CASIA OpenIR  > 毕业生  > 硕士学位论文
图像特征提取、匹配和组织
柯启发
Subtype工学硕士
Thesis Advisor马颂德
1997-06-01
Degree Grantor中国科学院自动化研究所
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Abstract本文以特征为主线,总结了我们在特征提取、匹配与组织这些研究方向上所做 的一些工作。按照Marr的计算视觉理论框架,这三个研究方向具有重要地位。 我们首先提出了一种设计微分滤波器的直接方法。微分滤波器是特征检测的常 用工具。传统上为了设计N阶微分滤波器(NODF),我们首先必需找到一N阶 可微的解析函数(ASFDN)作为零阶滤波器(即平滑滤波器),它的N阶微分即 为NODF。我们的方法允许直接设计N阶微分滤波器而不用ASFDN。这样做 的好处是我们可以找到一些NODF,它们的ASFDN可能不存在。我们首先给 出了一个滤波器成为NODF的充要条件,然后提出了设计NODF的系统方法。 在这基础上我们设计了新的过零点检测器用于边缘检测,实验结果说明了它优 于传统的算子。 接下来的一章,我们探讨了立体视觉中的点对应问题。我们利用几何极线的约 束首先将该问题转化为优化问题,然后采用自适应禁忌搜索技术优化目标函 数。这样,特征点匹配、删去外点(outlier detect)和几何极线求解可在同一个优 化过程中的到。在真实图像中的实验证明了该方法的有效性。 第四章的工作属于知觉组织。由特征检测所得到的点或不连续的小线段需经组 织成更抽象的表象才真正具有实际意义。我们在这一章的工作保括几何基元提 取和线性特征提取。几何基元提取实质上是一个优化问题,我们采用禁忌搜索 技术来解决该问题。实验证明该方法准确有效地提取出了直线、圆和椭圆。第 四章的第二部分阐述了线性特征提取的一个新方法,并将它用于卫星图像中公 路提取和医学图像中的血管提取。人眼可以通过全局的显著性迅速地检测出卫 星图像中的公路,但该问题在计算机视觉里一直是一个没有很好解决的问题。 我们提出了一种具有边际效应的能量泛函来表述这种显著性。基于这种能量泛 函,我们给出了公路提取的有效方法,并将该方法成功地运用在医学图像中的 血管提取。
Other AbstractIn this paper, we address some new approaches for feature extraction, feature matching and feature grouping. These subjects are common and important in computer vision according to the theory frame-work of Marr. We first address a direct approach for designing differential operator, which is the most common method for feature extraction. Traditionally, to design a N-th order derivative filter (NODF), an Analytic Smooth Function Derivable up to N-th order (ASFDN) must first be found, whose N-th order derivative is the NODF. Our new approach allows us to design a NODF without using the ASFDN. This is important because we can find a number of NODF satisfied certain desired optimization criteria but their corresponding ASFDN may not exist. We first propose the sufficient and necessary conditions for a filter to be NODF, then the systematic design methods are addressed. New derivative filters are presented and the experiments shows that the new filter works better than the traditional filters. In the following chapter, we address the problem of feature points correspondence, which is a key step is stereo vision. We propose a new approach, the Reactive Tabu Search (RTS) approach, to this problem. Tabu Search is a mctaheuristic search technique that guides a local heuristic search procedure to explore the solution space beyond local optimality. Using RTS to minimizing a proposed cost function, we match the feature points, discard the outliers and recover the epipolar geometry in one step. Experiments on real images show that this approach is effective and fast. Chapt 4 deals with the feature grouping. The feature detector typically produces points or short linear disjointed edge segments, which by themselves are generally of little use unless they are grouped to form a higher level representation. We deal with two typical problems: geometric primitive extraction and linear feature extraction. Geometric primitive extraction is essentially an optimization problem. We propose Tabu Search technique to this problem. Our experiments show this approach works very well in extracting the line, circle and ellipse from the images. The second part of Chapter 4 deals with the linear feature extraction: road extraction from the satellite images. Humans can easily extract roads from images since humans can find the global saliency of roads. How to represent global saliency has been a challenging problem. Here, we propose an energy functional to represent this saliency. Based on the proposed energy functional, we proposed a new and efficient approach for road tracking and extraction. The experiments show that our proposed approach is efficient and without heuristic search.
shelfnumXWLW437
Other Identifier437
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7198
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
柯启发. 图像特征提取、匹配和组织[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,1997.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[柯启发]'s Articles
Baidu academic
Similar articles in Baidu academic
[柯启发]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[柯启发]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.