Selection of thresholding methods for nondestructive testing applications

Sezgin, Mehmet; Sankur, B.

doi:10.1109/icip.2001.958231

Cited by 35 publications

(24 citation statements)

References 10 publications

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“…Thresholding can be regarded as partitioning pixels in the images into foreground object and background based on the comparison between the grey level of a pixel and a threshold. Because of their simplicity in theory and efficiency in computation speed, thresholding techniques have been widely employed in image data segmentation and a variety of algorithms have been proposed [26,27]. These algorithms automatically compute a threshold based on a given distribution or histogram of grey levels.…”

Section: Image Segmentationmentioning

confidence: 99%

Automated Defect Recognition as a Critical Element of a Three Dimensional X-ray Computed Tomography Imaging-Based Smart Non-Destructive Testing Technique in Additive Manufacturing of Near Net-Shape Parts

et al. 2017

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Abstract:In this paper, a state of the art automated defect recognition (ADR) system is presented that was developed specifically for Non-Destructive Testing (NDT) of powder metallurgy (PM) parts using three dimensional X-ray Computed Tomography (CT) imaging, towards enabling online quality assurance and enhanced integrity confidence. PM parts exhibit typical defects such as microscopic cracks, porosity, and voids, internal to components that without an effective detection system, limit the growth of industrial applications. Compared to typical testing methods (e.g., destructive such as metallography that is based on sampling, cutting, and polishing of parts), CT provides full coverage of defect detection. This paper establishes the importance and advantages of an automated NDT system for the PM industry applications with particular emphasis on image processing procedures for defect recognition. Moreover, the article describes how to establish a reference library based on real 3D X-ray CT images of net-shape parts. The paper follows the development of the ADR system from processing 2D image slices of a measured 3D X-ray image to processing the complete 3D X-ray image as a whole. The introduced technique is successfully integrated into an automated in-line quality control system highly sought by major industry sectors in Oil and Gas, Automotive, and Aerospace.

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Section: Image Segmentationmentioning

confidence: 99%

Automated Defect Recognition as a Critical Element of a Three Dimensional X-ray Computed Tomography Imaging-Based Smart Non-Destructive Testing Technique in Additive Manufacturing of Near Net-Shape Parts

et al. 2017

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“…Its applications include several classics such as document image analysis, whose goal is to extract printed characters (Abak et al, 1997;Kamel & Zhao, 1993) logos, graphical content, or musical scores; also it is used for map processing which aims to locate lines, legends, and characters (Trier & Jain, 1995). It is also used for scene processing, aiming for object detection and marking (Bhanu, 1986); Similarly, it has been employed to quality inspection for materials (Sezgin & Sankur, 2001;Sezgin & Tasaltin, 2000), discarding defective parts. Thresholding selection techniques can be classified into two categories: bi-level and multilevel.…”

Section: Introductionmentioning

confidence: 99%

Segmentation with Learning Automata

Cuevas¹,

Zaldívar²,

Pérez‐Cisneros³

2011

Image Segmentation

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“…Several binarization algorithms have been proposed in the literature [4][5][6][7][8][9][10][11][12][13]. However, it is difficult to select the optimal algorithm.…”

Section: Introductionmentioning

confidence: 99%

Optimal selection of binarization techniques for the processing of ancient palm leaf manuscripts

Chamchong

Fung

2010

2010 IEEE International Conference on Systems, Man and Cybernetics

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Abstract-Ancient palm leaf manuscripts in Thailand have been preserved by many organizations for the protection and retrieval of traditional knowledge. With advanced computer technology, digitized media is now commonly used to record these documents. One objective of such work is to develop an efficient image processing system that could be used to retrieve knowledge and information automatically from these manuscripts.Binarization is an important stage during preprocessing of the manuscripts for subsequent extraction of text and characters.The output is then used for further processes such as character recognition and knowledge extraction. However, there is no single binarization technique that is suitable for all documents. This study aims to improve the binarization process intelligently by applying machine learning techniques to classify the optimal selection of binarization techniques of palm leaf manuscripts.Experiments results are reported and this technique could be applied for automatic selection of binarization techniques in other image processing problems.

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Selection of thresholding methods for nondestructive testing applications

Cited by 35 publications

References 10 publications

Automated Defect Recognition as a Critical Element of a Three Dimensional X-ray Computed Tomography Imaging-Based Smart Non-Destructive Testing Technique in Additive Manufacturing of Near Net-Shape Parts

Automated Defect Recognition as a Critical Element of a Three Dimensional X-ray Computed Tomography Imaging-Based Smart Non-Destructive Testing Technique in Additive Manufacturing of Near Net-Shape Parts

Segmentation with Learning Automata

Optimal selection of binarization techniques for the processing of ancient palm leaf manuscripts

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