A class of aggregation functions encompassing two-dimensional OWA operators

In this paper we prove that, under suitable conditions, Atanassov’s K_α operators, which act on intervals, provide the same numerical results as OWA operators of dimension two. On one hand, this allows us to recover OWA operators from K_α operators. On the other hand, by analyzing the properties of Atanassov’s operators, we can generalize them. In this way, we introduce a class of aggregation functions - the generalized Atanassov operators - that, in particular, include two-dimensional OWA operators. We investigate under which conditions these generalized Atanassov operators satisfy some properties usually required for aggregation functions, such as bisymmetry, strictness, monotonicity, etc. We also show that if we apply these aggregation functions to interval-valued fuzzy sets, we obtain an ordered family of fuzzy sets.     

Salient feature and reliable classifier selection for facial expression classification

A novel facial expression classification (FEC) method is presented and evaluated. The classification process is decomposed into multiple two-class classification problems, a choice that is analytically justified, and unique sets of features are extracted for each classification problem. Specifically, for each two-class problem, an iterative feature selection process that utilizes a class separability measure is employed to create salient feature vectors (SFVs), where each SFV is composed of a selected feature subset. Subsequently, two-class discriminant analysis is applied on the SFVs to produce salient discriminant hyper-planes (SDHs), which are used to train the corresponding two-class classifiers. To properly integrate the two-class classification results and produce the FEC decision, a computationally efficient and fast classification scheme is developed. During each step of this scheme, the most reliable classifier is identified and utilized, thus, a more accurate final classification decision is produced. The JAFFE and the MMI databases are used to evaluate the performance of the proposed salient-feature-and-reliable-classifier selection (SFRCS) methodology. Classification rates of 96.71% and 93.61% are achieved under the leave-one-sample-out evaluation strategy, and 85.92% under the leave-one-subject-out evaluation strategy.     

An improved method for voice pathology detection by means of a HMM-based feature space transformation

This paper presents new a feature transformation technique applied to improve the screening accuracy for the automatic detection of pathological voices. The statistical transformation is based on Hidden Markov Models, obtaining a transformation and classification stage simultaneously and adjusting the parameters of the model with a criterion that minimizes the classification error. The original feature vectors are built up using classic short-term noise parameters and mel-frequency cepstral coefficients. With respect to conventional approaches found in the literature of automatic detection of pathological voices, the proposed feature space transformation technique demonstrates a significant improvement of the performance with no addition of new features to the original input space. In view of the results, it is expected that this technique could provide good results in other areas such as speaker verification and/or identification.     

Embedding HMMs-based models in a Euclidean space: the topological hidden Markov models

Current extensions of hidden Markov models such as structural, hierarchical, coupled, and others have the power to classify complex and highly organized patterns. However, one of their major limitations is the inability to cope with topology: When applied to a visible observation (VO) sequence, the traditional HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the VO sequence. To fulfill this need, we propose a novel paradigm named “topological hidden Markov models” (THMMs) that classifies VO sequences by embedding the nodes of an HMM state transition graph in a Euclidean space. This is achieved by modeling the noise embedded in the shape generated by the VO sequence. We cover the first and second level topological HMMs. We describe five basic problems that are assigned to a second level topological hidden Markov model: (1) sequence probability evaluation, (2) statistical decoding, (3) structural decoding, (4) topological decoding, and (5) learning. To show the significance of this research, we have applied the concept of THMMs to: (i) predict the ASCII class assigned to a handwritten numeral, and (ii) map protein primary structures to their 3D folds. The results show that the second level THMMs outperform the SHMMs and the multi-class SVM classifiers significantly.     

三维曲面的特征描述和特征匹配

本文提出了一种新的三维曲面特征描述算法,将二维图像上的特征描述思想推广到三维网格。算法将三维网格表示成从顶点到高斯曲率的映射函数,从而获得可类比于二维图像的相似性。借助于法线和梯度建立局部球坐标系,通过二维统计直方图对特征点邻域的几何信息进行描述,使得特征描述具有平移、旋转和缩放不变性,最终生成128维的特征向量(特征描述符)。基于特征向量,我们实现了多分辨率和异拓扑网格下的特征匹配,展示并分析了实验结果。本文的研究动机来源于三维扫描建模以及多视点三维重建技术中对特征描述和特征匹配的需求,主要的应用方向包括:扫描配准、模型注册、动画跟踪、对称检测和模型检索。     

简化的SIFT算法在双目立体视觉中的应用

图像均匀匹配是双目立体视觉领域研究的重点。本文用SIFT特征匹配算法处理立体匹配,并利用构造圆环形窗口以及12维向量表示一个特征点的方法,既保持SIFT算法的尺度不变性,又有效降低了算法的复杂度,提高了算法实时性。     

基于改进的非负矩阵分解的人脸表情识别

针对存在部分遮挡的人脸,提出了一种基于改进的非负矩阵分解的人脸表情识别方法,首先,用改进的非负矩阵分解算法对人脸图像进行表情特征提取,然后用最大相关分类器对面部表情进行分类。在Cohn-Kanade人脸表情数据库上的实验,结果表明,该方法提高了无遮挡的人脸表情识别,对有遮挡的人脸表情识别也有改善。     

基于人工神经网络的智能设计系统研究与实现

针对轴对称的拉深零件的设计,引入了特征编码文件概念,将零件特征以特征编码的形式表示出来。为了实现过程状态模型的智能输出,运用人工神经网络实现自动推理的功能;通过智能推理系统,除去了经验化设计中的不断摸索和改进,将零件的特征造型输入之后,系统能够自动确定零件的工艺流程,进而实现自动化生产。系统能够帮助开发和设计人员迅速的产生设计实例,使开发人员能够更专注于产品的创新活动,对于提高企业的产品设计和研发能力有着重要的实际意义。     

Organizational Norm Congruency and Interpersonal Familiarity in E‐Mail: Examining Messages From Two Different Status Perspectives

Relying on SIDE theory, this 2 × 2 experimental design tested the effects of adherence to organizational norms and interpersonal familiarity in e‐mail messages from both superior and subordinate perspectives. Results reveal that using norm‐congruent e‐mail messages account for over 50% of the variance in both superior and subordinate attitudes toward the message and between 30% and 56% of the variance in perceptions of source credibility. Data from the superiors indicate that norm congruent e‐mail messages account for 14.4% of the variance in message compliance. Results from subordinates reveal an interaction effect that is consistent with SIDE‐based predictions. Subordinates believe superiors are most likely to comply with e‐mail requests from unfamiliar subordinates that are crafted in a manner congruent with organizational norms.     

Extending the two‐stage information systems continuance model: incorporating UTAUT predictors and the role of context

This study presents two extensions to the two‐stage expectation‐confirmation theory of information systems (IS) continuance. First, we expand the belief set from perceived usefulness in the original IS continuance model to include three additional predictors identified in the unified theory of acceptance and use of technology, namely effort expectancy, social influence and facilitating conditions. Second, we ground the IS continuance model in the context of transactional systems that involve transmission of personal and sensitive information and include trust as a key contextual belief in the model. To test the expanded IS continuance model, we conducted a longitudinal field study of 3159 Hong Kong citizens across two electronic government (e‐government) technologies that enable citizens' access to government services. In general, the results support the expanded model that provides a rich understanding of the changes in the pre‐usage beliefs and attitudes through the emergent constructs of disconfirmation and satisfaction, ultimately influencing IS continuance intention. Finally, we discuss the theoretical and practical implications of the expanded model.