.png){kind=icon}
返回
Grain boundary detection in microstructure images using computational intelligence
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compind.2005.05.012.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Two computational intelligence approaches, a fuzzy logic algorithm and a neural network (NN) algorithm, for grain boundary detection in images of superalloy steel microstructure during sintering are presented in this paper, The images are obtained from an optical microscope and are quite noisy, which adversely affects the performance of common image processing tools. The only known way to accurately determine the grain boundaries is digitizing by hand. This is a very time-consuming process, causes operator fatigue, and it is prone to human errors and inconsistency, An automated system is therefore needed to complete as much work as possible and we consider a fuzzy approach and a neural approach, Both methods performed better than the widely available standard image processing tools with the neural approach superior on images similar to those trained while the fuzzy approach showed more tolerance of disparate images. (c) 2005 Elsevier B.V. All rights reserved.
Keyword:
grain boundary detection
fuzzy logic
neural networks
image processing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.1
论文数:
2.9K
被引数:
1.1W
机构
暂无机构信息
引用论文
分享收藏Nanocrystalline silicon films as multifunctional material for optoelectronic and photovoltaic applications纳米硅薄膜作为光电和光伏应用的多功能材料
PREAI
分享收藏Heat transfer of brake pad used in the autos after friction and examination of thermal tension analysis汽车刹车片摩擦后的传热及热张力分析.png)
Mechanics
OAAI
MechanicsIF0
OAAI分享收藏 没有更多内容
没有更多内容