CLASS OF ADAPTIVE MODEL- AND OBJECT-DRIVEN NONUNIFORM SAMPLING METHODS FOR 3-D INSPECTION.

被引：0

作者：

Malloch, Charles B. ^{[1
]}

Kwak, William I. ^{[1
]}

Gerhardt, Lester A. ^{[1
]}

机构：

[1] Rensselaer Polytechnic Inst, Troy,, NY, USA, Rensselaer Polytechnic Inst, Troy, NY, USA

来源：

Machine Vision and Applications | 1988年 / 1卷 / 02期

关键词：

3-D VISION - FAULT LIKELIHOOD METHOD - OBJECT-DRIVEN INSPECTION - OPTIMAL SAMPLING - UNKNOWN OBJECT SAMPLING;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Inspection of manufactured parts and assemblies often requires large amounts of information in the form of test probe point locations and large amounts of time to perform the inspection. By optimally locating the probe points it is possible to maintain inspection reliability using fewer test probes in a reduced amount of time. We have developed algorithms which use part model and manufacturing process information to generate an optimal probe-point location set for routine inspection in a model-based, open-loop mode. An alternate set of adaptive algorithms that sequentially generates probe-point locations in an object-based, closed-loop mode characterizes a fault when one is detected. Test results show that the algorithms perform favorably on a large class of surfaces.

引用

页码：97 / 114