is proposed. Difference vector is the difference between the original image and the common vector which is obtained by the images processed by the Gram-Schmidt orthogonalization and represents the common invariant properties of the class. The optimal feature vectors are obtained by KPCA procedure for the difference vectors. Recognition result is derived from finding the minimum distance between the test difference feature vectors and the training difference feature vectors. To test and evaluate the proposed approach performance, a series of experiments are performed on four face databases: ORL, Yale, FERET and AR face databases and the experimental results show that the proposed method is encouraging.     

3D shape recovery from image focus using kernel regression in eigenspace

Shape from focus (SFF) is one of the optical passive methods for three dimensional (3D) shape recovery of an object from its two dimensional (2D) images. The focus measure plays important role in SFF algorithms. Mostly, conventional focus measures are based on gradient, so their performance is restricted under noisy conditions. Moreover, SFF methods also suffer from loss of focus information due to discreteness. This paper introduces a new SFF method based on principal component analysis (PCA) and kernel regression. The focus values are computed through PCA by considering a sequence of small 3D neighborhood for each object point. We apply unsupervised regression through Nadaraya and Watson Estimate (NWE) on depth values to get a refined 3D shape of the object. It reduces the effect of noise within a small surface area as well as approximates the accurate 3D shape by exploiting the depth dependencies in the neighborhood. Performance of the proposed scheme is investigated in the presence of different types of noises and textured areas. Experimental results demonstrate effectiveness of the proposed approach.     

Learning to generate novel views of objects for class recognition

Multi-view object class recognition can be achieved using existing approaches for single-view object class recognition, by treating different views as entirely independent classes. This strategy requires a large amount of training data for many viewpoints, which can be costly to obtain. We describe a method for constructing a weak three-dimensional model from as few as two views of an object of the target class, and using that model to transform images of objects from one view to several other views, effectively multiplying their value for class recognition. Our approach can be coupled with any 2D image-based recognition system. We show that automatically transformed images dramatically decrease the data requirements for multi-view object class recognition.     

基于纹理特征和轮廓光流矢量的烟雾识别

为了提高火灾探测的准确率和快速性,提出了基于纹理特征和轮廓光流矢量的烟雾识别算法。一方面为了获得更全面的纹理特征,建立图像金字塔,使用局部二值模式（ LBP）和基于方差的局部二值模式（ LBPV）结合的新方法分别提取金字塔不同层的纹理特征。另一方面是动态纹理特征,由于烟雾运动的湍流特性导致方向具有特定的一致性,改进了对全部可疑区域进行分析的方法,仅对可疑区域轮廓进行光流矢量分析,降低运算量。将静态纹理特征和动态纹理特征输入支持向量机（ SVM ）中进行识别。采用“静—静—动”的新型识别方法,实验结果表明：该算法能够及时准确报警,可靠率高。     

基于轮廓PCA的字母手势识别算法研究

提出了一种改进的手势检测识别算法。首先对采集的手势图像综合运用Krisch算子的背景差分与肤色分割等算法进行预处理,然后根据手势的方向去除胳膊等非目标,截取手型区域。最后对手型图像进行轮廓检测,根据手型轮廓的二值图像进行PCA降维,并与投影降维后的样本计算空间距离,从而进行手势分类。通过此方法可以快速、准确地检测到手型区域,识别手型含义,且具有实时性。     

ECG biometric authentication based on non-fiducial approach using kernel methods

Identity recognition faces several challenges especially in extracting an individual's unique features from biometric modalities and pattern classifications. Electrocardiogram (ECG) waveforms, for instance, have unique identity properties for human recognition, and their signals are not periodic. At present, in order to generate a significant ECG feature set, non-fiducial methodologies based on an autocorrelation (AC) in conjunction with linear dimension reduction methods are used. This paper proposes a new non-fiducial framework for ECG biometric verification using kernel methods to reduce both high autocorrelation vectors' dimensionality and recognition system after denoising signals of 52 subjects with Discrete Wavelet Transform (DWT). The effects of different dimensionality reduction techniques for use in feature extraction were investigated to evaluate verification performance rates of a multi-class Support Vector Machine (SVM) with the One-Against-All (OAA) approach. The experimental results demonstrated higher test recognition rates of Gaussian OAA SVMs on random unknown ECG data sets with the use of the Kernel Principal Component Analysis (KPCA) as compared to the use of the Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA).     

