Laplacian group sparse modeling of human actions

Recently, many local-feature based methods have been proposed for feature learning to obtain a better high-level representation of human behavior. Most of the previous research ignores the structural information existing among local features in the same video sequences, while it is an important clue to distinguish ambiguous actions. To address this issue, we propose a Laplacian group sparse coding for human behavior representation. Unlike traditional methods such as sparse coding, our approach prefers to encode a group of relevant features simultaneously and meanwhile allow as less atoms as possible to participate in the approximation so that video-level sparsity is guaranteed. By incorporating Laplacian regularization the method is capable to ensure the similar approximation of closely related local features and the structural information is successfully preserved. Thus, a compact but discriminative human behavior representation is achieved. Besides, the objective of our model is solved with a closed-form solution, which reduces the computational cost significantly. Promising results on several popular benchmark datasets prove the efficiency and effectiveness of our approach.     

H.264/SVC mode decision based on mode correlation and desired mode list

Design of video encoders involves implementation of fast mode decision (FMD) algorithm to reduce computation complexity while maintaining the performance of the coding. Although H.264/scalable video coding (SVC) achieves high scalability and coding efficiency, it also has high complexity in implementing its exhaustive computation. In this paper, a novel algorithm is proposed to reduce the redundant candidate modes by making use of the correlation among layers. A desired mode list is created based on the probability to be the best mode for each block in base layer and a candidate mode selection in the enhancement layer by the correlations of modes among reference frame and current frame. Our algorithm is implemented in joint scalable video model (JSVM) 9.19.15 reference software and the performance is evaluated based on the average encoding time, peak signal to noise ration (PSNR) and bit rate. The experimental results show 41.89% improvement in encoding time with minimal loss of 0.02 dB in PSNR and 0.05% increase in bit rate.     

基于线性动态系统稀疏编码的异常事件检测

线性动态系统模型结合稀疏编码实现异常事件检测。线性动态系统可有效地捕捉动态纹理在时间和空间的转移信息,描述视频的时空小块。然而,线性动态系统属于非欧氏空间,无法直接用传统的稀疏编码进行异常检测。基于约束凸优化公式,将相似性变换与稀疏编码结合,可实现线性动态系统稀疏编码的优化求解。实验表明,所提出的方法具有更好的性能。     

改进行移编码的文本数字水印算法研究

研究了文本数字水印在追溯涉密纸质文档中的重要意义,介绍了常见的文本数字水印技术,分析了Brassil经典行移编码文本数字水印技术,在算法的标记策略中引入检验编码,文本行分割划段和引入基调行三个方面进行改进,改进后算法具有更大水印信息容量,更强的鲁棒性和可控的计算量.     

语音识别中小波分析的应用

语音信号是一种时变、非平稳的随机信号。现有语音识别系统的适应性比较差,尤其在高噪声环境下的语音识别非常困难,若将语音识别系统商品化,还有识别速度、拒识问题以及关键词／句检测技术等细节问题需要解决。如何把语言学、生理学、心理学方面的研究成果量化、建模并用于语音识别,还需深入研究。本文介绍了语音识别中小波分析的研究现状及几种常见的技术方法,并且分析探讨了语音识别中小波分析的应用和发展前景。     

孤立词语音识别技术及其应用前景分析

孤立词语音识别技术,采用的是模式匹配法,是语音识别技术的核心之一。首先,用户将词汇表中的每一词依次说一遍,并且将其特征矢量作为模板存入棋板库。然后,将输入语音的特征矢量依次与模板库中的每个模板进行相似度比较,将相似度最高者作为识别结果输出。本文介绍了孤立词语音识别技术的研究现状及几种常见的技术方法,并且分析探讨了孤立词语音识别技术的应用和发展前景。     

A minimum variance distortionless response filter based on the bifrequency spectrum for single-channel noise reduction

This paper deals with the problem of single-channel noise reduction in the short-time Fourier transform (STFT) domain. Many algorithms have been developed to solve this important problem, most of which generally assume that the STFT coefficients in different frequency bands are uncorrelated, so the noise reduction is achieved by applying a gain function to the STFT of the noisy speech in each frequency band. However, this assumption is not accurate and the STFT coefficients of speech signals between neighboring frequency bands are correlated in practice due to the use of small lengths of the fast Fourier transform (FFT) and overlap add/save techniques in implementation. This paper formulates the noise reduction problem by taking into account the interband correlation using the so-called bifrequency spectrum. Based on this formulation, a single-channel minimum variance distortionless response (MVDR) filter is derived, which is shown to be able to significantly improve the signal-to-noise ratio (SNR) and meanwhile maintain the desired speech not much distorted. Simulations are presented to justify the claimed merits of the developed MVDR filter.     

Olfactory coding in the perception of semiochemicals

Information processing in the olfactory pathway underlying the perception of semiochemicals by insects is discussed. Both the chemical message for mates and the message for food consist of blends of chemicals. Olfactory receptors in an insect species are tuned to the detection of those compounds which comprise such chemical messages for that species. The classification of receptors as specialists or generalists coincides with two concepts of information processing, i.e., labeled lines and across-fiber patterns, respectively. The olfactory code coming from antennal receptors inPieris brassicae larvae is a combination of labeled lines and across-fiber patterning. When antennae of adult Colorado potato beetles,Leptinotarsa decemlineata, are stimulated by binary mixtures of leaf odor components, the pattern of neural activities in the olfactory receptors shows some separation into two channels, quantitative versus qualitative detection. The separation is complete in the antennal lobe of this beetle.Presented at the Symposium on Insect Chemical Communications: Unifying Concepts. ACS 194th National Meeting, New Orleans, Louisiana, August 31, 1987.     

一种多参数优化的联合信源信道编码方法*

针对JPEG2000码流的渐进传输特性,提出了一种多参数优化的联合信源信道编码方法,即MPO-JSCC。在码率分配的基础上,动态地选择Turbo码交织长度、迭代译码次数,通过优化编码器多个参数更好地执行不等差错保护策略,同时加入JPEG2000容错工具,在接收端利用错误掩藏机制以提高图像解码成功率。将该方法应用于噪声信道的图像传输系统中,仿真结果表明,MPO-JSCC既能在不增加系统复杂度及延迟时间的同时提高重建图像的质量,又能节省系统发射功率,具有一定的现实指导意义。