基于视觉感知的图像理解方法研究
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摘要
图像理解是一门从图像中自动获取信息的科学,其主要的技术包括图像分割,图像成份的建模,图像分析及推理。该领域的研究包括了开发图像(户内/室外,人,脸,树,大楼,等等)目标特征的自动提取方法以及实现图像解释的有关方法,这对诸如图片的数据库管理或自动建立是非常有益的。图像理解是人工智能中涉及基于输入图像解释来做出决策或完成一些控制系统的主要环节,其在医学和国防等领域中有着广阔的用途。
本文在总结近几年研究成果的基础上,给出了一个图像理解的基本框架结构,并对各部分的具体功能及实现进行详细的分析,文中对图像理解的研究主要分为六个部分。具体是:第一部分着重探讨图像理解方法,在这一部分中首先对当前有关的视觉感知理论进行介绍。简要地回顾低级视觉过程对目标的识别的作用,如从阴影或纹理中感知结构等,其主要目的是集中讨论图像理解方法。并对有关理论的生物学似然性进行了评定。这些来自心理的研究证据对不同的计算模型有着不同的结论,它们或支持于或矛盾于计算模型。除此之外还给出了最近有关生理学的研究成果,这些研究结果表明大脑中的3D信息处理是一个层次型的结构模式。第二部分主要涉及图像分割技术的实现,其主要用来检测目标的边界。重点给出了边缘检测后的再分割算法以及对分割后结果的评价分析方法;创新地提出了基于视觉感知的分割思想。第三个部分主要研究形状表示和分析的各种技术。形状的表示方法或基于边界(边的边之间的关系描述)或基于区域(利用边界检测算法生成的闭合区域)。这一部分主要给出了边界形状分析方法的回顾及链码表示技术。第四部分集中于理解技术与有关知识表示技术的集成以及感知推理方法的研究。第五部分讨论有关图像理解中基
Image Understanding is the subject of automated extraction of information from images. The essential technologies of the science include image segmentation, image component modeling, image analysis and reasoning. The research in this field involves development of automated ways to detect various characteristics of objects in a picture (indoor/outdoor, people, faces, trees, buildings, etc.) that may be useful in future applications such as database management or automatic creation of photo albums. Image understanding is a major step towards the development of Artificial Intelligence, which involves systems that will make certain decisions or perform some activities based on their interpretation of an input image. Image understanding also has widespread applications in medicine and defense.By summing up the research result of the rescent years , this dissertation presents the fundamental frame structure of image understanding , and analyzes the function of every section with to achieve. This thesis consists of six major components. The first addresses the method of image understanding. In this component current theories of the visual perception are introduced. Starting with a brief overview of low-level visual processes, which contribute to the recognition of objects, such as the perception of structure from shading or texture, this component mainly concentrates on the method of image understanding. The described theories are assessed with respect to their biological plausibility. Evidence from psychological studies is given, which either supports or contradicts the different computational models. Besides this, recent results from physiological studies reflect the hierarchical processing of 3D information in the primate brain. The second component deals with the implementation of segmentation technique, which is applied in order to detect the boundaries of the objects that define the scene. This component mainly gives out the algorithm of resegmentation , moreover the evaluation of the performance of the result is carried out by analyzing the information associated with the singular edge points. The third component surveys the various techniques for shape representation and analysis. The representation can be either edge based (descriptions of edges and the relationship between edges) or region based (you may have to fiddle with your edge detection algorithm to produce closed regions). Specifically, a review of boundary shape analysis methods and chain-code representation techniques is presented. The fourth component focuses on integrating advanced knowledge representation technology with image understanding technology and perceptual reasoning techniques. The fifth component is concerned with the use of feature-based match similarity measures and feature match algorithms
in image understanding. The component addresses the object recognition and reasoning by using the information resulting from the application of the segmentation algorithm. The recognition stage consists of matching the features derived from the scene regions, while the reasoning is addressed using uncertain reasoning. The last gives a simulation tracking system is designed based on runway recognition algorithm.In this dissertation ,lots of research work has been done around some key techniques of IU (Image Understanding). The presented study is the current research focus of image understanding and image processing. Thus its research has both the theory and the application value. The contribution of this thesis was that we presented a realize method of image understanding based on visual perception, and this study lay foundation for further research.
