Tiled fuzzy Hough transform for crack detection

Surface cracks can be the bellwether of the failure of any component under loading as it indicates the component's fracture due to stresses and usage. For this reason, crack detection is indispensable for the condition monitoring and quality control of road surfaces. Pavement images have high levels of intensity variation and texture content, hence the crack detection is difficult. Moreover, shallow cracks result in very low contrast image pixels making their detection difficult. For these reasons, studies on pavement crack detection is active even after years of research. In this paper, the fuzzy Hough transform is employed, for the first time to detect cracks on any surface. The contribution of texture pixels to the accumulator array is reduced by using the tiled version of the Hough transform. Precision values of 78% and a recall of 72% are obtaining for an image set obtained from an industrial imaging system containing very low contrast cracking. When only high contrast crack segments are considered the values move to mid to high 90%

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

AI-Enabled Contextual Representations for Image-based Integration in Health and Safety

Recent advancements in the area of Artificial Intelligence (AI) have made it the field of choice for automatically processing and summarizing information in big-data domains such as high-resolution images. This approach, however, is not a one-size-fits-all solution, and must be tailored to each application. Furthermore, each application comes with its own unique set of challenges including technical variations, validation of AI solutions, and contextual information. These challenges are addressed in three human-health and safety related applications: (i) an early warning system of slope failures in open-pit mining operations; (ii) the modeling and characterization of 3D cell culture models imaged with confocal microscopy; and (iii) precision medicine of biomarker discovery from patients with glioblastoma multiforme through digital pathology. The methodologies and results in each of these domains show how tailor-made AI solutions can be used for automatically extracting and summarizing pertinent information from big-data applications for enhanced decision making

Modelling the fracture of advanced carbon and related materials

This thesis outlines the development of a novel computational model which is used to simulate the mechanical response of nuclear graphites on a microstructural scale. Application of finite element analysis (FEA) to the simulated microstructure models allows for the determination of material properties and demonstrates the effect of porosity on these outputs. Further, a methodology for crack propagation through the model enables the simulation of load-displacement curves and fracture parameters. A comprehensive microstructural characterisation programme was undertaken to ascertain pore data for use in computational models. Composite images were generated through optical microscopy in order to sample large areas (10 x 10 mm) of the graphite surface. Results for this work demonstrated the inherent variability of graphite and successfully quantified the pore size distribution. Extensive mechanical testing was undertaken to determine the failure distribution of graphite and two additional brittle materials (glass and ligament material). Biaxial and three-point flexural experiments were employed in order to test a large number of samples. Data from these test programmes was determined to be consistent with a normal distribution and did not provide conclusive evidence for disparate flaw populations. Additional experimental tests were performed to provide data that could be used in the determination of suitable modelling input parameters. Development and solution of the microstructure model allowed accurate representation of pore distributions in an FEA environment which in turn enabled computationally derived mechanical properties to be determined. These properties were comparable to values expected of graphite. Additionally, some simulated fracture parameters compared favourably with experimental results. However, not all properties were representative due to the significant geometric contrast between computational models and experimental samples