Browsing by Author "Hoang-Nam Nguyen"

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A Novel Automatic Concrete Surface Crack Identification Using Isotropic Undecimated Wavelet Transform
(2012)
Hoang-Nam Nguyen
;
Kam, Tai-Yan
;
Cheng, Pi-Ying
;
機械工程學系
;
Department of Mechanical Engineering
traditionally, concrete surface cracks are manually measured and recorded by experienced inspectors who observe cracks with their naked eye. This task is very costly, time-consuming and dangerous. Some automatic crack detection techniques utilizing image processing have been proposed. However, it is very difficult to automatically identify cracks sufficiently fast and accurate. This paper presents a novel automatic algorithm for effectively identifying cracks in concrete structures, which can be applied to high resolution photographs. Firstly, as cracks can be considered as elongated dark objects in concrete surface images, all dark objects are roughly identified using Isotropic Undecimated Wavelet Transform (IUWT) and morphological image processing. Secondly, all previous detected dark objects are classified into crack and non-crack objects by analyzing their cross section pixel intensity profiles, which are computed perpendicularly across their centre-lines in the original input image, using a novel method based on Savitzky-Golay filter. Therefore, both local and global changes in crack width can be readily quantified. Experiments show that our method can automatically detect true cracks in concrete surface images using a unique set of parameters.
An Automatic Approach for Accurate Edge Detection of Concrete Crack Utilizing 2D Geometric Features of Crack
(2014-12-01)
Hoang-Nam Nguyen
;
Kam, Tai-Yan
;
Cheng, Pi-Ying
;
機械工程學系
;
Department of Mechanical Engineering
The automatic edge detection of cracks on concrete structures plays an important role in the damage assessment process for cracked structures. In this paper, we proposed an automatic method for accurate edge detection of concrete cracks from real 2D images of concrete surfaces containing noisy and unintended objects. In the 2D image of a damaged concrete surface, cracks are usually observed as tree-like topology dark objects of which the branches are line-like and have local symmetry across their center axes. We utilize these two geometric properties of cracks to detect crack edges and discriminate them with edges of other unintended objects. The novel automatic crack edge detection is composed of two sequential stages. In the first stage, cracks are enhanced by a novel phase symmetry-based crack enhancement filter (PSCEF) based on their symmetric and line-like properties while non-crack objects are removed. Estimated crack center-lines are then obtained by thresholding the filtered images and applying morphological thinning algorithm to the binary image. In the second stage, the estimated center lines of the detected cracks are fitted by cubic splines and the pixel intensity profiles in the directions perpendicular to the splines are used to determine the edge points. The edge points are linked together to form the desired continuous crack edges. Various experiments of real concrete crack images are used to demonstrate the excellent performance of the proposed method.
Object Contour Extraction Using Adaptive NURBS VFC Active Contour
(2011-01-01)
Hoang-Nam Nguyen
;
Lee, An-Chen
;
機械工程學系
;
Department of Mechanical Engineering
We proposed a novel active contour model, adaptive NURBS VFC snake, for extracting complex object boundaries. This model enables local control as well as adaptive shape description capabilities for state-of-the-art VFC (vector field convolution) snake to capture complicated contours with high concavity regions. The active contour is expressed as a non-uniform rational B-spline (NURBS) with a novel approach to iteratively estimate the control points' positions and weight values in the snake' evolution process based on a two-steps linear NURBS fitting method. A proposed algorithm automatically defines the fit number of control points based on the Euclidean distance between two consecutive control points and their weight values. Experimental results demonstrate the capability of accurate adaptive shape description for complex object contour with high concavity regions, and advantages in capture range, and initialization insensitivity.