Department of Artificial Intelligence, Martin Marietta Laboratories, Baltimore, MD 21227.
IEEE Trans Pattern Anal Mach Intell. 1986 May;8(5):604-18. doi: 10.1109/tpami.1986.4767835.
This paper presents a powerful image understanding system that utilizes a semantic-syntactic (or attributed-synibolic) representation scheme in the form of attributed relational graphs (ARG's) for comprehending the global information contents of images. Nodes in the ARG represent the global image features, while the relations between those features are represented by attributed branches between their corresponding nodes. The extraction of ARG representation from images is achieved by a multilayer graph transducer scheme. This scheme is basically a rule-based system that uses a combination of model-driven and data-driven concepts in performing a hierarchical symbolic mapping of the image information content from the spatial-domain representation into a global representation. Further analysis and inter-pretation of the imagery data is performed on the extracted ARG representation. A distance measure between images is defined in terms of the distance between their respective ARG representations. The distance between two ARG's and the inexact matching of their respective components are calculated by an efficient dynamic programming technique. The system handles noise, distortion, and ambiguity in real-world images by two means, namely, through modeling and embedding them into the transducer's mapping rules, as well as through the appropriate cost of error-transformation for the inexact matching of the ARG image representation. Two illustrative experiments are presented to demonstrate some capabilities of the proposed system. Experiment I deals with locating objects in multiobject scenes, while Experiment II is concerned with target detection in SAR images.
本文提出了一种强大的图像理解系统，该系统使用语义句法（或属性符号）表示方案，以属性关系图（ARG）的形式表示图像的全局信息内容。ARG 中的节点表示全局图像特征，而这些特征之间的关系则由相应节点之间的属性分支表示。通过多层图转换器方案从图像中提取 ARG 表示。该方案基本上是一个基于规则的系统，它结合了模型驱动和数据驱动的概念，将图像信息内容从空间域表示分层符号映射到全局表示。对提取的 ARG 表示进行进一步的分析和解释。根据各自的 ARG 表示之间的距离来定义图像之间的距离。通过有效的动态规划技术计算两个 ARG 之间的距离以及它们各自组件的不精确匹配。该系统通过两种方式处理实际图像中的噪声、失真和模糊，即通过建模并将其嵌入到转换器的映射规则中，以及通过对 ARG 图像表示的不精确匹配进行适当的错误转换代价。进行了两个说明性实验，以展示所提出系统的一些功能。实验 I 涉及在多目标场景中定位对象，而实验 II 则涉及 SAR 图像中的目标检测。
