【作者】 胡良梅；

【导师】 高隽；

【作者基本信息】 合肥工业大学 ， 信号与信息处理， 2006， 博士

【摘要】 图像理解的根本任务就是要正确解释所感知的图像场景。图像数据本身存在含糊性,图像理解中的信息获取、知识表述以及目标识别等都存在信息的不确定问题。而信息融合技术研究多源信息的加工和协同利用,形成多形式信息互补,获得对同一事物或目标更客观、更本质的认识。采用信息融合的智能信息处理技术可以有效解决图像理解中的不确定性问题,是一种新颖的研究思路,具有深远的理论意义和广阔的应用前景。 论文在对信息融合理论和方法研究的基础上,采用信息融合技术分别从像素层融合、特征层融合以及决策层融合这三个层次对于图像理解中的信息获取、知识表述以及目标识别问题展开研究。 论文主要工作如下: (1)通过分析图像理解中的信息获取、数据和知识表述方法及目标识别问题的研究现状和存在问题,讨论了研究基于信息融合的图像理解方法的有效性。 (2)从图像数据融合的角度研究基于像素层融合的图像信息获取方法。比较了常用的像素层图像融合算法,分析了融合图像的质量评价问题,提出了一种新的融合图像质量评价标准,并提出了一种新的夜视图像彩色化融合算法,实验结果表明该算法的有效性。 (3)将D-S证据理论等不确定性处理的方法引入到特征层融合,研究其在图像理解中知识表述方面的应用,分析了D-S证据理论中的关键问题和解决途径,提出了基于D-S证据理论的融合图像分割及融合边缘提取的新方法,得到了较好的图像分割及边缘提取结果。 (4)研究基于决策层融合的多分类器目标识别,针对图像理解中的多类目标识别问题,提出了一种基于D-S证据理论的多特征的层次识别方法,讨论了D-S证据理论与模糊集合方法的联系,并应用于生物特征认证和交通标志识别中。更多还原

【Abstract】 The essential task of image understanding is to interpret the acquired image scene accurately. Since image data are some extent fuzzy, the processing of the uncertainty information is of vital importance to all three major steps of image understanding: information acquisition, data representation, and object recognition. Information fusion techniques involve how to process and synthesize information from multi-source, and make them complementary to each other, to obtain knowledge on the object observed which are more objective, more essential than from single source. It is effective to apply information fusion, which is one of the most important fields in intelligent information processing, to process the uncertainty in image understanding. It can be seen as a novel idea which is of high theoretic value and wide application.In this thesis, based on the theories and methodologies of information fusion technology, information acquisition, data representation, and object recognition in image understanding are studied by using pixel-level fusion, feature-level fusion and decision-level fusion respectively.This thesis includes the following contents:(1) The effectiveness of the new approach to image understanding using information fusion are investigated, by analyzing the state of the art of information acquisition, representation of data and knowledge, and object recognition in image understanding.(2) A new method for image data acquisition via image fusion is proposed. Based on the methods of pixel-level fusion and the analysis of the measures of fused image quality in existence, a new evaluation of fused image is proposed. A new method of colorizing night-vision images is also studied, and experiments demonstrate the effectiveness of this method.(3) D-S evidence theory which is often used in uncertainty processing is introduced to the feature-level fusion and its application to the representation of data and knowledge in image understanding are investigated. Key problems and theirs solutions in D-S evidence theory are discussed in details, based on which, new methods for the segmentation of fused image and edge detection are proposed. Experiments demonstrate the excellent performance of the fused algorithms.(4) Multi-classifier-based object recognition using decision-level fusion is studied. A multi-feature hierarchical recognition method based on D-S evidence theory is then proposed to recognize multi-class objects in image understanding. The relationships between D-S evidence theory and fuzzy set theory are investigated and applied in multibimetrics and traffic signs recognition.更多还原