Multi-Resolution and Noise-Resistant Surface Defect Detection Approach Using New Version of Local Binary Patterns

Multi-Resolution and Noise-Resistant Surface Defect Detection Approach Using New Version of Local Binary Patterns

Blog Article

Visual quality inspection systems play an important role in many industrial applications.In Theoretical aspects of the research to the capacity of the security system from the symbolic approaches this respect, surface defect detection is one of the problems that have received much attention by image processing scientists.Until now, different methods have been proposed based on texture analysis.An operation that provides discriminate features for texture analysis is local binary patterns (LBP).LBP was first introduced for gray-level images that makes it useless for colorful samples.

Sensitivity to noise is another limitation of LBP.In this article, a new noise-resistant and multi-resolution version of LBP is used that extracts color and texture features jointly.Then, a robust algorithm is proposed for detecting abnormalities in surfaces.It includes two steps.First, new version of LBP Acteoside and ursolic acid synergistically protects H2O2-induced neurotrosis by regulation of AKT/mTOR signalling: from network pharmacology to experimental validation is applied on full defect-less surface images, and the basic feature vector is calculated.

Then, by image windowing and computing the non-similarity amount between windows and basic vector, a threshold is computed.In test phase, defect parts are detected on test samples using the tuned threshold.High detection rate, low computational complexity, low noise sensitivity, and rotation invariant are some advantages of our proposed approach.