&lt;title&gt;Surface defect detection with histogram-based texture features&lt;/title&gt;

In any industry, quality control is an essential and inevitable part of process. Defect detection in steel plates, is one of the most important quality control steps in steel process. Image processing is a dominant technique to recognize defect in steel plates. In this paper a fast and precise solution for detection of this type of defects by using 2D wavelet is introduced. The result show considerable improvement and Precision and speed of this suggested approach is compared to the previous methods.

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“…This Wavelet due to begin optimal in both frequency and local domain, can utilize the benefits of signal processing in both domains [11]- [12].…”

Section: Texture Analysis and Feature Extraction Withmentioning

confidence: 99%

Application of two dimensional wavelet for defect detection in steel process

Sadeghi

saraie

Mahdeian

2011

The 2nd International Conference on Control, Instrumentation and Automation

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“…most work focuses very specifically on the relevant problem domain. Among the statistical approaches, gray level statistics (Iivarinen, 2000;Chetverikov, 2000;Chetverikov and Hanbury, 2002), co-occurrence matrices (Rautkorpi and Iivarinen, 2005) and local binary patterns (Niskanen et al, 2001;Mäenpää et al, 2003;Tajeripour et al, 2008) are the most frequently used ones. Unser and Ade (1984) and Monadjemi et al (2004) make use of an Eigenfilter approach.…”

Section: Automatic Visual Inspectionmentioning

confidence: 99%

Enhanced anomaly detection in wire ropes by combining structure and appearance

Wacker

Denzler

2013

Pattern Recognition Letters

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Automatic visual surface inspection is a challenging task, which has become important for quality assurance in the last years. Wire rope inspection is a special problem within this field. Usually, the huge and heavy ropes cannot be detached. Thus, an inspection of the ropes must be conducted, while the ropes are in use. The rope surface exhibits various appearance characteristics so that the existing, purely appearance-based approaches tend to fail.We explicitly integrate information about the object geometry, which we obtain by aligning a sequence of 2d rope images with a perfectly regular 3d model of the rope. The rendering equation is used to link object geometry to the observed rope appearance. Based on the connection between geometry and surface appearance we build a probabilistic appearance model which serves as representation for normal surface variations. A robust localization of rope surface defects is achieved by means of anomaly detection. The presented approach has no need for knowledge about the illumination setting or the reflectance properties of the material.An evaluation on real-world data from ropeways leads to an accuracy comparable to that of a human expert. With an accuracy of 95% and a false-alarm-rate of 1.5% the approach outperforms all other existing approaches.

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“…As an alternative approach, higher order statistical moments can be derived from a twodimensional histogram [18], where the relationship between gray level values and their spatial distribution allows a description of structural features. Such a technique is known as the gray level co-occurrence matrix (GLCM) [16].…”

Section: Two-dimensional Histogram (Glcm) Analysismentioning

confidence: 99%

Assessment and visualisation of machine tool wear using computer vision

Kerr

Pengilley

Garwood

2005

Int J Adv Manuf Technol

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Tool wear monitoring is an integral part of modern CNC machine control. Cutting tools must be periodically checked for possible or actual premature failures, and it is necessary to record the cutting history for a tool's full life of utilisation. This means that an on-line monitoring system would be of great benefit to overall process control in manufacturing systems. Computer vision has already shown promise as a candidate technology for this task. In this paper, we describe the use of digital image processing techniques in the analysis of images of worn cutting tools in order to assess their degree of wear and thus remaining useful life. It is shown that a processing strategy using a variety of image texture measures allows for effective visualisation and assessment of tool wear, and indicates good correlation with the expected wear characteristics.

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<title>Surface defect detection with histogram-based texture features</title>

Cited by 35 publications

References 9 publications

Application of two dimensional wavelet for defect detection in steel process

Application of two dimensional wavelet for defect detection in steel process

Enhanced anomaly detection in wire ropes by combining structure and appearance

Assessment and visualisation of machine tool wear using computer vision

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