Extraction and analysis of multiple time window features associated with muscle fatigue conditions using sEMG signals

In this work, an attempt has been made to differentiate surface electromyography (sEMG) signals under muscle fatigue and non-fatigue conditions with multiple time window (MTW) features. sEMG signals are recorded from biceps brachii muscles of 50 volunteers. Eleven MTW features are extracted from the acquired signals using four window functions, namely rectangular windows, Hamming windows, trapezoidal windows, and Slepian windows. Prominent features are selected using genetic algorithm and information gain based ranking. Four different classification algorithms, namely naïve Bayes, support vector machines, k-nearest neighbour, and linear discriminant analysis, are used for the study. Classifier performances with the MTW features are compared with the currently used time- and frequency-domain features. The results show a reduction in mean and median frequencies of the signals under fatigue. Mean and variance of the features differ by an order of magnitude between the two cases considered. The number of features is reduced by 45% with the genetic algorithm and 36% with information gain based ranking. The k-nearest neighbour algorithm is found to be the most accurate in classifying the features, with a maximum accuracy of 93% with the features selected using information gain ranking.     

Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features

We propose an effective method for automatic writer recognition from unconstrained handwritten text images. Our method relies on two different aspects of writing: the presence of redundant patterns in the writing and its visual attributes. Analyzing small writing fragments, we seek to extract the patterns that an individual employs frequently as he writes. We also exploit two important visual attributes of writing, orientation and curvature, by computing a set of features from writing samples at different levels of observation. Finally we combine the two facets of handwriting to characterize the writer of a handwritten sample. The proposed methodology evaluated on two different data sets exhibits promising results on writer identification and verification.     

Automatic writer identification framework for online handwritten documents using character prototypes

This paper proposes an automatic text-independent writer identification framework that integrates an industrial handwriting recognition system, which is used to perform an automatic segmentation of an online handwritten document at the character level. Subsequently, a fuzzy c-means approach is adopted to estimate statistical distributions of character prototypes on an alphabet basis. These distributions model the unique handwriting styles of the writers. The proposed system attained an accuracy of 99.2% when retrieved from a database of 120 writers. The only limitation is that a minimum length of text needs to be present in the document in order for sufficient accuracy to be achieved. We have found that this minimum length of text is about 160 characters or approximately equivalent to 3 lines of text. In addition, the discriminative power of different alphabets on the accuracy is also reported.     

Writer identification using oriented Basic Image Features and the Delta encoding

We describe how oriented Basic Image Feature Columns (oBIF Columns) can be used for writer identification and how this texture-based scheme can be enhanced by encoding a writer's style as the deviation from the mean encoding for a population of writers. We hypothesise that this deviation, the Delta encoding, provides a more informative encoding than the texture-based encoding alone. The methods have been evaluated using the IAM dataset and by making entries to two top international competitions for assessing the state-of-the-art in writer identification. We demonstrate that the oBIF Column scheme on its own is sufficient to gain a performance level of 99% when tested using 300 writers from the IAM dataset. However, on the more challenging competition datasets, significantly improved performance was obtained using the Delta encoding scheme, which achieved first place in both competitions. In our characterisation of the Delta encoding, we demonstrate that the method is making use of information contained in the correlation between the written style of different textual elements, which may not be used by other methods.     

Arabic writer identification based on hybrid spectral–statistical measures

Many techniques have been reported for handwriting-based writer identification. None of these techniques assume that the written text is in Arabic. In this paper we present a new technique for feature extraction based on hybrid spectral–statistical measures (SSMs) of texture. We show its effectiveness compared with multiple-channel (Gabor) filters and the grey-level co-occurrence matrix (GLCM), which are well-known techniques yielding a high performance in writer identification in Roman handwriting. Texture features were extracted for wide range of frequency and orientation because of the nature of the spread of Arabic handwriting compared with Roman handwriting, and the most discriminant features were selected with a model for feature selection using hybrid support vector machine–genetic algorithm techniques. Four classification techniques were used: linear discriminant classifier (LDC), support vector machine (SVM), weighted Euclidean distance (WED), and the K nearest neighbours (K_NN) classifier. Experiments were performed using Arabic handwriting samples from 20 different people and very promising results of 90.0% correct identification were achieved.     

An optimal orthonormal system for discriminant analysis

This paper proposes a new discriminant analysis with orthonormal coordinate axes of the feature space. In general, the number of coordinate axes of the feature space in the traditional discriminant analysis depends on the number of pattern classes. Therefore, the discriminatory capability of the feature space is limited considerably. The new discriminant analysis solves this problem completely. In addition, it is more powerful than the traditional one in so far as the discriminatory power and the mean error probability for coordinate axes are concerned. This is also shown by a numerical example.     

人脸检测中基于自适应ICA的特征提取算法

如何从图片中提取出有效特征来区分人脸与非人脸一直是一个难题.文中提出了利用自适应独立成分分析(Self-Adaptive ICA)算法对图像结构信息非常敏感的特点,有效地从大量正面人脸图片中分离出人脸的局部特征,从而利用这些局部特征基底有效地表示人脸图片.自适应ICA算法的优点是能自适应的拟合图像数据的统计性质,而不用预先设定.通过比较待检测的人脸图片与非人脸图片在这组特征基底上的投影系数,可以较好的区分二者.实验结果也表明这种特征提取方法可以找到一组很好的人脸特征基底.使用这种方法构造的弱分类器的分类准确率在相同的误检率下比Boosted Cascaded方法中的弱分类器高1% ～ 1.5%.     

