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研究兴趣
Thermographic NDT for through-life inspection of high value components
Non-destructive testing (NDT) are techniques used to detect and characterise flaws that occur in materials from manufacture through to evaluating the health of the material without causing further damage to the component. With the development of cutting-edge technology over the last two decades, active thermographic NDT has grown considerably as a field. Access to lower cost, more portable hardware with higher performance has fuelled a major drive in research to develop analytical techniques and widen the applicability of thermography in order to exploit its advantages as a low-cost and non-contact inspection technique. While passive thermography is a heavily standardised process, active thermography is considerably lacking in industrial standards. Development of these standards represents an opportunity for research in the field of active thermography to be a part of that process. Recent industry pressure regarding research in NDT has developed a demand for NDT techniques to be quantifiable, and linked directly to material properties, thus allowing an estimation of remaining useful life (RUL) in order to maximise product value. There have been significant research developments in the field over recent years. Through developments in signal processing of thermography data, inspections would enable repeatable, quantifiable benchmarking of samples, which would allow automation of carefully controlled quality checks; and the measurement of thermal properties of the material, which would allow estimation of components’ RUL. Addressing these challenges would increase the deployment of thermography in industry, enhance the toolset of through-life engineering, and significantly improve the competitiveness of industries which embrace these developments.
Satellite image resolution enhancement using discrete wavelet transform and new edge-directed interpolation
An image resolution enhancement approach based on discrete wavelet transform (DWT) and new edge-directed interpolation (NEDI) for degraded satellite images by geometric distortion to correct the errors in image geometry and recover the edge details of directional high-frequency subbands is proposed. The observed image is decomposed into four frequency subbands through DWT, and then the three high-frequency subbands and the observed image are processed with NEDI. To better preserve the edges and remove potential noise in the estimated high-frequency subbands, an adaptive threshold is applied to process the estimated wavelet coefficients. Finally, the enhanced image is reconstructed by applying inverse DWT. Four criteria are introduced, aiming to better assess the overall performance of the proposed approach for different types of satellite images. A public satellite images data set is selected for the validation purpose. The visual and quantitative results show the superiority of the proposed approach over the conventional and state-of-the-art image resolution enhancement.
Saliency driven vasculature segmentation with infinite perimeter active contour model
Automated detection of retinal blood vessels plays an important role in advancing the understanding of the mechanism, diagnosis and treatment of cardiovascular disease and many systemic diseases, such as diabetic retinopathy and age-related macular degeneration. Here, we propose a new framework for precisely segmenting retinal vasculatures. The proposed framework consists of three steps. A non-local total variation model is adapted to the Retinex theory, which aims to address challenges presented by intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The image is then divided into superpixels, and a compactness-based saliency detection method is proposed to locate the object of interest. For better general segmentation performance, we then make use of a new infinite active contour model to segment the vessels in each superpixel. The proposed framework has wide applications, and the results show that our model outperforms its competitors.
Intensity and compactness enabled saliency estimation for leakage detection in diabetic and malarial retinopathy
Leakage in retinal angiography currently is a key feature for confirming the activities of lesions in the management of a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. This paper proposes a new saliency-based method for the detection of leakage in fluorescein angiography. A superpixel approach is firstly employed to divide the image into meaningful patches (or superpixels) at different levels. Two saliency cues, intensity and compactness, are then proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. The two saliency maps over different cues are fused using a pixel-wise multiplication operator. Leaking regions are finally detected by thresholding the saliency map followed by a graph-cut segmentation. The proposed method has been validated using the only two publicly available datasets: one for malarial retinopathy and the other for diabetic retinopathy. The experimental results show that it outperforms one of the latest competitors and performs as well as a human expert for leakage detection and outperforms several state-of-the-art methods for saliency detection.
Comparison of alternatives to amplitude thresholding for onset detection of acoustic emission signals
Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors in an array is essential in performing localisation. Currently, this is determined using a fixed threshold which is particularly prone to errors when not set to optimal values. This paper presents three new methods for determining the onset of AE signals without the need for a predetermined threshold. The performance of the techniques is evaluated using AE signals generated during fatigue crack growth and compared to the established Akaike Information Criterion (AIC) and fixed threshold methods. It was found that the 1D location accuracy of the new methods was within the range of <1–7.1