An innovative image-processing model for rust detection using Perlin Noise to simulate oxide textures

被引：33
作者：
Gamarra Acosta, Margarita R. [1 ]
Velez Diaz, Juan C. [2 ]
Schettini Castro, Norelli [2 ]
机构：
[1] Univ Autonoma Caribe, Dept Elect & Telecommun Engn, Barranquilla, Colombia
[2] Univ Norte, Dept Elect & Elect Engn, Barranquilla, Colombia
来源：
CORROSION SCIENCE | 2014年 / 88卷
关键词：
Modeling studies; Rust; ATMOSPHERIC CORROSION; NEURAL-NETWORK; CLASSIFICATION; DAMAGE; SELECTION; SURFACE; ROC;
D O I：
10.1016/j.corsci.2014.07.027
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This article presents an image-processing model for detection of rust zones using digital images of metals. The input image, containing a wide range of possible rusted textures, is simulated with Perlin Noise, which allows simulating extreme corrosion conditions, without waiting for these conditions to occur. Probabilistic descriptors are determined by means of discriminant analysis using Fisher indexes. A Bayesian classifier is used to identify rusted regions. Additionally, performance tests under different noise conditions and texture variations, generated with Perlin Noise, are presented. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码：141 / 151
页数：11
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