Drawing image understanding framework using state transition models

S. Satoh, M. Sakauchi
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引用次数: 8

Abstract

A flexible drawing understanding system with state transition models is proposed. The drawing processor AI-Mudams (written in C) is used as the token extractor in the embodiment discussed. Given drawing images are converted efficiently to suitable geometrical primitives, such as contour vectors, core vectors, dots loops, or, in some cases, primitives with semantics (road line, or house etc.). The understanding system kernel is implemented in Prolog, and the geometrical evaluator is also prepared in C for checking basic geometrical situations, including shape, geometrical relations, and allocations. This understanding kernel accepts the individual state transition rules corresponding to individual drawing images and recognition targets and realizes understanding in the form of bottom-up and top-down state transition. Experiments on different types of drawings reveal that the framework is flexible and effective for various kinds of drawing image.<>
使用状态转换模型绘制图像理解框架
提出了一种具有状态转换模型的柔性绘图理解系统。绘图处理器AI-Mudams(用C语言编写)用作所讨论的实施例中的令牌提取器。给定的绘图图像被有效地转换为合适的几何原语,例如轮廓向量,核心向量,点环,或者在某些情况下,具有语义的原语(道路线或房屋等)。在Prolog中实现了理解系统内核,并在C语言中编写了几何评估器,用于检查基本的几何情况,包括形状、几何关系和分配。该理解内核接受对应于单个绘图图像和识别目标的单个状态转换规则,并以自底向上和自顶向下的状态转换形式实现理解。对不同类型的绘图图像进行了实验,结果表明该框架对不同类型的绘图图像具有灵活性和有效性
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