Probabilistic image models for object recognition and pose estimation

被引：0

作者：

Hornegger, J ^{[1
]}

Niemann, H ^{[1
]}

机构：

[1] Univ Erlangen Nurnberg, Inst Informat, D-91058 Erlangen, Germany

来源：

STATE-OF-THE-ART IN CONTENT-BASED IMAGE AND VIDEO RETRIEVAL | 2001年 / 22卷

关键词：

probabilistic object models; statistical object recognition;

D O I：

暂无

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 chapter we describe ongoing research that seeks to solve the object recognition and localization problem using a probabilistic framework. Computing statistical object models and calculating pose parameters are considered as nonlinear estimation problems. Recognition is done according to Bayes rule. We give an overview of a wide range of statistical models including a new modeling scheme for intensity images. This novel model avoids feature segmentation and it is discussed in detail including the description of the mathematical framework as well as the experimental evaluation.

引用

页码：125 / 142

页数：18

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