The Recognition Method of Objects Based on Moment Invariant and BP Neural Network

SUN Hong-hui; WANG Hong-xia; TIAN Tao

Chinese

您当前的位置：

首页 >

文章列表页 >

The Recognition Method of Objects Based on Moment Invariant and BP Neural Network

更新时间：2026-09-04

- The Recognition Method of Objects Based on Moment Invariant and BP Neural Network
- Vol. 28, Issue 3, Pages: 63-65(2011)
- 作者机构：
  
  第二炮兵工程学院,陕西,西安,710025
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2011
- 稿件说明：
移动端阅览
孙红辉, 王红霞, 田涛. 一种基于不变矩和BP网络的目标识别方法[J]. 微电子学与计算机, 2011, 28(3): 63-65,69.

SUN Hong-hui, WANG Hong-xia, TIAN Tao. The Recognition Method of Objects Based on Moment Invariant and BP Neural Network[J]. 2011, 28(3): 63-65.
孙红辉, 王红霞, 田涛. 一种基于不变矩和BP网络的目标识别方法[J]. 微电子学与计算机, 2011, 28(3): 63-65,69. DOI：

SUN Hong-hui, WANG Hong-xia, TIAN Tao. The Recognition Method of Objects Based on Moment Invariant and BP Neural Network[J]. 2011, 28(3): 63-65. DOI：

摘要

为了有效地提高旋转、尺度变化目标的识别率

首先提取目标图像的不变矩

以此作为目标识别的特征向量

然后利用将附加动量项与自适应学习速率相结合的改进BP算法实现对目标的分类识别.字符图像仿真实验表明

这种针对旋转、尺度变化目标的识别方法是有效的

可行的.

Abstract

In order to improve the recognition rate of rotation and scale changed objects

firstly the moment invariant of object images was extracted

which was taken as the characteristic vector

then improved BP algorithm that connects additional momentum with self-adaptive learning rate was used to classify and recognize the objects. The emulation experimental results of character images show the object recognition method of rotation and scale changed images is effective and feasible.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Lightweight countermeasures against Spectre attacks on the branch prediction unit

Aerial vehicle detection algorithm based on multi-scale fusion and small-object enhancement

A high-efficiency charge pump for ultra-low voltage boost systems

Design of ultra-low power real-time clock circuits

Implementation and verification of a dual-chain power gating strategy for low power design

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