摘要显微图像融合是图像融合的一个重要领域，它已广泛应用于生物医学、微电子产品视觉检测、材料检测等领域。图像中蕴涵着丰富的信息，如何获取这些信息并采用有效的手段进行融合是模式识别中的一项重要课题。而图像融合中的特征级融合在目标识别、医疗诊断以及生物特征识别等领域有着越来越重要的作用。 63003
本课题考虑利用主成分分析（PCA）对多层面的源图像序列进行分解，建立各源图像的主成分序列，进而对源图像的主成分序列进行融合处理，最终得到完整的融合图像。
本课题中的算法利用主成分分解可以保留原数据中的主要信息这一特点, 由源图像获取数据的协方差矩阵, 协方差矩阵的特征值和特征向量, 据此确定图像融合算法中的加权系数和最终融合图像。并且本课题对基于主成分分析的特征融合算法的运算进行了一定的深入研究。最终试验表明, 应用该算法融合后的图像取得了满意的效果.
毕业论文关键词 显微图像 融合 主成分分析 加权
毕业设计说明书（论文）外文摘要
Title Based on principal component analysis (PCA) algorithm microscopic image
Abstract As an important field in image fusion, microscopic image fusion has been applied widely in the field of biomedicine, microelectronic product visual inspection and material inspection and so on. How to get the information and use efficient ways for fusion is an important topic in pattern recognition because there is a lot of information in images. The feature fusion of image has more and more important applications, such as in target recognition, medical treatment and biology feature recognition.
This topic considered the principal component analysis (PCA) of the multi-level decomposition of the source image sequences, to establish a main component of the sequence of source images, and thus the main component of the source image fusion sequence, the full integration of the final image obtained.
The subject of the principal component decomposition algorithm can preserve the original data in the main message of this feature, access to data from the source image covariance matrix, covariance matrix eigenvalues and eigenvectors, which determine the weighted image fusion algorithm coefficients and final fused image. And this topic based on principal component analysis feature fusion algorithm works for a certain amount of in-depth study. Final tests showed that the application of the algorithm fused image with satisfactory results.
Keywords microscopic images fuse principal component analysis weighted
目 次
1 引言 1
1.1 课题研究的背景和意义 1
1.2.1 图像融合技术研究现状 2
1.2.2 显微图像融合技术现状 3
2 图像融合 4
2.1 显微图像融合基本理论 4
2.1.1 光学显微镜透镜成像的几何光学原理 4
2.1.2 显微光学成像系统的特性 5
2.2 图像融合技术概要 7
2.2.1 图像融合的形式与层次 7
2.2.2 图像融合的目的与思想 10
2.2.3 图像融合的算法 基于主成分分析（PCA）的显微图像算法的研究:http://www.751com.cn/tongxin/lunwen_69367.html