融合多尺度稀疏与稠密特征结构的透视不变图像匹配模型研究

项目名称： 融合多尺度稀疏与稠密特征结构的透视不变图像匹配模型研究

项目编号： No.61471250

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 无线电电子学、电信技术

项目作者： 李征

作者单位： 四川大学

项目金额： 80万元

中文摘要： 基于人类视觉系统中多层视觉神经细胞的接受域由低层向高层逐层合并的基本结构，本项目以透视不变的多尺度视觉特征为研究对象，旨在解决现有视觉特征提取与匹配模型中的两个关键局限：现有模型不具有透视不变性；稀疏与稠密特征结构缺乏深度融合。主要创新点包括：（1）新目标：1）构造透视不变的局部稠密特征提取与匹配模型，2）建立深入融合稀疏与稠密结构的视觉特征定义，并构造透视不变的多尺度特征提取与匹配模型；（2）新概念：1）透视不变特征点，2）射影高斯核，3）稀密结构融合的视觉特征；（3）新思路：1）使用射影变换适应局部特征的透视形变，从而提高匹配精度，2）基于射影高斯核生成射影变换图像，兼顾反走样与透视不变性，3）在多尺度视觉特征框架中融合稀疏与稠密特征结构，使二者在匹配过程中相互校正，从而获取透视不变性；（4）新方法：1）透视不变的局部特征提取与匹配方法；2）多尺度框架中稀密结构融合的特征匹配方法。

中文关键词： 内容匹配；特征匹配；结构匹配

英文摘要： Based on the theory that receptive fields of cells at one level of the visual system are formed from input by cells at a lower level of the visual system, the feature with multi-scale representation and perspective invariance is considered as the key research object of this project, and the research is aimed at solving two limits of classic visual feature extraction and matching models. The first limit is that the classic models do not have perspective invariance; the second is that classic models is deficient in deep fusion of scattered and dense feature structures.The main innovations of this research include creative objectives, creative concepts, creative ideas, and creative methods. There are two creative objectives in this research. The first objective is to construct a local dense feature extraction and matching model with perspective invariance; the second is to present a visual feature definition with deep fusion of scatterd and dense structures, and construct a multi-scale feature extraction and matching model with perspective invariance. There are three creative concepts in this research. The first concept is the feature point with perspective invariance; the second concept is the projective Gaussian kernel, which is transformed from normal Guassian kernel with projection transformation; and the third concept is the visual feature with fusion of scattered and dense structures. There are three creative ideas in this research. First, projective transformation is applied to adapt to perspective deformation of local feature. Second, before applying projective transformation to a image, convolution with projective Gaussian kernel is applied to perform anti-aliasing and obtain perpective invariance. Third, the fusion mechanism of scatterd and dense feature structures is realized with the mlti-scale representation of visual feature, these two feature structures are rectified with each other in feature matching procedure, and finally perspective invariance of the feature is obtained. There are two creative methods in this research, which are local dense feature extraction and matching method with perspective invariance,and feature matching method based on fusion mechanism of scattered and dense structures with multi-scale representation.

英文关键词： Content Matching;Feature Matching;Structure Matching