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

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 Bayes-ian classifier is used to identify rusted regions. Additionally, performance tests under different noise conditions and texture variations, generated with Perlin Noise, are presented.