Pairwise Registration by Local Orientation Cues

Inspired by recent work on robust and fast computation of 3D Local Reference Frames (LRFs), we propose a novel pipeline for coarse registration of 3D point clouds. Key to the method are: (i) the observation that any two corresponding points endowed with an LRF provide a hypothesis on the rigid motion between two views, (ii) the intuition that feature points can be matched based solely on cues directly derived from the computation of the LRF, (iii) a feature detection approach relying on a saliency criterion which captures the ability to establish an LRF repeatably. Unlike related work in literature, we also propose a comprehensive experimental evaluation based on diverse kinds of data (such as those acquired by laser scanners, Kinect and stereo cameras) as well as on quantitative comparison with respect to other methods. We also address the issue of setting the many parameters that characterize coarse registration pipelines fairly and realistically. The experimental evaluation vouches that our method can handle effectively data acquired by different sensors and is remarkably fast.     

A rapid-prototyping framework for extracting small-scale incident-related information in microblogs: Application of multi-label classification on tweets

Small scale-incidents such as car crashes or fires occur with high frequency and in sum involve more people and consume more money than large and infrequent incidents. Therefore, the support of small-scale incident management is of high importance.Microblogs are an important source of information to support incident management as important situational information is shared, both by citizens and official sources. While microblogs are already used to address large-scale incidents detecting small-scale incident-related information was not satisfyingly possible so far.In this paper we investigate small-scale incident reporting behavior with microblogs. Based on our findings, we present an easily extensible rapid prototyping framework for information extraction of incident-related tweets. The framework enables the precise identification and extraction of information relevant for emergency management. We evaluate the rapid prototyping capabilities and usefulness of the framework by implementing the multi-label classification of tweets related to small-scale incidents. An evaluation shows that our approach is applicable for detecting multiple labels with an match rate of 84.35%.     

An approach for community detection in social networks based on cooperative games theory

Detecting communities is of great importance in social network analysis. However it is an issue that has not yet been satisfactorily solved, despite the efforts made by interdisciplinary research communities over the past few years, because of the nature of complexity in deciding how community structures should be recognized. In this paper we propose an approach based on cooperative game theory for community detection in social networks. We regard individuals as players, and regard communities as coalitions formed by players, and model community detection problem as the formation and optimization of coalitions. Furthermore, we define coalition profile for players to indicate coalitions that players joined, the order of a coalition profile is defined as the number of coalitions in a coalition profile, and we introduce a utility function to measure preference of coalition profiles. Accordingly, we propose an algorithm to detect a coalition profile with maximal utility function values. We have implemented the algorithms developed in this study and experimental results demonstrate the effectiveness of our approaches.     

User Activities Outliers Detection; Integration of Statistical and Computational Intelligence Techniques

In this article, a hybrid technique for user activities outliers detection is introduced. The hybrid technique consists of a two‐stage integration of principal component analysis and fuzzy rule‐based systems. In the first stage, the Hamming distance is used to measure the differences between different activities. Principal component analysis is then applied to the distance measures to find two indices of Hotelling's T² and squared prediction error. In the second stage of the process, the calculated indices are provided as inputs to the fuzzy rule‐based systems to model them heuristically. The model is used to identify the outliers and classify them. The proposed system is tested in real home environments, equipped with appropriate sensory devices, to identify outliers in the activities of daily living of the user. Three case studies are reported to demonstrate the effectiveness of the proposed system. The proposed system successfully identifies the outliers in activities distinguishing between the normal and abnormal behavioral patterns.     

Improving ESL writing using an online formulaic sequence word‐combination checker

Writing correct English sentences can be challenging. Furthermore, writing correct formulaic sequences can be especially difficult because accepted combinations do not follow clear rules governing which words appear together in a sequence. One solution is to provide examples of correct usage accompanied by statistical feedback from web‐based applications for learners to emulate. The goal of the study was to investigate whether such a dedicated web‐based program that provides instant examples of correct word combinations and detailed results of who used these examples, where and when, can improve students' writing. Data were collected from 74 ESL (English as a Second Language) undergraduate students divided into control and experimental groups, who performed formulaic sequence tests, wrote passages in English, followed by interviews with members of the experimental group. The results show that students who used the software outperformed those who did not, on the tests and the overall grades for their writing. Implementing formulaic sequence check programs in writing classes to help students decide which words can appear together, under which circumstances, and in which context is recommended.     