Palmprint authentication using a symbolic representation of images

A new branch of biometrics, palmprint authentication, has attracted increasing amount of attention because palmprints are abundant of line features so that low resolution images can be used. In this paper, we propose a new texture based approach for palmprint feature extraction, template representation and matching. An extension of the SAX (Symbolic Aggregate approXimation), a time series technology, to 2D data is the key to make this new approach effective, simple, flexible and reliable. Experiments show that by adopting the simple feature of grayscale information only, this approach can achieve an equal error rate of 0.3%, and a rank one identification accuracy of 99.9% on a 7752 palmprint public database. This new approach has very low computational complexity so that it can be efficiently implemented on slow mobile embedded platforms. The proposed approach does not rely on any parameter training process and therefore is fully reproducible. What is more, besides the palmprint authentication, the proposed 2D extension of SAX may also be applied to other problems of pattern recognition and data mining for 2D images.     

A Bayesian approach to object detection using probabilistic appearance-based models

In this paper, we introduce a Bayesian approach, inspired by probabilistic principal component analysis (PPCA) (Tipping and Bishop in J Royal Stat Soc Ser B 61(3):611–622, 1999), to detect objects in complex scenes using appearance-based models. The originality of the proposed framework is to explicitly take into account general forms of the underlying distributions, both for the in-eigenspace distribution and for the observation model. The approach combines linear data reduction techniques (to preserve computational efficiency), non-linear constraints on the in-eigenspace distribution (to model complex variabilities) and non-linear (robust) observation models (to cope with clutter, outliers and occlusions). The resulting statistical representation generalises most existing PCA-based models (Tipping and Bishop in J Royal Stat Soc Ser B 61(3):611–622, 1999; Black and Jepson in Int J Comput Vis 26(1):63–84, 1998; Moghaddam and Pentland in IEEE Trans Pattern Anal Machine Intell 19(7):696–710, 1997) and leads to the definition of a new family of non-linear probabilistic detectors. The performance of the approach is assessed using receiver operating characteristic (ROC) analysis on several representative databases, showing a major improvement in detection performances with respect to the standard methods that have been the references up to now.This revised version was published online in November 2004 with corrections to the section numbers.     

基于Curvelet变换和SVM的人脸表情识别方法研究

论文将Curvelet变换用于人脸表情识别,提出了一种基于Curvelet变换与SVM相结合的人脸表情识别方法.在表情特征提取过程中,还采用了主分量分析方法对Curvelet变换分解后得到的系数特征进行降维处理.分别对JAFFE和Cohn-Kanade表情数据库进行了实验,结果表明该方法可以有效地对人脸表情进行识别,与其他方法比较,采用该文方法得到人脸表情的平均识别率明显更优.     

An efficient 3D face recognition approach using local geometrical signatures

This paper presents a computationally efficient 3D face recognition system based on a novel facial signature called Angular Radial Signature (ARS) which is extracted from the semi-rigid region of the face. Kernel Principal Component Analysis (KPCA) is then used to extract the mid-level features from the extracted ARSs to improve the discriminative power. The mid-level features are then concatenated into a single feature vector and fed into a Support Vector Machine (SVM) to perform face recognition. The proposed approach addresses the expression variation problem by using facial scans with various expressions of different individuals for training. We conducted a number of experiments on the Face Recognition Grand Challenge (FRGC v2.0) and the 3D track of Shape Retrieval Contest (SHREC 2008) datasets, and a superior recognition performance has been achieved. Our experimental results show that the proposed system achieves very high Verification Rates (VRs) of 97.8% and 88.5% at a 0.1% False Acceptance Rate (FAR) for the “neutral vs. nonneutral” experiments on the FRGC v2.0 and the SHREC 2008 datasets respectively, and 96.7% for the ROC III experiment of the FRGC v2.0 dataset. Our experiments also demonstrate the computational efficiency of the proposed approach.     

