Application of multi-layer perceptron neural networks to vision problems

被引：0
|
作者：
A. Khotanzad
C. Chung
机构：
[1] Southern Methodist University,Electrical Engineering Department
来源：
Neural Computing & Applications | 1998年 / 7卷
关键词：
3D object recognition; 3D pose estimation; Combination of MLP classifiers; Handwritten digit recognition; Multi-layer perceptron; Pseudo-Zernike moments; Shadow code features; Vision problems;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper discusses the application of a class of feed-forward Artificial Neural Networks (ANNs) known as Multi-Layer Perceptrons(MLPs) to two vision problems: recognition and pose estimation of 3D objects from a single 2D perspective view; and handwritten digit recognition. In both cases, a multi-MLP classification scheme is developed that combines the decisions of several classifiers. These classifiers operate on the same feature set for the 3D recognition problem whereas different feature types are used for the handwritten digit recognition. The backpropagationlearning rule is used to train the MLPs. Application of the MLP architecture to other vision problems is also briefly discussed.
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页码：249 / 259
页数：10
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