CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor彭思龙
Degree Grantor中国科学院研究生院
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
Keyword纹理 自适应模型 信号分析与合成 线形相位 二维非乘积型小波 滤波器 图像压缩 Texture Adaptive Modeling Method Signal Analysis And Synthesis Blvarlate Texture-matched Nonseparable Wavelet Fllterbanks Image
Abstract纹理是自然图像的一个重要组成部分,也是许多其他类型的图像的一个重要特征,因此图像纹理分析是计算机视觉研究的一个重要方面。本文是对纹理图像的自适应的二维的非乘积型的小波滤波器模型的研究。信号的自适应模型是新一代面向对象的信号处理思想的集中体现,是当今信号分析与合成方法研究的热点。在视频领域,图像的自适应滤波器模型更是一个相当重要的课题,它涉及了 图像处理的各个方面,已经引起了广泛的讨论与兴趣。 本文的前面部分从总体上阐述了一般的信号模型和信号的分析与合成方法 的关系,简单介绍了信号模型的一些背景知识,比如紧支表示、基本扩张和冗余 扩张等等,同时回顾了近四十年来各种用于纹理分析或处理的方法和模型,并对纹理的自适应模型中的难点问题和相关的研究发展进行了分析和讨论。 对于纹理的自适应的二维的非乘积型的小波滤波器选取是本文的主要内容。本文结合F.Ade和T.Greiner的方法,基于二维的非乘积型的小波理论,采用实 验遍历和理论推导两种方法,选取与纹理图像自适应的4×4和6×6的具有线形相位的非乘积型的小波滤波器,用于纹理图像的压缩。在第三章,本文给出了我们的滤波器与同长度的Daubechies小波、Haar小波和Peng小波的对比实验。并且,本文还将自己的方法进行了推广,得到了选取与纹理图像自适应的任意长 度的n维的非乘积型的小波滤波器的一般推导方法,并对这个方法进行了一些讨论。 本文的最后部分从计算机视觉的角度总结了整个研究工作,并提出了进一步需要解决的问题。
Other AbstractTexture is an important characteristic for the analysis of many types of images. It can be seen in all images from multispectral scanner images obtained from aircraft or satellite platforms (which the remote sensing community analyzes) to microscopic images of cell cultures or tissue samples (which the biomedical community analyzes). In this dissertation, we investigate the method of bivariate texture-matched nonseparable wavelet filterbanks for hierarchical texture analysis. Adaptive signal modeling method is the typical representation of the new generation object-orient signal processing. It is the focus of today's signal modeling method. Texture-matched modeling method is an important aspect of Computer Vision, which has been received increased attention in the literature. The first part of this thesis provides a review of background materials related to texture modeling as well as a summary of traditional methods of texture analysis. The difficulties and the newly developments of texture analysis is discussed, which focus on the adaptive modeling method. The main goal of this thesis is the design of bivariate texture-matched nonseparable wavelet filterbanks for texture compression. Connecting with the methods of F.Ade and T.Greiner, we have developed a new method to design the 4×4 and 6×6 texture-matched nonseparable wavelet filterbanks for texture compression. A comparison of the results with the 4×4 Daubechies wavelet, Haar wavelet and Peng wavelet is described in Chapter 3. In addition, we generalize our method to n-dimension space for arbitrary filter-length. In the closing chapter, the key points of the thesis are summarized. The conclusion also discusses extensions to Computer Vision and provides suggestions for further work related to texture compression.
Other Identifier643
Document Type学位论文
Recommended Citation
GB/T 7714
陈惠人. 纹理分析中的最优非乘积型小波选取[D]. 中国科学院自动化研究所. 中国科学院研究生院,2002.
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