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.     

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.     

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。     

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．