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图象的增强与分割方法研究
唐明
学位类型工学博士
导师马颂德
2002-04-01
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词图象分割 非参数概率模型 区域竞争 基于尺度空间的分类 图象 增强 自适应滤波 Image Segmentation Nonparametric Probability Region Competition Scale-space-based Classification Image Enhancement Adaptive f
摘要作为图象分析与理解的基础,图象分割是计算机视觉领域中最基本、也是 最困难的问题之一。图象增强同样是计算机视觉领域中的一个重要问题,它关 系到后继算法的性能。自图象分析和计算机视觉领域诞生以来人们就开始了图 象增强和分割的研究,至今每年仍有许多这方面的研究论文发表。 在图象增强方面,本文首先从理论和实验上分析了近年国际上提出的两种 基于模型的远红外(序列)图象滤波器,即空间同态滤波器(SHF)和时空同 态滤波器(STHF),并在此基础上提出了一种新的基于模型的远红外序列图象 自适应增强算法(ASTHF)。一般地,采用SHF可以获得比采用STHF更好的 增强质量,但处理时间也更长;而采用ASTHF则可以在比采用SHF更短的时 间内获得比采用STHF更高的增强质量。该方法保持了SHF的良好效果和STHF 的计算效率,从而达到了视觉效果和运算速度的良好统一。 图象分割是计算机视觉中获取和分析形状的基础。在计算机视觉领域,图 象分割不仅仅属于图象特征提取问题,它还涉及到各种图象特征的知觉组织。 因此,一种好的分割算法不仅应能有效地利用图象特征,还应能为特征的知觉 组织提供手段。在认真分析研究现有的各种图象分割算法的基础上,本文提出 了一种综合利用边缘和区域信息的图象分割方法-基于尺度空间的区域竞争一 般框架(GSRC)。通过将被误标记可能性小的象素作为种子,GSRC首先自动 确定初始分割(粗分割),然后以能量泛函为工具,通过综合运用轮廓平滑、概 率模型和区域竞争来确定最终的分割(精细分割)。这项工作的其他一些主要贡 献和创新之处是: 1.可以在不同的特征尺度下综合利用边缘和区域信息进行分割,从而更 好地体现视觉的精粗程度,更加符合人类视觉系统的机理; 2.提出了一个实用的自动确定初始分割的算法,从而显著提高了分割算 法的自动化程度; 3.提出了一种在不同特征尺度下语义均匀分割的概念和方法,为图象特 征的知觉组织提供了一种形式化的框架,可以实现不同分割环境下语 义均匀的分割,从而为自顶向下的分割指导提供了一条计算途径。通 过把语义均匀的分割和定量地控制最终分割结果中的区域个数结合 起来,GSRC可以净化分割结果,从而不但降低了后继计算机视觉算 法的复杂度,还可为基于内容的图象数据库检索系统提供工具。 该项研究不仅在理论上有所进展,而且通过大量仿真图象和实际图象的实 验,证明了该理论所形
其他摘要As the basis of image analysis and understanding, image segmentation is one of the most underlying and most difficult problems in computer vision. Image enhancement is also very important, because its results will affect the performances of the following vision algorithms. There have been a great many of researches in image enhancement and segmentation, since the beginning of image analysis and computer vision, and there are still many papers on both topics published up to the present. In image enhancement, this dissertation analyzes two enhancement algorithms, i.e., the spatial and spatiotemporal homomorphic filter (SHF and STHF), proposed in IEEE T-PAMI in 1997 to enhance far infrared images based upon a far infrared imaging model, and proves theoretically and experimentally that the resulting images with SHF are in general smoother than those with STHF, although STHF may reduce the processing time greatly in comparison to SHE Based on this conclusion, an adaptive spatiotemporal homomorphic filter (ASTHF) is proposed. With ASTHF, the resulting images are smoother than those with STHF, while the processing time is less than that with SHF for a similar degree of convergence. ASTHF keeps the advantages of both SHF and STHF, featuring both good quality and less processing time. In image segmentation, this dissertation proposes an integrative segmentation Framework-general scheme of region competition based on scale space (GSRC). GSRC first labels pixels whose corresponding regions can be determined in large likelihood, and then fine-tunes the final regions with the help of probability model, boundary smoothing, and region competition. By means of a novel scale-space-based classification scheme, GSRC controls the extent to which an image is segmented, and establishes a quantitative relation between its parameter and the number of resulting homogeneous regions. GSRC can result in varieties of statistically homogeneous segmentation under different scales of the feature space, and also provides a formal method to group several individually statistically homogeneous patches into a single region which represents a concerned object or its background. Such segmentation is semantically homogeneous. With both semantic homogeneity and quantitative control of the number of the resulting homogeneous regions, GSRC may produce a 'clean' resulting image, therefore simplifying the following procedures. Although the description of the scheme is non-parametric in this dissertation, GSRC can also work parametrically if all non-parametric procedures in this dissertation are substituted with the parametric counterparts.
馆藏号XWLW670
其他标识符670
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5734
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
唐明. 图象的增强与分割方法研究[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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