邹采荣
多维数字信号处理理论及其应用
个性化签名
- 姓名:邹采荣
- 目前身份:
- 担任导师情况:
- 学位:
-
学术头衔:
博士生导师
- 职称:-
-
学科领域:
信息处理技术
- 研究兴趣:多维数字信号处理理论及其应用
邹采荣,1963年12月生,江苏省昆山市人,现为东南大学副校长、无线电工程系教授。1984年7月毕业于南京工学院(现为东南大学)无线电工程系水声工程专业,获学士学位;1984年9月至1991年12月就读于无线电工程系信号与信息处理专业,先后获硕士、博士学位;其间曾担任博士生党支部书记;1991年12月至1993年1月在加拿大Concordia大学电子与计算机工程系作博士后研究。1993年1月回国后历任无线电工程系数字信号处理研究室副主任、主任、校科技处副处长、处长及校长助理等职,1999年3月起任现职。目前主要从事多维数字信号处理理论及其应用的研究。为博士生开设《多维数字信号处理》课程一门,先后共发表学术论文50多篇,其中在IEEE Transactions on Circuits & Systems、《电子学报》等国内外重要刊物上发表论文20多篇。参与编写出版专著两部,其中《多维数字信号处理理论及其应用》由国防工业出版社出版,攀登计划丛书《神经智能》由湖南科技出版社出版。三项成果获得教育部科技进步一等奖、二等奖。组织召开了1993年和1995年国际神经网络及信号处理学术大会,担任程序委员会主席一职。邹采荣教授于1994年被评为江苏省优秀青年骨干教师,现为中国高等院校科研管理研究会理事、南京市青年科协主席、江苏省科学学与科研管理研究会副理事长、江苏省高校科协副理事长、国际《电路、系统与信号处理》杂志编委、国家十五“八六三”信息获取与处理主题专家组成员。
-
主页访问
2584
-
关注数
0
-
成果阅读
1530
-
成果数
10
【期刊论文】An Improved Algorithm for Kernel Principal Component Analysis
邹采荣
,-0001,():
-1年11月30日
Kernel principal component analysis (KPCA), introduced by Sch
Kernel principal component analysis, eigenvalue decomposition, feature extraction
-
67浏览
-
0点赞
-
0收藏
-
0分享
-
145下载
-
0评论
-
引用
【期刊论文】Facial Expression Recognition Using Kernel Canonical Correlation Analysis (KCCA)
邹采荣, Wenming Zheng, , Xiaoyan Zhou, Cairong Zou, Li Zhao
,-0001,():
-1年11月30日
In this paper, we address the facial expression recognition (FER) problem using kernel canonical correlation analysis (KCCA). Following the method proposed by Lyons et al. [7] and Zhang et al. [8], we locate 34 points manually from each facial image as the landmark locations and then convert these geometric locations into a labeled graph (LG) [7] vector using Gabor wavelet transformation method to represent the facial image. On the other hand, a semantic expression vector consisting of the semantic ratings of each facial image is used as the semantic expression representation. Learning the correlation between the LG vector and the semantic expression vector is performed by KCCA. According to this correlation, we can estimate the associated semantic expression vector of a given test image and then perform the expression classification according to this semantic expression vector. Moreover, we propose another efficient algorithm for KCCA, which can avoid regularization to the Gram matrix. The experimental results on the Japanese Female Facial Expression (JAFFE) database and the Ekman's "Pictures of Facial Affect" database illustrate effectiveness of the KCCA method in facial expression recognition problem.
Kernel Canonical Correlation Analysis,, Facial Expression Recognition,, Kernel Method,, Generalized Discriminant Analysis.,
-
168浏览
-
0点赞
-
0收藏
-
0分享
-
650下载
-
0评论
-
引用
邹采荣, 何良华, 包永强, 赵力
,-0001,():
-1年11月30日
随着信息和计算机技术的飞速发展,人脸面部表情识别技术越来越受到重视。本文综述了近年来人脸面部表情识别的研究进展。首先对面部表情识别技术的研究背景和发展历程作了简单的回顾,然后着重介绍了几种主要的识别方法,并力争从理论上对各种方法进行分析和比较,最后简单地讨论了进一步提高面部表情识别率的难点和必须考虑的几个重要方面,进而展望了人脸面部表情识别技术的发展方向。
面部表情识别, 面部运动编码系统, 主分量分析
-
550浏览
-
0点赞
-
0收藏
-
0分享
-
2126下载
-
0评论
-
引用
邹采荣, 赵力, 蒋春晖, 吴镇扬
,-0001,():
-1年11月30日
提出了一种利用全局和时序结构的组合特征以及MMD进行情感特征识别的方法。对于从10名话者中搜集的带有欢快,愤怒,惊奇和悲伤4种情感的1000句语句,利用提出的新的识别方法获得了94%的平均情感识别率。
语音信号, 情感特征分析, MMD, 情感识别
-
62浏览
-
0点赞
-
0收藏
-
0分享
-
533下载
-
0评论
-
引用
【期刊论文】Foley-Sammon Optimal Discriminant Vectors Using Kernel Approach
邹采荣, Wenming Zheng, Li Zhao, Cairong Zou
IEEE Transactions on Neural Networks, Vol. 16, No.1, pp. 1-9, 2005,-0001,():
-1年11月30日
A new nonlinear feature extraction method called kernel Foley-Sammon optimal discriminant vectors (KFSODV) is presented in this paper. This new method extends the well-known Foley-Sammon optimal discriminant vectors (FSODV) method from linear domain to a nonlinear domain via the kernel trick that has been used in support vector machine (SVM) and other commonly used kernel based methods. The proposed method also provides an effective technique to solve the so-called "small sample size" (SSS) problem which exists in many classification problems such as face recognition. We give the derivation of KFSODV and the comparison to other commonly used kernel based methods. The experimental results on both simulated and real data confirm that the KFSODV method is superior to other commonly used kernel based methods in terms of the performance of discrimination.
