【作者】 王大伟；

【导师】 王延杰；

【作者基本信息】 中国科学院研究生院（长春光学精密机械与物理研究所） ， 机械电子工程， 2010， 博士

【摘要】 基于多传感器特征融合的目标识别技术是计算机视觉领域方兴未艾的重要研究课题,为提高多传感器图像系统的目标识别率,解决多传感器的特征融合问题,本文研究了面向目标识别技术的多传感器图像特征融合问题。首先从多元数据分析的角度,研究了基于相关性的多元数据分析和基于独立性的多元数据分析的特征融合方法。(1)针对传统主分量分析很难实现多传感器数据融合的局限性,提出了双向二维复数值主分量分析(2DCPCA)的特征融合方法,从行列两个方向抽取复数值主分量。实验结果表明,在没有剧烈光照和姿态变化情况下,2DCPCA的方法能够获得比使用单一传感器图像的PCA方法更高的识别率;(2)针对线性典型相关分析(LCCA)不能有效描述非高斯分布数据的局限性,引入了核函数,改进了基于核典型相关分析(KCCA)的特征融合方法,实验结果表明提出的改进方法对姿态变化的抵抗效果较好,较普通的基于2DCCA方法识别率提高了5到10个百分点,识别效果好于PCA的方法;(3)典型相关分析和主分量分析是从数据相关性入手探讨数据关系的数学方法,本文进一步从数据独立性讨论特征不变量的提取,提出了基于复数值独立分量分析(Complex ICA)的特征融合方法。实验结果表明,复数值独立分量分析的方法是一种小样本最优方法。在没有剧烈光照和姿态变化情况下,复数值独立分量分析的特征融合方法是目前最具鉴别力的方法。其次,针对多特征协方差矩阵的非欧空间的距离测量方法容易陷入奇异解,分类效果不理想,提出了一种归一化的Fisher方法,实验结果表明,我们的方法与依靠单一特征的目标识别方法相比,能够把识别率提高20%,且对目标的畸变的有很强的抵抗能力。从理论上讨论了粒子群优化算法和人工免疫算法的收敛性。研究了基于自然计算的特征融合方法,提出了基于离散粒子群优化算法和基于离散人工免疫算法的特征融合方法。实验结果表明我们提出的基于粒子群优化算法的特征融合方法,使特征维数变为原来的一半,识别率的精度、稳定性和鲁棒性都有很大的改善。更多还原

【Abstract】 Object recognition based on multi-sensor feature-level fusion is the important researching subject in computer vision. For improving the recognition accuracy of multi-sensor imaging systems, solving the difficult problems of feature-level fusion in multi-sensor systems, we research the feature-level image fusion for Object recognition based on multi-sensor.We study the algorithms of feature-level fusion based on the viewpoint of dependence and independence multi-variant data analysis, respectively. First of all, it is difficult to using traditional principal component analysis in multi-sensor systems, two direction complex valued principal component analysis (2DCPCA) feature-level fusion algorithms is presented, the method extracts PCA features from row and column directions. The experimental results show that 2DCPCA could get higher recognition accuracy than single sensor with PCA without the situation of exquisite illumination and pose changes. Secondly, we introduced the kernel canonical correlation analysis into feature-level fusion method, because traditional linear canonical correlation analysis (LCCA) does not effectively describe the non-Gaussian distribution data. Our experimental results show that the improved method has excellent performance to deal with illumination and pose changes and increase the recognition accuracy 5-10 percent than traditional methods and is better than PCA at the recognition accuracy. Lastly, we discuss the method of extract feature from the viewpoint of data independence and propose the feature-level fusion method based on complex independent component analysis (Complex ICA). The experimental results show that our method is optimal with little samples. It is also demonstrated that our method could improve the most discriminating one without the situation of illumination and pose changes.Furthermore, we introduce multi-feature co-variance matrix into target recognition。However, the distance measure of co-variance matrix in non-Euclidean space usually lead to singular solution and bad recognition accuracy, we put forward the normalized fisher linear discrimination. The experiment results show that our method can improve the recognition accuracy 20% for some targets and has good resistance to target image aberrance. We also study feature fusion based on natural computation. First, we discuss the convergence of particle swarm optimization and artificial immune system. Our mathematic demonstration shows that particle swarm optimization is not the absolutely convergence method, but it is the excellent optimization. Second, we give the principle of binary coding method for feature level. The different discrimination function’s discriminalities are compared with each other. The experiment results show that our method halve the feature dimensions and improve the recognition accuracy, stability and robustness.更多还原