一种基于小波的人脸图像光照归一化算法

光照变化严重影响了人脸图像的外貌,这阻碍了人脸识别的过程。为了识别可变光照下的人脸图像,提出了一种基于小波的光照归一化算法,将一幅图像分解为低频成分和高频成分,对不同的频带成分进行不同的操作——对低频成分进行直方图均衡化,同时着重强调细节（高频成分）,将它乘以一个标量从而增强图像边缘;对修改后的系数进行逆小波变换就得到归一化后的图像。最后,直接使用PCA方法对单训练样本条件下的人脸进行识别,在AR和FERRET人脸库上的实验结果表明,提出的方法可以显著提高人脸识别系统的识别率。     

基于二进小波变换和仿生模式识别的人脸识别

针对人脸图像易受光线和表情影响的特点,提出了一种基于二进小波变换和仿生模式识别的人脸识别方法。应用样条二进小波对人脸图像进行处理,对得到的细节子图进行融合。在FFT和PCA处理与降维后,用仿生模式识别进行学习和识别。实验结果表明,该方法比传统方法具有更高的识别率。     

基于光照归一化分块完备LBP特征的人脸识别

针对复杂光照条件下的人脸识别,提出了一种基于光照归一化分块完备局部二值模式（B-CLBP）特征的人脸识别算法。该方法对人脸图像进行光照归一化预处理,对处理后的人脸图像进行B-CLBP特征提取,融合成B-CLBP直方图,根据最近邻准则进行分类识别。在Extended Yale B人脸库上的实验结果表明,所提算法可以有效提高复杂光照条件下的人脸识别率。     

Properties and some fast algorithms of the Haar transform in image processing and pattern recognition

Properties of the Haar transform in image processing and pattern recognition are investigated. A lower bound of the performance of the Haar transform relative to that of the Karhunen-Loeve transform for first-order Markov processes is found. It is proved that the Haar transform is inferior to the Walsh-Hadamard transform for such processes. A unique condition is presented which, if satisfied by the elements of a matrix, will make the Karhunen-Loeve transform of the matrix and the Haar transform equivalent. Some fast algorithms are given to realize the diagonal elements of a Haar transformed matrix.     

A unifying framework for invariant pattern recognition

We introduce a group-theoretic model of invariant pattern recognition, the Group Representation Network. We show that many standard invariance techniques can be viewed as GRNs, including the DFT power spectrum, higher order neural network and fast translation-invariant transform.     

图像灰度分布标准化算法研究

人脸图像的灰度分布标准化处理是人脸识别的预备工作 ,文献中并不多见 ,且大体上为经验式的 ;本文从较为理论化的角度推导了一种简单的近似标准化算法 ;接着又设计了另一种新算法 ,对图像灰度作精确的标准化处理 .两种算法各有其优缺点和应用场合 ,文中设计了它们的质量评价方法 .本文工作使灰度分布标准化算法研究达到一个比较系统的阶段 .     

Pseudo two-dimensional shape normalization methods for handwritten Chinese character recognition

The nonlinear normalization (NLN) method based on line density equalization is popularly used in handwritten Chinese character recognition. To overcome the insufficient shape restoration capability of one-dimensional NLN, a pseudo two-dimensional NLN (P2DNLN) method has been proposed and has yielded higher recognition accuracy. The P2DNLN method, however, is very computationally expensive because of the line density blurring of each row/column. In this paper, we propose a new pseudo 2D normalization method using line density projection interpolation (LDPI), which partitions the line density map into soft strips and generate 2D coordinate mapping function by interpolating the 1D coordinate functions that are obtained by equalizing the line density projections of these strips. The LDPI method adds little computational overhead to one-dimensional NLN yet performs comparably well with P2DNLN. We also apply this strategy to extending other normalization methods, including line density projection fitting, centroid-boundary alignment, moment, and bi-moment methods. The latter three methods are directly based on character image instead of line density map. Their 2D extensions provide real-time computation and high recognition accuracy, and are potentially applicable to gray-scale images and online trajectories.     

