Region based foreground segmentation combining color and depth sensors via logarithmic opinion pool decision

In this paper we present a novel foreground segmentation system that combines color and depth sensors information to perform a more complete Bayesian segmentation between foreground and background classes. The system shows a combination of spatial-color and spatial-depth region-based models for the foreground as well as color and depth pixel-wise models for the background in a Logarithmic Opinion Pool decision framework used to correctly combine the likelihoods of each model. A posterior enhancement step based on a trimap analysis is also proposed in order to correct the precision errors that the depth sensor introduces. The results presented in this paper show that our system is robust in front of color and depth camouflage problems between the foreground object and the background, and also improves the segmentation in the area of the objects’ contours by reducing the false positive detections that appear due to the lack of precision of the depth sensors.     

WAVE协议栈下基于高斯混合模型的无线定位研究

无线定位是车联网中各应用的基础,对实时性和精度要求更高。基于此,本文提出了一个在WAVE系统下基于混合高斯模型的位置指纹定位算法。该算法由离线训练和在线定位两部分组成,采用GMM对离线训练阶段的指纹数据库进行聚类建模处理,并在定位阶段对结果采用多点均值方法,不仅降到了系统定位误差,也减少了定位阶段的运算量,提高了定位的实时性。实验结果表明,该算法有较好的定位精度和实时性,为WAVE环境下的快速定位进一步研究提供有效参考价值。     

Robust techniques for abandoned and removed object detection based on Markov random field

This paper presents a novel framework for detecting abandoned objects by introducing a fully-automatic GrabCut object segmentation. GrabCut seed initialization is treated as a background (BG) modelling problem that focuses only on unhanded objects and objects that become immobile. The BG distribution is constructed with dual Gaussian mixtures that are comprised of high and low learning rate models. We propose a primitive BG model-based removed object validation and Haar feature-based cascade classifier for still-people detection once a candidate for a released object has been detected. Our system can obtain more robust and accurate results for real environments based on evaluations of realistic scenes from CAVIAR, PETS2006, CDnet 2014, and our own datasets.     

STATISTICAL FEATURE OF PITCH FREQUENCY DISTRIBUTIONS FOR OBUST SPEAKER IDENTIFICATION

This letter proposes an effective and robust speech feature extraction method based on statistical analysis of Pitch Frequency Distributions (PFD) for speaker identification. Compared with the conventional cepstrum, PFD is relatively insensitive to Additive White Gaussian Noise (AWGN), but it does not show good performance for speaker identification, even if under clean environments. To compensate this shortcoming, PFD and conventional cepstrum are combined to make the ultimate decision, instead of simply taking one kind of features into account. Experimental results indicate that the hybrid approach can give outstanding improvement for text-independent speaker identification under noisy environments corrupted by AWGN.     

实际语音盲分离客观评价指标研究

由于没有可比对的语音源信号,如何评价实际语音盲分离的效果非常困难。目前还没有相应的客观评价指标。将语音特征引入实际语音盲分离评价指标,提出了基于信号相关性和Mel倒谱系数高斯混合模型的听觉-独立性联合指标,客观评价了实际语音盲分离的性能。     

一种GMM蠕动微位移机械控制节拍方法

针对超磁致伸缩材料蠕动微位移机械在位移过程中的控制节拍问题,文章研究四节拍运动和六节拍运动,并根据仿生原理,由尺蠖运动规律,设计了以单片机为核心的控制器硬件和软件。通过比较两种节拍运动,情况表明六节拍运动比四节拍运动平稳。     

针对说话人识别进行的加权小波去噪方法

采用改进的小波去噪方法对含噪语音进行了前端处理,并针对说话人识别的特点,在小波重构之前对各小波系数进行加权处理;识别过程采用GMM识别算法。实验结果显示,相比纯粹使用MFCC作为识别特征的说话人识别提出的方法对于含噪说话人识别有明显的优越性。该方法对实时说话人识别有很好的指导作用。     

基于GMM和神经网络的辐射源识别方法

针对基于截获雷达脉冲特征参数的辐射源识别问题,通过建立一个高斯混合模型(GMM),采用最大化期望(EM)方法对模型参数进行训练,构建了一个输入为截获雷达脉冲特征参数,输出为雷达辐射源类型的分类器。同时,为实现对分类识别性能对比,进一步提出基于神经网络方法构建雷达辐射源类型分类器。仿真试验结果表明,基于GMM和神经网络构建的两种分类器均能实现对雷达辐射源的在线识别,且当用于训练的样本比例不低于10%时,均能获得90%以上的分类正确率。     

基于声纹识别技术的简易门禁系统

本文为公司和家庭探索设计了一款基于梅尔倒谱系数和高斯混合模型的声纹识别的简易门禁系统,主要基于STM32平台.为了实现无接触式畅通,用户也可在APP上进行声纹验证,此APP基于讯飞开放平台,具有较高的准确性和识别效率.