川南石刻图像特征提取方法研究

本文以四川南部地区南宋墓葬石刻艺术为切入点,提出石刻图像特征提取的具体方法,并通过仿真试验证明了该方法的有效性,最后指出了进一步的研究方向.     

Investigation on LP-residual representations for speaker identification

Feature extraction is an essential and important step for speaker recognition systems. In this paper, we propose to improve these systems by exploiting both conventional features such as mel frequency cepstral coding (MFCC), linear predictive cepstral coding (LPCC) and non-conventional ones. The method exploits information present in the linear predictive (LP) residual signal. The features extracted from the LP-residue are then combined to the MFCC or the LPCC. We investigate two approaches termed as temporal and frequential representations. The first one consists of an auto-regressive (AR) modelling of the signal followed by a cepstral transformation in a similar way to the LPC-LPCC transformation. In order to take into account the non-linear nature of the speech signals we used two estimation methods based on second and third-order statistics. They are, respectively, termed as R-SOS-LPCC (residual plus second-order statistic based estimation of the AR model plus cepstral transformation) and R-HOS-LPCC (higher order). Concerning the frequential approach, we exploit a filter bank method called the power difference of spectra in sub-band (PDSS) which measures the spectral flatness over the sub-bands. The resulting features are named R-PDSS. The analysis of these proposed schemes are done over a speaker identification problem with two different databases. The first one is the Gaudi database and contains 49 speakers. The main interest lies in the controlled acquisition conditions: mismatch between the microphones and the interval sessions. The second database is the well-known NTIMIT corpus with 630 speakers. The performances of the features are confirmed over this larger corpus. In addition, we propose to compare traditional features and residual ones by the fusion of recognizers (feature extractor + classifier). The results show that residual features carry speaker-dependent features and the combination with the LPCC or the MFCC shows global improvements in terms of robustness under different mismatches. A comparison between the residual features under the opinion fusion framework gives us useful information about the potential of both temporal and frequential representations.     

Motif analysis for automatic segmentation of CT surface contours into individual surface features

This paper presents a new layer-based technique for automatic high-level segmentation of 3-D surface contours into individual surface features through motif analysis. The procedure starts from a contour-based surface model representing a composite surface area of an object. For each of the surface contours, a relative turning angle (RTA) map is derived. The RTA map usually contains noise and minor features. Algorithms based on motif analysis are applied for extracting a main profile of the RTA map free from background noise and other minor features. All feature points on the extracted profile are further identified from the extracted main profile through further motif analysis. The original contour is thus partitioned into individual segments with the identified feature points. A collection of consecutive contour segments among different layers form an individual 3-D surface feature of the original composite surface. The developed approach using motif analysis is particularly useful for the identification of smooth joins between individual surface features and for the elimination of superposed noise and unwanted minor features.     

Topic identification based on document coherence and spectral analysis

In a world with vast information overload, well-optimized retrieval of relevant information has become increasingly important. Dividing large, multiple topic spanning documents into sets of coherent subdocuments facilitates the information retrieval process. This paper presents a novel technique to automatically subdivide a textual document into consistent components based on a coherence quantification function. This function is based on stem or term chains linking document entities, such as sentences or paragraphs, based on the reoccurrences of stems or terms. Applying this function on a document results in a coherence graph of the document linking its entities. Spectral graph partitioning techniques are used to divide this coherence graph into a number of subdocuments. A novel technique is introduced to obtain the most suitable number of subdocuments. These subdocuments are an aggregation of (not necessarily adjacent) entities. Performance tests are conducted in test environments based on standardized datasets to prove the algorithm’s capabilities. The relevance of these techniques for information retrieval and text mining is discussed.     

A discriminant analysis using composite features for classification problems

In this paper, we propose a new discriminant analysis using composite features for pattern classification. A composite feature consists of a number of primitive features, each of which corresponds to an input variable. The covariance of composite features is obtained from the inner product of composite features and can be considered as a generalized form of the covariance of primitive features. It contains information on statistical dependency among multiple primitive features. A discriminant analysis (C-LDA) using the covariance of composite features is a generalization of the linear discriminant analysis (LDA). Unlike LDA, the number of extracted features can be larger than the number of classes in C-LDA, which is a desirable property especially for binary classification problems. Experimental results on several data sets indicate that C-LDA provides better classification results than other methods based on primitive features.     

高光谱数据特征选择与特征提取研究

高光谱遥感数据的最主要特点是: 传统图像维与光谱维信息融合为一体, 即“图谱合一”。针对高光谱数据波段多、数据量大、冗余度大等特点, 论述了特征选择和特征提取的若干算法, 分析了各自的优缺点。重点研究了导数光谱算法, 并针对二值编码的不足研究了其改进算法-- 四值编码算法。最后用编码技术和导数光谱技术提取了地物的光谱特征参数; 试验表明: 四值编码算法比二值编码算法效果更佳; 光谱导数阶数越高, 对地物特征的表达越有效。     

Evaluation of the effectiveness of features selected by discriminant analysis methods

New theoretical and practical results concerning the use of discriminant analysis for feature selection are presented in the paper. Numerical values for the eigenvalues of the matrix SW_W⁻¹ S_B (within-class and between-class scatter matrices) are investigated. An analytic expression for their minimum value representing the minimum effectiveness is derived. Differences between real values and these minimum values are important for the evaluation of the effectiveness of features and thus for feature selection.