基于Gabor小波变换的人脸疲劳模式识别

疲劳是造成交通事故的主因之一,提出了一种基于Gabor小波变换的疲劳监控新方法。首先,在训练阶段采用频繁模式挖掘算法对疲劳脸部图像序列集进行疲劳模式挖掘;然后,在疲劳识别阶段,将待检测的脸部图像序列基于Gabor小波变换表示为融合特征序列;最后,采用分类算法进行人脸序列的疲劳检测。对自行收集的一天内500幅疲劳图像的仿真结果表明,所提方法正确检测率达到92.8%,错误检测率达到0.02%,优于比较算法。     

正则化最近点优化图像集匹配的三维人脸识别

针对传统的三维人脸识别算法受光照、表情、姿态及遮掩等变化而影响识别性能的问题,提出了一种基于正则化最近点优化图像集匹配算法。将图库图像集和探针图像集建模成正则化仿射包,利用迭代器自动确定两个图像集间的正则化最近点;利用最近子空间分类器最小化正则化最近点;根据正则化最近点之间的欧氏距离及结构计算RNP集之间的距离,利用最近邻分类器完成人脸的识别。在Honda/UCSD、BU4DFE两大视频人脸数据库上的实验验证了该算法的有效性及可靠性,实验结果表明,相比其他几种较为先进的三维人脸识别算法,该算法取得了更好的识别效果,同时,大大减少了训练及测试总完成时间。     

Using eigencolor normalization for illumination-invariant color object recognition

Color is one of salient features for color object recognition, however, the colors of object images sensitively depend on scene illumination. To overcome the lighting dependency problem, a color constancy or color normalization method has to be used. This paper presents a color image normalization method, called eigencolor normalization, which consists of two phases as follows. First, the compacting method, which was originally used for compensating the adverse effect due to shape distortion for 2-D planar objects, is exploited for 3-D color space to make the color distribution less correlated and more compact. Second, the compact color image is further normalized by rotating the histogram to align with the reference axis computed. Consequently, the object colors are transformed into a new color space, called eigencolor space, which reflects the inherent colors of the object and is more invariant to illumination changes. Experimental results show that our eigencolor normalization method is superior to other existing color constancy or color normalization schemes on achieving more accurate color object recognition.     

Adaptive correlation filters for pattern recognition

Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance. The text was submitted by the authors in English. Vitaly Kober obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984 and his PhD degree in 1992 and Doctor of Sciences degree in 2004 in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. He is now a titular researcher at the Centro de Investigatión Cientifica y de Educatión Superior de Ensenada (Cicese), Mexico. His research interests include signal and image processing and pattern recognition. Mikhail Mozerov received his MS degree in Physics from Moscow State University in 1982 and his PhD degree in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences, in 1995. He is with the Laboratory of Digital Optics of the Institute of Information Transmission Problems, Russian Academy of Sciences. His research interests include signal and image processing, pattern recognition, and digital holography. Iosif A. Ovseyevich graduated from the Moscow Electrotechnical Institute of Telecommunications. He received his candidate’s degree in 1953 and doctoral degree in Information Theory in 1972. At present he is Emeritus Professor at the Institute of Information Transmission Problems, Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of the IEEE and Popov Radio Society.     

结合图像归一化与Slant变换的鲁棒图像哈希

为了提高图像哈希算法抵抗几何攻击的性能,提出了一种结合图像归一化和Slant变换的图像哈希算法。使用几何不变矩将原始图像进行归一化操作得到仿射不变图像,仿射不变图像分块并进行Slant变换,根据用户密钥选择Slant变换中频系数极性并生成最终的图像哈希序列。实验结果表明该算法对常见图像操作处理和常见几何攻击鲁棒,对原始图像内容和用户密钥敏感,是一种有效的图像哈希算法。     

Optical pattern recognition using two-channel joint transform correlation

A new joint transform correlation (JTC) technique, named two-channel JTC (TJTC), is proposed in this paper for optical pattern recognition applications. The TJTC technique independently evaluates the autocorrelation and crosscorrelation values of the reference and the target images and employs a modified decision algorithm. In addition, optical threshold operation and fringe-adjusted filter are incorporated in the proposed technique to enhance the correlation output and to improve the discrimination performance. The proposed technique shows better recognition performance compared to existing JTC techniques. Computer simulation are presented to investigate the salient features of the proposed TJTC technique with noise-free as well as noisy input scenes. The text was submitted by the authors in English.     

