A framework for statistical 3-D object recognition

被引：0
|
作者：
Paulus, D [1 ]
Hornegger, J [1 ]
Niemann, H [1 ]
机构：
[1] Univ Erlangen Nurnberg, Lehrstuhl Mustererkennung Informat 5, D-91058 Erlangen, Germany
来源：
PATTERN RECOGNITION LETTERS | 1997年 / 18卷 / 11-13期
关键词：
object-oriented programming; statistical object recognition;
D O I：
10.1016/S0167-8655(97)00139-6
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this contribution we describe an object-oriented software architecture for image segmentation, 3-D pose estimation as well as Bayesian object recognition: models are represented by densities, model generation corresponds to parameter estimation tasks, and the identification applies the Bayesian decision rule. We show results of 3-D object recognition experiments based on the observation of 2-D points or lines. (C) 1997 Elsevier Science B.V.
引用
收藏
页码：1153 / 1157
页数：5
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