Online motion recognition using an accelerometer in a mobile device

This paper introduces a new method to implement a motion recognition process using a mobile phone fitted with an accelerometer. The data collected from the accelerometer are interpreted by means of a statistical study and machine learning algorithms in order to obtain a classification function. Then, that function is implemented in a mobile phone and online experiments are carried out. Experimental results show that this approach can be used to effectively recognize different human activities with a high-level accuracy.     

A probabilistic integrated object recognition and tracking framework

This paper describes a probabilistic integrated object recognition and tracking framework called PIORT, together with two specific methods derived from it, which are evaluated experimentally in several test video sequences. The first step in the proposed framework is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. These probabilities are updated dynamically and supplied to a tracking decision module capable of handling full and partial occlusions. The two specific methods presented use RGB color features and differ in the classifier implemented: one is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results obtained have shown that, on one hand, the neural net based approach performs similarly and sometimes better than the Bayesian approach when they are integrated within the tracking framework. And on the other hand, our PIORT methods have achieved better results when compared to other published tracking methods in video sequences taken with a moving camera and including full and partial occlusions of the tracked object.     

基于改进2DPCA的快速交通标志识别法

为了提高交通标志的识别速度,提出了一种样本类中间值结合均值的的2DPCA.该方法采用每类训练样本中间值的均值代替训练样本的平均值,以此重建散布矩阵从而得到最优投影矩阵.在已经建立的两个交通标志图像集上用该方法进行了实验,结果表明新方法识别速度得到了大幅提升并且取得了较高的识别率.     

声纹识别系统设计

论述了声纹识别的相关概念、应用领域及其区别于其他身份认证的优势,从软件方面构建了一个基于Matlab的声纹识别系统,并对整个系统进行了仿真实验,实验结果表明该系统具有较高的识别率,可应用于有限人群的身份认证。     

基于多种特征的红外目标识别技术研究

红外图像独特的热辐射特性,通常是由其各个子部件的红外热辐射特性所形成的。文中在传统红外热图像特征量的基础上,通过分析红外热图像自身特性以及它的形成与其自身姿态的关系,提出了对目标物体红外热图像基于目标姿态的子分类,并对目标红外热图像进行了子区域分割,通过多目标分类器进行识别,形成了新的红外图像特征量。     

图形识别的有限自动机方法

针对工程图中具有某种特殊意义的图形符号(如粗糙度、标高符号等),研究了确定性有限自动机识别矢量图形的方法.从DFA出发,构造待识别图形符号的状态集合以及状态间的转换关系,然后通过搜索图形元素集合以及一定的条件进行状态转换,根据其最终所处状态判断识别结果.     

说话人识别中LPCCEP倒谱分量的相对重要性

文中研究表明,反映说话人特征信息的特征参数矢量的各个分量通常具有不同的分布,对正确识别说话人身份的有效性是有差别的。文中通过增减分量的方法对LPCCEP的各维分量进行分析,得到一个关于LPCCEP参数各维分量的平均贡献序列,将此序列运用于WDMVQ有助于系统识别率的提高。     

Auditory-visual cross-modal perception of communicative stimuli in Tufted Capuchin Monkeys (Cebus apella).

Research on cross-modal performance in nonhuman primates is limited to a small number of sensory modalities and testing methods. To broaden the scope of this research, the authors tested capuchin monkeys (Cebus apella) for a seldom-studied cross-modal capacity in nonhuman primates, auditory-visual recognition. Monkeys were simultaneously played 2 video recordings of a face producing different vocalizations and a sound recording of 1 of the vocalizations. Stimulus sets varied from naturally occurring conspecific vocalizations to experimentally controlled human speech stimuli. The authors found that monkeys preferred to view face recordings that matched presented vocal stimuli. Their preference did not differ significantly across stimulus species or other stimulus features. However, the reliability of the latter set of results may have been limited by sample size. From these results, the authors concluded that capuchin monkeys exhibit auditory-visual cross-modal perception of conspecific vocalizations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Theoretical Correlations and Measured Correlations: Relating Recognition and Recall in Four Distributed Memory Models.

This article addresses the relation between item recognition and associative (cued) recall. Going beyond measures of performance on each task, the analysis focuses on the degree to which the contingency between successful recognition and successful recall of a studied item reflects the commonality of memory processes underlying the recognition and recall tasks. Specifically, 4 classes of distributed memory models are assessed for their ability to account for the relatively invariant correlation (≈ .5) between successive recognition and recall. Basic versions of each model either under- or overpredict the intertask correlation. Introducing variability in goodness-of-encoding and response criteria, as well as output encoding, enabled all 4 models to reproduce the moderate intertask correlation and the increase in correlation observed in 2 mixed-list experiments. This model-based analysis provides a general theoretical framework for interpreting contingencies between successive memory tests. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Classification of heart rate data using artificial neural network and fuzzy equivalence relation

The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc. may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human observer. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the signal parameters, extracted and analysed using computers, are highly useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network (ANN) and fuzzy equivalence relations. The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classification. The same data is also used for fuzzy equivalence classifier. The feedforward architecture ANN classifier is seen to be correct in about 85% of the test cases, and the fuzzy classifier yields correct classification in over 90% of the cases.