Can machines interpret line drawings?
Can machines interpret line drawings?
复制标题
DOI：
10.2312/sbm/sbm04/107-116
复制
发表时间：
2004-08
期刊：
影响因子：
--
通讯作者：
Peter Ashley Clifford Varley;Hiromasa Suzuki;Ralph Robert Martin
中科院分区：
文献类型：
--
作者：
Peter Ashley Clifford Varley;Hiromasa Suzuki;Ralph Robert Martin
如果计算机能够解释最初的概念图，工程设计将变得更容易。我们概述了一种自动解释多面体线条图的方法，并总结了已经可能发生的事情，在不久的将来可以预期的发展，以及哪些领域仍然存在问题。我们通过特别参考我们自己的系统ribald来说明这一点，总结了已发表的最新技术状态，并讨论了最近未发表的对ribald的改进。一般来说，成功的解释取决于两个因素：线的数量，以及图形是否可以被归类为特殊形状类别的成员(例如，拉伸或法线)。最先进的技术实现了对任何大小的挤压和20-30条线的大多数法线的正确解释，但对于未分类的对象来说，仅绘制10-20条线可能会有问题。尽管取得了成功，但在某些情况下，所需的解释对人类来说是显而易见的，但不能通过当前可用的算法来确定。我们列举了我们的成功和人类技能无法复制的典型案例。
Engineering design would be easier if a computer could interpret initial concept drawings. We outline an approach for automated interpretation of line drawings of polyhedra, and summarise what is already possible, what developments can be expected in the near future, and which areas remain problematic. We illustrate this with particular reference to our own system, RIBALD, summarising the published state of the art, and discussing recent unpublished improvements to RIBALD. In general, successful interpretation depends on two factors: the number of lines, and whether or not the drawing can be classified as a member of special shape class (e.g. an extrusion or normalon). The state-of-the-art achieves correct interpretation of extrusions of any size and most normalons of 20—30 lines, but drawings of only 10—20 lines can be problematic for unclassified objects.Despite successes, there are caseswhere the desired interpretation is obvious to a human but cannot be determined by currently-available algorithms. We give examples both of our successes and of typical caseswhere human skill cannot be replicated.