基于PCA融合PSO-SVM的运动想象脑电信号分类方法

运动想象脑电信号的分类识别是当前脑机接口(BCI)技术面临的难点.针对该问题,提出一种融合主成分分析(PCA)和粒子群优化-支撑向量机(PSO-SVM)的运动想象脑电信号分类方法.首先利用PCA对采集到的高维脑电信号进行分析,剔除其中噪声分量并提取三维反应不同脑电信号差异特性的特征向量.然后利用SVM对特征向量进行分类...     

PCA-改进RPROP方法的BP算法在音乐信号分类中的应用

针对标准的BP神经网络仅从预测误差负梯度方向修正权值和阈值,学习过程收敛缓慢,并且容易陷入局部最小值,导致泛化能力不足的问题,提出了一种基于学习经验变学习速率改进的RPROP方法作为BP神经网络权值和阈值更新方法,并与主成分分析法(Principal Component Analysis,PCA)相结合,形成了PCA-改进神经网络算法。同时,采用Matlab软件对四类音乐信号进行分类实验。实验结果表明,改进算法比标准算法的稳定识别率提高2.6%,当稳定识别率达到90%时,用时节省75%,表明该算法可以加快网络的收敛过程,提高泛化能力。     

Preservation of local linearity by neighborhood subspace scaling for solving the pre-image problem

An important issue involved in kernel methods is the pre-image problem. However, it is an ill-posed problem, as the solution is usually nonexistent or not unique. In contrast to direct methods aimed at minimizing the distance in feature space, indirect methods aimed at constructing approximate equivalent models have shown outstanding performance. In this paper, an indirect method for solving the pre-image problem is proposed. In the proposed algorithm, an inverse mapping process is constructed based on a novel framework that preserves local linearity. In this framework, a local nonlinear transformation is implicitly conducted by neighborhood subspace scaling transformation to preserve the local linearity between feature space and input space. By extending the inverse mapping process to test samples, we can obtain pre-images in input space. The proposed method is non-iterative, and can be used for any kernel functions. Experimental results based on image denoising using kernel principal component analysis （PCA） show that the proposed method outperforms the state-of-the-art methods for solving the pre-image problem.     

Spatio-temporal shape building from image sequences using lateral interaction in accumulative computation

To be able to understand the motion of non-rigid objects, techniques in image processing and computer vision are essential for motion analysis. Lateral interaction in accumulative computation for extracting non-rigid shapes from an image sequence has recently been presented, as well as its application to segmentation from motion. In this paper, we introduce a modified version of the first multi-layer architecture. This version uses the basic parameters of the LIAC model to spatio-temporally build up to the desired extent the shapes of all moving objects present in a sequence of images. The influences of LIAC model parameters are explained in this paper, and we finally show some examples of the usefulness of the model proposed.     

Improving the classification accuracy of streaming data using SAX similarity features

The classification accuracy of time series is highly dependent on the quality of used features. In this study, features of new type, called SAX (Symbolic Aggregate approXimation) similarity features, are presented. SAX similarity features are a combination of the traditional statistical number-based and the template-based classification. SAX similarity features are obtained from the data of the time window by first transforming the time series into a discrete presentation using SAX. Then the similarity between this SAX presentation and predefined SAX templates are calculated, and these similarity values are considered as SAX similarity features. The functioning of these features was tested using five different activity data sets collected using wearable inertial sensors and five different classifiers. The results show that the recognition rates calculated using SAX similarity features together with traditional features are much better than those obtained employing traditional features only. In 20 tested cases out of 23, the improvement is statistically significant according to the paired t-test.