A HIGHER-ORDER NEURAL NETWORK FOR DISTORTION INVARIANT PATTERN-RECOGNITION

被引：11

作者：

KANAOKA, T ^{[1
]}

CHELLAPPA, R ^{[1
]}

YOSHITAKA, M ^{[1
]}

TOMITA, S ^{[1
]}

机构：

[1] UNIV MARYLAND,CTR AUTOMAT RES,COLL PK,MD 20742

来源：

PATTERN RECOGNITION LETTERS | 1992年 / 13卷 / 12期

关键词：

HIGHER-ORDER NEURAL NETWORK; DISTORTION INVARIANT; PATTERN RECOGNITION; LEARNING;

D O I：

10.1016/0167-8655(92)90082-B

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recently, it is shown that a single layer, higher-order neural network is effective for scale, rotation and shift invariance and in the training process it requires only one example for one category and a very small number of iterations. However, there are problems that scale invariance doesn't hold precisely and it is not so effective for distortion of unknown patterns. In this paper we present an idea to realize the scale invariance precisely and suggest a method that is available to distorted patterns. The experimental results are presented to show the feasibility of our approach.

引用

页码：837 / 841

页数：5

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