采用双状态传播卡方检验和模糊自适应滤波的容错组合导航算法

太阳能高空长航时无人机导航系统中,捷联惯导/北斗2κ全球卫星导航/星光导航(SINS/BD2/GPS/CNS)是一种可用的组合方案.针对常规容错组合导航算法故障检测类型单一,故障时滤波精度下降的问题,提出一种采用双状态卡方检验(TSPCST)和模糊自适应滤波(FAF)的容错组合导航算法.为了同时检测多种故障,将TSPCST应用于联邦滤波结构中;为了防止故障数据污染系统,利用FAF输出的高精度导航信息,对双状态传播器定期交替校正;进一步,FAF运用TSPCST检测得到的故障信息变量,定义量测子系统模糊有效域,将检测阈值模糊化,以弥补常规固定检测阈值算法难以选取阈值的不足;最后,通过计算信息分配因子,自适应处理多种故障数据.仿真结果表明,该容错组合导航算法性能优于常规固定检测阈值算法.     

一种融合Hough变换和ASM定位瞳孔中心点方法

特征点的准确定位是机器视觉和模式识别的关键技术之一.主动形状模型(ASM)是一种传统的图像特征点定位算法,具有较高的精确性和鲁棒性.为了提高ASM人脸瞳孔特征点定位的精确度,提出了使用Hough变换方法来改进瞳孔特征点的定位.通过ASM算法,初步定位出瞳孔特征点,并使用Sobel算子对图像进行边缘检测,然后在人眼位置选择一个合适的窗口使用Hough圆检测,找出精确瞳孔点相对于ASM初步定位瞳孔点的偏移量.在实验室采集的人脸图像上的对比实验表明,该方法能够显著的改善ASM人眼瞳孔特征点定位准确性.由于使用了初定位进行了搜索范围的限制,计算量也得到了有效的控制.     

面向视频压缩的显著性协同检测JND模型

为了更好的将人眼感知特性用于视频压缩系统,提出了一种改进的基于显著性协同检测的恰可察觉失真模型（Just Noticeable Distortion,JND）.该模型通过像素域和变换域下联合建模计算得到的最优JND模型,基于上下文感知的显著性算法得到相应的显著图,并将检测结果用于JND模型权值分配.提出的JND残差滤波器可以嵌入到HEVC视频编码框架中.实验结果表明:在全I帧配置下,提出的算法编码结果与HM16相比,在视觉主观感知质量一致的情况下,平均码率可节省10.7%.     

A new robust double-talk detector based on the Stockwell transform for acoustic echo cancellation

Despite great developments in the field of acoustic echo cancellation (AEC), the presence of double-talk remains difficult problem. The main role of double-talk detection (DTD) is to control adaptation of the filter coefficients by halting their update in double-talk situations. In this paper, we propose a new method of DTD based on a time–frequency analysis that uses the Stockwell transform (ST).The ST is a time–frequency spectral localization method that combines the characteristics of the short-time Fourier transform and the wavelet transform. This method provides better time–frequency resolution, especially for non-stationary signals. In the experimental tests, the normalized least mean squares (NLMS) algorithm is used to update the filter coefficients along with speech signals taken from the TIMIT database. The obtained results show better performance compared to existing methods in terms of misalignment convergence and speech intelligibility enhancement.     

Set‐based fault detection and isolation for detectable linear parameter‐varying systems

In the context of fault detection and isolation of linear parameter‐varying systems, a challenging task appears when the dynamics and the available measurements render the model unobservable, which invalidates the use of standard set‐valued observers. Two results are obtained in this paper, namely, using a left‐coprime factorization, one can achieve set‐valued estimates with ultimately bounded hyper‐volume and convergence dependent on the slowest unobservable mode; and by rewriting the set‐valued observer equations and taking advantage of a coprime factorization, it is possible to have a low‐complexity fault detection and isolation method. Performance is assessed through simulation, illustrating, in particular, the detection time for various types of faults. Copyright © 2017 John Wiley & Sons, Ltd.