一种基于色差和彩色归一化的车身颜色识别算法

通过对多个彩色空间色差公式用于色彩识别的比较研究,找出了其中利于色彩识别的彩色空间模型及对应色差公式。在此基础上,针对车身颜色识别系统,利用彩色归一化处理等技术提高了系统的识别精度和鲁棒性。     

Digit recognition in a natural scene with skew and slant normalization

This paper proposes a method to recognize digits in a natural scene, such as telephone numbers on a signboard. Candidate regions of digits are extracted from an image through contrast enhancement, edge extraction, and labeling. Since the target text patterns are in a 3D space, unlike traditional character recognition problems, we have to deal with the image transformation effect due to the orientation in the 3D space and projection. We have to cancel the effect as much as possible before digit recognition. In our method, the image transformation effect is modeled as skew and slant. In the proposed method, simplified Hough transform is used for the skew normalization. After the skew normalization, the remaining effect of image transformation is corrected by circumscribing digit patterns with tilted rectangles and affine transformation. In experiments, we tested a total of 1,332 images of signboards with 11,939 digits. We obtained a digit extraction rate of 99.2% and a correct digit recognition rate of 98.8%.Received: 15 December 2003, Accepted: 21 October 2004, Published online: 2 February 2005     

图像配准的小波分解方法

提出了利用图像与其作小波分解后的近似分量的轮廓相似性，进行图像配准的一种方法．首先利用仿射变换和小波分解的理论，证明了该方法的正确性，并对求配准参数的运算量进行了分析；然后给出了利用该方法实现图像配准的步骤；最后结合MRI图像的配准，对该方法进行了实验验证．该方法能提高配准的速度，对实时图像配准具有实用价值．     

Inexact graph matching for structural pattern recognition

This paper is concerned with the inexact matching of attributed, relational graphs for structural pattern recognition. The matching procedure is based on a state space search utilizing heuristic information. Some experimental results are reported.     

基于字符规范化的文档图像水印算法

运用字符规范化和小波变换的知识,提出一种将文档图像分割成字符图像,再对字符图像规范化,然后将随机序列嵌入到小波图像低频系数的水印算法.根据视觉系统纹理掩蔽特性,将不同强度的水印分量嵌入到了不同的小波系数中.由于文档图像分割和规范化本身具有抗几何攻击的特性,故该方法对缩放、小角度旋转有一定的鲁棒性,实验结果表明:该方法在文档图像上比其它方法更具备优越性.     

基于多尺度中心化二值模式的人脸表情识别

现有局部二值模式(LBP) 算子存在不足: 产生的直方图维数过长、鉴别力不高、对噪声反应敏感. 针对此类问题, 提出中心化二值模式(CBP) 算子, 其优点: 1) 通过比较邻域中近邻点对, 大大降低了直方图维数; 2) 考虑中心像素点的作用并赋予其最高权重, 实现鉴别力的提高; 3) 改变LBP算子的符号函数, 明显减弱白噪声对图像的影响.此外, 为提高识别率, 将多尺度CBP(MCBP) 直方图作为人脸表征. 为增强算法对表情图像中细小变形的鲁棒性, 引入图像欧式距离(IMED) 并将其嵌入MCBP方法. 在JAFFE和Cohn-Kanade表情库的实验结果表明: 所提方法优于其它表情识别方法, IMED可增强MCBP的表情识别能力.     

An efficient low cost approach for on-line signature recognition based on length normalization and fractional distances

This work presents a new proposal for an efficient on-line signature recognition system with very low computational load and storage requirements, suitable to be used in resource-limited systems like smart-cards. The novelty of the proposal is in both the feature extraction and classification stages, since it is based on the use of size normalized signatures, which allows for similarity estimation, usually based on dynamic time warping (DTW) or hidden Markov models (HMMs), to be performed by an easy distance calculation between vectors, which is computed using fractional distance, instead of the more typical Euclidean one, so as to overcome the concentration phenomenon that appears when data are high dimensional. Verification and identification tasks have been carried out using the MCYT database, achieving an EER (common threshold) of 6.6% and 1.8% with skilled and random forgeries, respectively, in the first task and 3.6% of error in the second. The proposed system outperforms DTW-based and HMM-based ones, even though these have proved to be very efficient in on-line signature recognition, with storage requirements between 9 and 90 times lesser and a processing speed between 181 and 713 times greater than the DTW-based systems.