—Foley-Sammon Optimal Discriminant Vectors, Kernel methods,, Kernel Principal Component Analysis, Null Space, Face Recognition.,
-
241浏览
-
0点赞
-
0收藏
-
0分享
-
183下载
-
0评论
-
引用
【期刊论文】Locally Nearest Neighbor Classifiers for Pattern Classification
邹采荣, Wenming Zheng, Li Zhao, Cairong Zou
Pattern Recognition, Vol.37, No.6, pp.1 307-1309, 2004,-0001,():
-1年11月30日
In this paper, two classifiers based on locally nearest neighborhood rule, called nearest neighbor line (NNL) and nearest neighbor plane (NNP), are presented for pattern classification. Comparison to nearest feature line (NFL) and nearest feature plane (NFP), the proposed methods take much lower computation cost and achieve competitive performance.
Pattern Classification, Nearest Feature Line, Nearest Neighbor Line.,
-
83浏览
-
0点赞
-
0收藏
-
0分享
-
134下载
-
0评论
-
引用
【期刊论文】A Modified Algorithm for Generalized Discriminant Analysis
邹采荣, Wenming Zheng, Li Zhao, Cairong Zou
Neural Computation 16(2004)1283-1297,-0001,():
-1年11月30日
Generalized discriminant analysis (GDA) is an extension of the classical linear discriminant analysis (LDA) from linear domain to a nonlinear domain via the kernel trick. However, in the previous lgorithm of GDA, the solutions may suffer from the degenerate eigenvalue problem (i.e., several eigenvectors with the same eigenvalue), which makes them not optimal in terms of the discriminant ability. In this article, we propose a modified algorithm for GDA (MGDA) to solve this problem. The MGDA method aims to remove the degeneracy of GDA and find the optimal discriminant solutions, which maximize the between-class scatter in the subspace spanned by the degenerate eigenvectors of GDA. Theoretical analysis and experimental results on the ORL face database show that the MGDA method achieves better performance than the GDA method.
-
145浏览
-
0点赞
-
0收藏
-
0分享
-
52下载
-
0评论
-
引用
【期刊论文】Optimal Shape Space and Searching in ASM Based Face Alignment
邹采荣, Lianghua He, Stan. Z Li, Jianzhong Zhou, Li Zhao Cairong Zou
,-0001,():
-1年11月30日
The Active Shape Models (ASM) is composed of two parts: the ASM shape model and the ASM search. The standard ASM, with the shape variance components all discarded and searching in image subspace and shape subspace independently, has blind searching and unstable search result. In this paper, we propose a novel idea, called Optimal Shape Subspace, for optimizing ASM search. It is constructed by both main shape and shape variance information. It allows the reconstructed shape to vary more than that reconstructed in the standard ASM shape space, hence is more expressive in representing shapes in real life. A cost function is developed, based on a careful study on the search process especially regarding relations between the ASM shape model and the ASM search. An Optimal Searching method using the feedback provided by the evaluation cost can significantly improve the performance of ASM alignment. This is demonstrated by experimental results.
-
70浏览
-
0点赞
-
0收藏
-
0分享
-
64下载
-
0评论
-
引用
【期刊论文】全局和时序结构特征并用的语音信号情感特征识别方法
邹采荣, 赵力, 王治平, 邹采荣吴镇扬
,-0001,():
-1年11月30日
提出了一种利用全局和各元音时序结构的组合特征以及MQDF进行情感特征识别的方法。对于从10名话者中搜集的带有欢快,愤怒,惊奇和悲伤4 种情感的1000句语句,利用提出的新的识别方法获得了94%的平均情感识别率。
语音信号, 情感特征分析, MQDF, 情感识别
-
78浏览
-
0点赞
-
0收藏
-
0分享
-
124下载
-
0评论
-
引用
邹采荣, 何良华, 赵力
,-0001,():
-1年11月30日
本文提出了一种基于肤色和模板梯度的人脸检测方法,该方法先利用肤色信息找到人脸的大致位置,再利用主分量分析提取出公共脸并以此作为匹配模板,最终采用二阶梯度法作为判别准则。结果表明本算法能明显的提高检测速度,检测率比一般的模板法要有较大地提高。
人脸检测, 肤色, 主分量分析
-
66浏览
-
0点赞
-
0收藏
-
0分享
-
251下载
-
0评论
-
引用