标题: 多边形骨架表达法及其在三维工件之分类应用
A Skeleton Approach to Modelling 2D Polygons and its Application to the Classification of 3D Parts
作者: 涂原彰
Tu, Yuan Chang
巫木诚, 庄荣宏
Muh-Cherng Wu, Jung-Hong Chuang
工业工程与管理学系
关键字: 修正简化线骨架;初步分类;环状码;细部分类;逆传递类神经网路;revised simplified skeleton;coarse calassification;ring code;refining classification;back-propagation neural network
公开日期: 1995
摘要: 本论文旨在提出一个以工件整体外形资讯为分类依据的工件分类方法
。本论文的第一个部分,提出多边形的修正简化线骨架表达法,做为工件
外形资讯表达的基础。在此表达法中,不仅克服简化线骨架表达法(Wu
and Chen 1992)中,可能出现外形相异之多边形却具相同骨架资讯的缺点
,同时也进一步提升了骨架和多边形间的拓朴类似性,免除了三维方形工
件分类研究(Wu and Jen 1994)中,多边形须先转换为正交而导致资讯失
真的困扰。 第二部分则提出两阶段的工件分类方法。首先,将一个三
维工件以其三个二维投影视图之近似多边形来表示,然后依其外形轮廓之
方位转换为代表性环状码,并据此来进行外形初步分类。第二个阶段则以
修正简化线骨架为基础,将代表此一工件的三个多边形以三个树状结构之
修正简化线骨架来表示,再将此骨架转为逆传递类神经网路之输入向量,
并藉此网路进一步将初步分类的结果中,整体外形差异较大的工件细分出
来。最后综合两阶段分类的结果,依工件相似程度形成工件族。
This thesis presents techniques for classifying workpieces
using the revised simplified skeleton. The proposed revised
simplified skeleton extends the firepropagation rules of the
simplified skeleton (Wu and Chen 1992). The revised simplified
skeleton, as a global shape descriptor, not only captures the
globalshape features, including the skeletal feature and acute
shapecorners, but alsogreatly simplifies skeleton's complexity.
As a result, the revised simplifiedskeleton is an ideal shape
descriptor for applications such as workpiece class-ification.
The proposed classification technique is a two-phase procedure.
In the first phase, workpieces are grouped coarsely according to
the contours oftheir projections. The coarse classification is
performed by similarity matchingon the representative ring code
derived from each contour. In the second phase,workpieces within
the same group are classified in a refinement fasion accordingto
the revised simplified skeletons of their contours. In the
refining classification, the derived revised simplified skeleton
is converted first to a three structureand then to a vector
representation. The vector representation will be read asan
input by a back-propagation neural network.Based on the results
of the neuralnetwork, workpieces within the same groupare
further classified into families ina three-level hierarchical
structure.
URI: http://140.113.39.130/cdrfb3/record/nctu/#NT840030026
http://hdl.handle.net/11536/60042
显示于类别:Thesis