用网络映射替代规则生成，实现抗干扰的复杂图模式识别
A New Graph Grammar Formalism for Robust Syntactic Pattern Recognition
- 以网络同构代替传统生成规则，直接描述图结构语法
- 可处理50-1000符号的复杂模式，支持几何变异与缺失补全
- 适合需要高鲁棒性的图像/结构识别任务，如医学影像分析
本文提出一种表示递归图结构模式语法的形式化方法。该方法不采用传统图语法中的产生式规则，而是以更直接、声明式的方式表达语法结构。语法与模式均以网络形式表示，解析被视作从模式到语法的同态构造过程。该语法能有效表示多维的迭代、分层及嵌套递归结构，支持高度并行的解析方式，将特征检测、分割、解析、缺失符号填补以及自上而下/自下而上的推理统一在一个过程中，充分发挥各环节间的协同效应。论文侧重理论探讨，也展示了若干实例，说明其在含50-1000个符号的复杂递归模式中具备容错能力，可应对几何关系变化、模糊符号、重叠符号、杂乱背景及擦除区域等挑战。
原文摘要 · Abstract (English)
I introduce a formalism for representing the syntax of recursively structured graph-like patterns. It does not use production rules, like a conventional graph grammar, but represents the syntactic structure in a more direct and declarative way. The grammar and the pattern are both represented as networks, and parsing is seen as the construction of a homomorphism from the pattern to the grammar. The grammars can represent iterative, hierarchical and nested recursive structure in more than one dimension. This supports a highly parallel style of parsing, in which all aspects of pattern recognition (feature detection, segmentation, parsing, filling in missing symbols, top-down and bottom-up inference) are integrated into a single process, to exploit the synergy between them. The emphasis of this paper is on underlying theoretical issues, but I also give some example runs to illustrate the error-tolerant parsing of complex recursively structured patterns of 50-1000 symbols, involving variability in geometric relationships, blurry and indistinct symbols, overlapping symbols, cluttered images, and erased patches.
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