Tool combination for the description of steel surface image and defect classification

From this factual result, local strain alone (without considering the coordination effect) is a desirable experimental structural descriptor of surface defectiveness, able

In this paper, weexplore pre-de?ned image ?lters and work on a procedureto extract a discriminant image feature, while realizing thebest trade-off between the improved recognition

In the paper, we are presenting, a multiresolution approach, based on the recent image encoding method, namely the Binarized Statistical Image features [17–19], is used to

The defect classification is carried out by means of two classifiers, namely the nearest neighbors and the support vector machine.. The method assessment is based on testing

KEYWORDS: Industrial vision; the K- Nearest Neighbour classifier; classification of surface defects; Histogram of Oriented Gradients (HOG); Principal Component Analysis

In this paper, four region-based shape descriptors well re- ported in the literature are used to characterize weld defect types of crack, lack of penetration, porosity and

The contributions of this work are three-fold: (1) we formulate the image-based localization problem as a re- gression on an image graph with images as nodes and edges

We found that in patients with full screening of the Ghent criteria, but dural ectasia (as this feature is not systematically looked for in clinical practice) 11 the best predictor