本文在总结近几年研究成果的基础上,给出了一个图像理解的基本框架结构,并对各部分的具体功能及实现进行详细的分析,文中对图像理解的研究主要分为六个部分。具体是:第一部分着重探讨图像理解方法,在这一部分中首先对当前有关的视觉感知理论进行介绍。简要地回顾低级视觉过程对目标的识别的作用,如从阴影或纹理中感知结构等,其主要目的是集中讨论图像理解方法。并对有关理论的生物学似然性进行了评定。这些来自心理的研究证据对不同的计算模型有着不同的结论,它们或支持于或矛盾于计算模型。除此之外还给出了最近有关生理学的研究成果,这些研究结果表明大脑中的3D信息处理是一个层次型的结构模式。第二部分主要涉及图像分割技术的实现,其主要用来检测目标的边界。重点给出了边缘检测后的再分割算法以及对分割后结果的评价分析方法;创新地提出了基于视觉感知的分割思想。第三个部分主要研究形状表示和分析的各种技术。形状的表示方法或基于边界(边的边之间的关系描述)或基于区域(利用边界检测算法生成的闭合区域)。这一部分主要给出了边界形状分析方法的回顾及链码表示技术。第四部分集中于理解技术与有关知识表示技术的集成以及感知推理方法的研究。第五部分讨论有关图像理解中基
Image Understanding is the subject of automated extraction of information from images. The essential technologies of the science include image segmentation, image component modeling, image analysis and reasoning. The research in this field involves development of automated ways to detect various characteristics of objects in a picture (indoor/outdoor, people, faces, trees, buildings, etc.) that may be useful in future applications such as database management or automatic creation of photo albums. Image understanding is a major step towards the development of Artificial Intelligence, which involves systems that will make certain decisions or perform some activities based on their interpretation of an input image. Image understanding also has widespread applications in medicine and defense.By summing up the research result of the rescent years , this dissertation presents the fundamental frame structure of image understanding , and analyzes the function of every section with to achieve. This thesis consists of six major components. The first addresses the method of image understanding. In this component current theories of the visual perception are introduced. Starting with a brief overview of low-level visual processes, which contribute to the recognition of objects, such as the perception of structure from shading or texture, this component mainly concentrates on the method of image understanding. The described theories are assessed with respect to their biological plausibility. Evidence from psychological studies is given, which either supports or contradicts the different computational models. Besides this, recent results from physiological studies reflect the hierarchical processing of 3D information in the primate brain. The second component deals with the implementation of segmentation technique, which is applied in order to detect the boundaries of the objects that define the scene. This component mainly gives out the algorithm of resegmentation , moreover the evaluation of the performance of the result is carried out by analyzing the information associated with the singular edge points. The third component surveys the various techniques for shape representation and analysis. The representation can be either edge based (descriptions of edges and the relationship between edges) or region based (you may have to fiddle with your edge detection algorithm to produce closed regions). Specifically, a review of boundary shape analysis methods and chain-code representation techniques is presented. The fourth component focuses on integrating advanced knowledge representation technology with image understanding technology and perceptual reasoning techniques. The fifth component is concerned with the use of feature-based match similarity measures and feature match algorithms
in image understanding. The component addresses the object recognition and reasoning by using the information resulting from the application of the segmentation algorithm. The recognition stage consists of matching the features derived from the scene regions, while the reasoning is addressed using uncertain reasoning. The last gives a simulation tracking system is designed based on runway recognition algorithm.In this dissertation ,lots of research work has been done around some key techniques of IU (Image Understanding). The presented study is the current research focus of image understanding and image processing. Thus its research has both the theory and the application value. The contribution of this thesis was that we presented a realize method of image understanding based on visual perception, and this study lay foundation for further research.
引文
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