A new approach of audio emotion recognition

A new architecture of intelligent audio emotion recognition is proposed in this paper. It fully utilizes both prosodic and spectral features in its design. It has two main paths in parallel and can recognize 6 emotions. Path 1 is designed based on intensive analysis of different prosodic features. Significant prosodic features are identified to differentiate emotions. Path 2 is designed based on research analysis on spectral features. Extraction of Mel-Frequency Cepstral Coefficient (MFCC) feature is then followed by Bi-directional Principle Component Analysis (BDPCA), Linear Discriminant Analysis (LDA) and Radial Basis Function (RBF) neural classification. This path has 3 parallel BDPCA + LDA + RBF sub-paths structure and each handles two emotions. Fusion modules are also proposed for weights assignment and decision making. The performance of the proposed architecture is evaluated on eNTERFACE’05 and RML databases. Simulation results and comparison have revealed good performance of the proposed recognizer.     

Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging

Early and accurate diagnosis of Parkinson’s disease (PD) is important for early management, proper prognostication and for initiating neuroprotective therapies once they become available. Recent neuroimaging techniques such as dopaminergic imaging using single photon emission computed tomography (SPECT) with ¹²³I-Ioflupane (DaTSCAN) have shown to detect even early stages of the disease. In this paper, we use the striatal binding ratio (SBR) values that are calculated from the ¹²³I-Ioflupane SPECT scans (as obtained from the Parkinson’s progression markers initiative (PPMI) database) for developing automatic classification and prediction/prognostic models for early PD. We used support vector machine (SVM) and logistic regression in the model building process. We observe that the SVM classifier with RBF kernel produced a high accuracy of more than 96% in classifying subjects into early PD and healthy normal; and the logistic model for estimating the risk of PD also produced high degree of fitting with statistical significance indicating its usefulness in PD risk estimation. Hence, we infer that such models have the potential to aid the clinicians in the PD diagnostic process.     

Face recognition with Symmetric Local Graph Structure (SLGS)

Face recognition demonstrates the significant progress in the research field of biometric and computer vision. The fact is due to the current systems perform well under relatively control environments but tend to suffer when the present of variation in pose, illumination, and facial expression. In this work, a novel approach for face recognition called Symmetric Local Graph Structure (SLGS) is presented based on the Local Graph Structure (LGS). Each pixel is represented with a graph structure of its neighbours’ pixels. The histograms of the SLGS were used for recognition by using the nearest neighbour classifiers that include Euclidean distance, correlation coefficient and chi-square distance measures. AT&T and Yale face databases were used to be experimented with the proposed method. Extensive experiments on the face database clearly showed the superiority of the proposed approach over Local Binary Pattern (LBP) and LGS. The proposed SLGS is robust to variation in term of facial expressions, facial details, and illumination. Due to good performance of SLGS, it is expected that SLGS has a potential for application implementation in computer vision.     

纹理豪斯多夫距离在人脸识别算法中的应用

很多经典的人脸识别方法难以适应姿势变化及人脸错位的情形。为了解决这一问题,提出一种基于纹理豪斯多夫距离(THD)的人脸识别算法。将人脸图像的空间量及纹理特征相结合,使其在深入的头部转动和人脸错位中都有很高的容错度。在FERET及Yale两大人脸数据库的实验表明,与其他经典的方法相比较,所提出的方法取得了更好的识别效果。     

基于条件随机场的低图像质量车牌字符分割

提出一种基于条件随机场的车牌字符分割算法,能够对光照不均、相机拍摄角度造成的低图像质量的车牌图像,特别是日益增多的车牌边框与字符相连接车牌图像进行有效的字符分割。算法首先进行车牌图像校正,然后利用标注车牌数据进行模型学习,对车牌图像像素列进行分类识别,最后组合成车牌字符分割结果。理论分析与实验结果验证了算法的有效性。     

基于模糊梯度卷积核的在线棒材断面中心识别

针对传动带上严重堆叠、形状尺寸不规则的棒材,提出基于模糊梯度卷积核的在线棒材断面中心识别方法.以标准圆边缘为中心环,结合棒材断面边缘点偏离中心环的程度定义一个模糊梯度卷积核;通过模糊梯度卷积核与实时棒材断面梯度图像的卷积操作,实现棒材断面中心的增强;依次执行候选点选取、局部峰值抑制、距离检测步骤确定中心位置.实验结果表明,该方法能有效抑制棒材中心增强过程中易产生的峰值扩散现象,减少误识别和漏识别;平均计算时间为8.3 ms,满足系统实时性要求.     

基于αβ运算的多层联想记忆模型的研究

介绍了αβ运算符的定义和αβ联想记忆矩阵的四种操作。通过四种矩阵操作来实现模式对的培训和回忆。αβ多层联想记忆模型相比形态学联想记忆模型数值计算相对容易。最后,通过αβ多层联想记忆的数字模拟实例验证了αβ多层联想记忆具有良好的回忆性能。     

Pattern recognition for characterization of pharmaceutical powders

A large part of pharmaceutical manufacturing involves the use of particulate materials. It is well known that both particle size and shape affect the physical characteristics of tablets. An image processing and analysis algorithm based on the invariant image moment technique was developed in this work to determine the particle shape by comparing features (moments) extracted from templates to those extracted from each of the objects in the image. First it determines the particle shape (rectangle, circle, etc.) and then calculates its specific dimensions (diameter, aspect ratio).The statistical validation of the vision system obtained a repeatability of 0.0012 in and 0.5% relative standard deviation and accuracy within 0.1 to 0.9% of the average value considered as true value. Also the pattern recognition technique indicated high precision and accuracy for images containing particles with some level of contact between them. The shape recognition of microcrystalline cellulose (MCC) indicated that particles of equant and acicular shape as defined by USP are predominant. The results suggest that image processing and analysis would be a suitable tool for pharmaceutical process analytical technologies (PAT) and process optimization.     

相空间重构在语音情感识别中的研究

为了更为全面地表征语音情感状态,弥补线性情感特征参数在刻画不同情感类型上的不足,将相空间重构理论引入语音情感识别中来,通过分析不同情感状态下的混沌特征,提取Kolmogorov熵和关联维作为新的情感特征参数,并结合传统语音特征使用支持向量机（SVM）进行语音情感识别。实验结果表明,通过引入混沌参数,与传统物理特征进行识别的方案相比,准确率有了一定的提高,为语音情感的识别提供了一个新的研究途径。     

LDE融合光谱回归分类的光照变化人脸识别

针对光照变化人脸识别问题中传统的光谱回归算法不能很好地进行特征提取而严重影响识别性能的问题,提出了局部判别嵌入优化光谱回归分类的人脸识别算法。计算出训练样本的特征向量;借助于数据的近邻和分类关系,利用局部判别嵌入算法构建分类问题所需的嵌入,同时学习每种分类的子流形所需的嵌入;利用光谱回归分类算法计算投影矩阵,并利用最近邻分类器完成人脸的识别。在两大人脸数据库扩展YaleB及CMU PIE上的实验验证了该算法的有效性,实验结果表明,相比其他光谱回归算法,该算法取得了更高的识别率、更好的工作特性,并且降低了计算复杂度。