Automated Quality Assessment of Constructed Surfaces through Intelligent Image Processing

AbdelRazig, Yassir; Chang, Luh‐Maan; Skibniewski, Mirosław J.

doi:10.22260/isarc1999/0107

Cited by 6 publications

(7 citation statements)

References 3 publications

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“…Also, the number of clusters should be no more than the number of samples. In general, the k-means algorithm has the following steps (De La Blanca, 1992;AbdelRazig et al, 1999;Chen, 2001):…”

Section: K-means Algorithmmentioning

confidence: 99%

“…Image thresholding can also divide an image into N segments. In this case, N − 1 threshold values are needed, and Equation (2) can be extended as Equation (3) (Russ, 1995;AbdelRazig et al, 1999):…”

Section: Image Thresholdingmentioning

confidence: 99%

“…E-mail: changlm@ecn.purdue.edu. industries (Croall and Mason, 1992). In the construction area, image processing has been used for defect recognition on steel bridge painting and underground sewer systems (Abraham et al, 1997;AbdelRazig et al, 1999;Chen and Chang, 2000).…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Steel Bridge Coating Assessment Using Neuro‐Fuzzy Recognition Approach

Chen

Chang²

2002

Computer aided Civil Eng

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Recently, digital image recognition has been applied to steel bridge coating assessment. However, nonuniform illumination is always a problem and affects the accuracy of processed results. In order to resolve the recognition problems arising from non-uniformly illuminated images, the neuro-fuzzy recognition approach (NFRA) is proposed. NFRA segments a grayscale image into three areas in accordance with the illumination values of the pixels in the image. The three average illumination values of the three areas are then sent to a pre-trained neural network to generate three corresponding threshold values. The fuzzy adjustment system adjusts the gray level values of the pixels along the boundaries between areas. Finally, image thresholding is applied to get a binary image containing only the object pixels and the background pixels. Besides the background and framework of NFRA, this paper also introduces a random sampling plan to help select positions for image taking during steel bridge coating inspections.

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Section: K-means Algorithmmentioning

confidence: 99%

Section: Image Thresholdingmentioning

confidence: 99%

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Intelligent Steel Bridge Coating Assessment Using Neuro‐Fuzzy Recognition Approach

Chen

Chang²

2002

Computer aided Civil Eng

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“…As computerized technologies were widely utilized, digital image processing was also prevalently adopted in many industries (Abraham et al 1997;Croall and Mason 1992;AbdelRazig et al 1999;Chen and Chang 2000). In the construction area, image processing has been used for defect recognition on steel bridge painting and underground sewer systems.…”

Section: Introductionmentioning

confidence: 99%

Computerized Bridge Coating Defects Assessment Using Neuro-Fuzzy Thresholding Method

Chen¹,

Chang²

2002

Proceedings of the 19th International Symposium on Automation and Robotics in Construction (ISARC)

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Digital image processing has been prevalently adopted in different areas. In the construction field, image processing has been used for defect detection on steel bridge painting and underground sewer systems. However, non-uniformly illuminated images always cause recognition problems and affect the accuracy. In order to resolve these problems, the neuro-fuzzy recognition approach (NFRA) was proposed. The NFRA segments an image into three areas based on illumination and conducts area-based thresholding. The neural network is used in this approach for automatic generation of three threshold values, with the three average illumination values of the three areas as the input. The fuzzy adjustment is utilized to smooth and adjust the gray level values of the image pixels along the boundaries. In this paper, the framework of NFRA and the rationale of the fuzzy adjustment will be presented, followed by the comparison of the recognition results using NFRA and the multi-resolution pattern classification (MPC) method. The result shows that the proposed NFRA performs fairly well on recognizing rust images. Finally, the conclusions will be drawn.

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“…As the computerized technologies were widely utilized, the digital image processing was also prevalently adopted in lots of industries [1]. In the construction area, the image processing has been used for defect recognition on steel bridge painting and underground sewer systems [2,3]. However, nonuniformly illuminated images always cause recognition problems and affect the accuracy.…”

Section: Introductionmentioning

confidence: 99%

Application of Neuro-Fuzzy Thresholding to Bridge Painting Defects Recognition

Chen¹,

Chang²

2000

Proceedings of the 17th IAARC/CIB/IEEE/IFAC/IFR International Symposium on Automation and Robotics in Construction

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The neuro-fuzzy thresholding method segments a grayscale image into three areas in accordance with the illumination values of the pixels in the image. The three average illumination values of the three areas are then sent to a pre-trained neural network to obtain the three corresponding threshold values. Fuzzy theory is used to adjust the gray level values of the pixels along the boundaries between areas. Finally, image thresholding is applied to get a binary image containing only the object pixels and the background pixels.

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Automated Quality Assessment of Constructed Surfaces through Intelligent Image Processing

Cited by 6 publications

References 3 publications

Intelligent Steel Bridge Coating Assessment Using Neuro‐Fuzzy Recognition Approach

Intelligent Steel Bridge Coating Assessment Using Neuro‐Fuzzy Recognition Approach

Computerized Bridge Coating Defects Assessment Using Neuro-Fuzzy Thresholding Method

Application of Neuro-Fuzzy Thresholding to Bridge Painting Defects Recognition

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