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Underwater inspections stand to gain from using stereo imaging systems to collect three-dimensional measurements. Although many stereo-matching algorithms have been devised to solve the correspondence problem, that is, find the same points in multiple images, these algorithms often perform poorly when applied to images of underwater scenes due to the poor visibility and the complex underwater light field. This article presents a new stereo-matching algorithm, called PaLPaBEL (Pyramidal Loopy Propagated BELief) that is designed to operate on challenging imagery. At its core, PaLPaBEL is a semiglobal method based on a loopy belief propagation message passing algorithm applied on a Markov random field. A pyramidal scheme is adopted that enables wide disparity ranges and high-resolution images to be handled efficiently. For performance evaluation, PaLPaBEL is applied to underwater stereo images captured under various visibility conditions in a laboratory setting, and to synthetic imagery created in a virtual underwater environment. The technique is also demonstrated on stereo images obtained from a real-world inspection. The successful results indicate that PaLPaBEL is well suited for underwater application and has value as a tool for the cost-effective inspection of marine structures. Science Foundation Ireland CAPACITES/IXEAD Society

We are presenting an evaluation of the outcomes of the FAIRMODE & AQUILA intercomparison exercise (IE) on spatial representativeness (SR). The assessment of the spatial representativeness (SR) of air quality monitoring stations is an important subject that is linked to several highly topical areas, including risk assessment and population exposure, the design of monitoring networks, model development, model evaluation and data assimilation. Nevertheless, European regulations lack a clear definition and provisions to determine the SR of the stations. Also in the scientific literature, there is no unified agreement to address this complex problem. In order to further explore this topic and to make progress in the harmonization of the related assessment procedures, the FAIRMODE (Forum for Air Quality Modelling in Europe) Cross-Cutting Activity group on SR organized a comprehensive intercomparison exercise (IE). The main objective of this IE was to evaluate the scattering of spatial representativeness results obtained by applying the range of different contemporary approaches to a jointly used example case study. In order to ensure a broad participation in this exercise, a collaborative effort has been established between FAIRMODE and AQUILA (the European Network of Air Quality Reference Laboratories). As a working basis, a shared dataset has been collected among a set of monitoring, emission and modelling data from the city of Antwerp. Within this IE, 11 different teams from 9 different countries provided their SR estimates for PM10 and NO2 at one traffic site, and for PM10, NO2 and O3 at two urban background sites. In order to narrow down the range of conceivable SR approaches and definitions, it was beforehand suggested to use the area of SR of the monitoring sites as a general concept to work with. During the course of the exercise, this concept of the SR area in fact turned out to be a useful indicator, and 10 of 11 teams were able to define shapes surrounding the stations under investigation, whereas one team rather worked towards a classification of the stations, as this was more common practice for SR evaluation in their member state. The resulting SR areas nevertheless revealed a considerable range of dissimilarity between the different teams - not only in terms of the extent and position of the SR perimeters, but also in the technical procedures and the extent of input data effectively used. These differences required detailed evaluations in order to identify the major factors triggering and controlling this spread, which can be found amongst (1) the basic principles of the methods, (2) the parameterization of the similarity criteria and thresholds, (3) the effective use of input data, and (4) the detailed conceptualization and definitions of SR. These outcomes do underline the need for (i) a more harmonized definition of the concept of “the area of representativeness” and (ii) consistent and transparent criteria used for its quantification. A comprehensive concluding section (chapter 10) is highlighting the challenges that the expert community working on spatial representativeness is currently facing. Recommendations are given for the directions to be focused on SR in the near to mid-term future. In this regards, we are outlining a roadmap towards a modular approach for better SR characterization. It is stressed that that for the aim of harmonization the concept of spatial representativeness will probably require a paradigm shift in its definition. Beyond the questions of harmonization, it should not be disregarded that alternative interpretations for the strong variability of the SR results might exist. The observed divergences in SR results could for example point us to some more fundamental discrepancies related to the evaluation of the air quality data. It is advised to take care, that in the endeavour for methodological harmonization such alternative explanations are not overlooked. An example could be a potential inconsistency within the input data coming from emission, monitoring and modelled data. Furthermore, the findings of this study are not only relevant with regard to the SR of a single monitoring station. It also gives evidence that questions need to be raised about what is the real representativeness of network monitoring data in general since it seems that there is no current consensus on its evaluation. Example given: Is there a need for the European Commission to check the criteria for the number and the siting of Air Quality Monitoring Stations set in the Air Quality Directive when a consensus on SR is reached?, JRC.C.5-Air and Climate

Imaging‐based damage detection techniques are increasingly being utilized alongside traditional visual inspection methods to provide owners/operators of infrastructure with an efficient source of quantitative information for ensuring their continued safe and economic operation. However, there exists scope for significant development of improved damage detection algorithms that can characterize features of interest in challenging scenes with credibility. This article presents a new regionally enhanced multiphase segmentation (REMPS) technique that is designed to detect a broad range of damage forms on the surface of civil infrastructure. The technique is successfully applied to a corroding infrastructure component in a harbour facility. REMPS integrates spatial and pixel relationships to identify, classify, and quantify the area of damaged regions to a high degree of accuracy. The image of interest is preprocessed through a contrast enhancement and color reduction scheme. Features in the image are then identified using a Sobel edge detector, followed by subsequent classification using a clustering‐based filtering technique. Finally, support vector machines are used to classify pixels which are locally supplemented onto damaged regions to improve their size and shape characteristics. The performance of REMPS in different color spaces is investigated for best detection on the basis of receiver operating characteristics curves. The superiority of REMPS over existing segmentation approaches is demonstrated, in particular when considering high dynamic range imagery. It is shown that REMPS easily extends beyond the application presented and may be considered an effective and versatile standalone segmentation technique. Irish Research Council for Science, Engineering and Technology