Real-Time Image Thresholding Based on Sample Space Reduction and Interpolation Approach

Cheng, Heng-Da; Shi, Xiangjun; Glazier, Chris

doi:10.1061/(asce)0887-3801(2003)17:4(264)

Cited by 112 publications

(58 citation statements)

References 7 publications

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“…Thus, the output of these types of methods is commonly termed a "crack map". Cheng et al 13 proposed a sample space reduction and interpolation thresholding approach for detecting cracks in pavement images. The thresholds were determined on the basis of a relation to the mean and standard deviation of the pixel intensities of the gray-level pavement images.…”

Section: Damage Detectionmentioning

confidence: 99%

“…The criteria investigated were: (1) image pixel mean intensity, (2) global entropy in pixel saturation and (3) a combination of (1) and (2); the third criteria was determined to be the most apt for the purpose of the work outlined in this paper. The two threshold values, T 1 and T 2 , are considered adaptive, as they are determined uniquely for each image by way of a sample space reduction and interpolation approach 13 . Each of the desired image characteristics (mean intensity and entropy in saturation) was determined for a sample space set of 70 images.…”

Section: Depth Retrieval: Longitudinal Reinforcement Detectionmentioning

confidence: 99%

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Comprehensive property retrieval and measurement of concrete spalling using machine vision for post-earthquake safety assessments

2012

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Current procedures in post-earthquake safety and structural assessment are performed by a skilled triage team of structural engineers/certified inspectors. These procedures, in particular the physical measurement of the damage properties, are time-consuming and qualitative in nature. Spalling has been accepted as an important indicator of significant damage to structural elements during an earthquake, and thus provides a sound springboard for a model in machine vision automated assessment procedures as is proposed in this research. Thus, a novel method that automatically detects regions of spalling on reinforced concrete columns and measures their properties in image data is the specific focus of this work. According to this method, the region of spalling is first isolated by way of a local entropy-based thresholding algorithm. Following this, the properties of the spalled region are depicted by way of classification of the extent of spalling on the column. The region of spalling is sorted into one of three categories by way of a novel global entropy-based adaptive thresholding algorithm in conjunction with well-established image processing methods in template matching and morphological operations. These three categories are specified as the following: (1) No spalling; (2) Spalling of cover concrete; and (3) Spalling of the core concrete (exposing reinforcement). In addition, the extent of the spalling along the length of the column is quantified. The method was tested on a database of damaged RC column images collected after the 2010 Haiti Earthquake, and comparison of the results with manual measurements indicate the validity of the method.

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Section: Damage Detectionmentioning

confidence: 99%

Section: Depth Retrieval: Longitudinal Reinforcement Detectionmentioning

confidence: 99%

Comprehensive property retrieval and measurement of concrete spalling using machine vision for post-earthquake safety assessments

2012

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“…Accordingly, various image processing models have been constructed to recognize asphalt pavement crack. Road crack detection models that employ image thresholding algorithms have been put forward by Cheng et al [21], Oliveira and Correia [22], and Ying and Salari [23]. Moreover, the models that are based on edge detection algorithms have also been established by many scholars [24][25][26].…”

Section: Review Of Related Workmentioning

confidence: 99%

Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding

Hoang

2018

Advances in Civil Engineering

114

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e detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. e manual process of crack detection is painstakingly time-consuming and suffers from subjective judgments of inspectors. is study establishes an intelligent model based on image processing techniques for automatic crack recognition and analyses. In the new model, a gray intensity adjustment method, called Min-Max Gray Level Discrimination (M2GLD), is proposed to preprocess the image thresholded by the Otsu method. e goal of this gray intensity adjustment method is to meliorate the accuracy of the crack detection results. Experimental results point out that the integration of M2GLD and the Otsu method, followed by other shape analysis algorithms, can successfully detect crack defects in digital images. erefore, the constructed model can be a useful tool for building management agencies and construction engineers in the task of structure maintenance.

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“…[3] uses intensity histograms of the images, so it cannot detect the line part similar to the background and it cannot handle the complex background images. Chang et al use adaptive threshold [4] set by intensity histograms of the images to segment the laser line. The adaptive threshold value can be also set by weighted neighborhood average [5] rather than intensities.…”

Section: Intorductionmentioning

confidence: 99%

A New Open Curve Detection Algorithm for Extracting the Laser Lines on the Road

Fan

et al. 2011

AEF

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Abstract. This paper proposes a new open curve detection algorithm for recognizing the laser rays on the road to verify the roughness of the road surface by parsing the curvature of the laser rays.The laser line in the image is first enhanced by the bright line enhance filtering to remove the clutters generated by the background. The enhanced line has the characteristics that its gray value is greater than that of background, so the established energy functional in open curve detection process has a gray value constraint to drive the evolution curve to the brightest place in image. The line on the road surface is an open curve throughout the whole image, so we fixed the two endpoints of the evolution curve to maintain it is an open curve in curve evolution. Moreover, we design an edge inhibit operator to restrain the influence of objects edges to ensure the accuracy of the test laser line. We compared our algorithm with the edge detection operators, and the comparison results showed that our method is more accurate and more robustness to the background.

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Real-Time Image Thresholding Based on Sample Space Reduction and Interpolation Approach

Cited by 112 publications

References 7 publications

Comprehensive property retrieval and measurement of concrete spalling using machine vision for post-earthquake safety assessments

Comprehensive property retrieval and measurement of concrete spalling using machine vision for post-earthquake safety assessments

Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding

A New Open Curve Detection Algorithm for Extracting the Laser Lines on the Road

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