Single linkage region growing algorithms based on the vector degreeof match

The class of the single linkage region growing (SLRG) algorithms, in which pairs of neighboring pixels are compared for merging, is one of the conceptually simplest approaches to image segmentation. A new normalized coefficient, which measures the degree of match between two multivalued vectors, termed the vector degree of match (VDM), provides the SLRG applications with the metric needed to group adjacent pixel pairs. Two new SLRG algorithms, applied to multiband images and exploiting the VDM criterion, are presented. Their major advantage, in comparison with the SLRG implementations found in the literature, is that they required only one user-defined parameter, the VDM Threshold (VDMT), which is a normalized value featuring local adaptivity and having an intuitive physical meaning. The use of an SLRG module is convenient as the first stage of a structured segmentation procedure featuring the following functional properties: (i) it is implemented sequentially; (ii) it combines single linkage, centroid linkage and hybrid linkage criteria; and (iii) its goal is the detection of image areas characterized by low contrast     

Boosting modern and historical handwritten text recognition with deformable convolutions

International Journal on Document Analysis and Recognition (IJDAR) - Handwritten Text Recognition (HTR) in free-layout pages is a challenging image understanding task that can provide a relevant...     

Natural interaction on tabletops

We present two different Computer Vision based systems that enable multiple users to concurrently manipulate graphic objects presented over tabletop displays. The two solutions have different hardware layouts and use two different algorithms for gesture analysis and recognition. The first one is a media-handling application that can be used by co-located and remote users. The second is a knowledge-building application where users can manipulate the contents of a wiki as a visual concept map. The performance of both systems is evaluated and expounded. A conceptual framework is introduced, providing the fundamental guidelines for the design of natural interaction languages on tabletops.

Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery

Recent researches in fault classification have shown the importance of accurately selecting the features that have to be used as inputs to the diagnostic model. In this work, a multi-objective genetic algorithm (MOGA) is considered for the feature selection phase. Then, two different techniques for using the selected features to develop the fault classification model are compared: a single classifier based on the feature subset with the best classification performance and an ensemble of classifiers working on different feature subsets. The motivation for developing ensembles of classifiers is that they can achieve higher accuracies than single classifiers. An important issue for an ensemble to be effective is the diversity in the predictions of the base classifiers which constitute it, i.e. their capability of erring on different sub-regions of the pattern space. In order to show the benefits of having diverse base classifiers in the ensemble, two different ensembles have been developed: in the first, the base classifiers are constructed on feature subsets found by MOGAs aimed at maximizing the fault classification performance and at minimizing the number of features of the subsets; in the second, diversity among classifiers is added to the MOGA search as the third objective function to maximize. In both cases, a voting technique is used to effectively combine the predictions of the base classifiers to construct the ensemble output. For verification, some numerical experiments are conducted on a case of multiple-fault classification in rotating machinery and the results achieved by the two ensembles are compared with those obtained by a single optimal classifier.     

Badly posed classification of remotely sensed images-an experimental comparison of existing data labeling systems

Although underestimated in practice, the small/unrepresentative sample problem is likely to affect a large segment of real-world remotely sensed (RS) image mapping applications where ground truth knowledge is typically expensive, tedious, or difficult to gather. Starting from this realistic assumption, subjective (weak) but ample evidence of the relative effectiveness of existing unsupervised and supervised data labeling systems is collected in two RS image classification problems. To provide a fair assessment of competing techniques, first the two selected image datasets feature different degrees of image fragmentation and range from poorly to ill-posed. Second, different initialization strategies are tested to pass on to the mapping system at hand the maximally informative representation of prior (ground truth) knowledge. For estimating and comparing the competing systems in terms of learning ability, generalization capability, and computational efficiency when little prior knowledge is available, the recently published data-driven map quality assessment (DAMA) strategy, which is capable of capturing genuine, but small, image details in multiple reference cluster maps, is adopted in combination with a traditional resubstitution method. Collected quantitative results yield conclusions about the potential utility of the alternative techniques that appear to be realistic and useful in practice, in line with theoretical expectations and the qualitative assessment of mapping results by expert photointerpreters.     

Effect of beclomethasone dipropionate on bone mineral content assessed by X-ray densitometry in asthmatic children: a longitudinal evaluation

There is little information on bone turnover in asthmatic children taking long-term treatment with inhaled steroids (ICS). The aim of this longitudinal study was to determine the effects of inhaled beclomethasone dipropionate (BDP) on bone mineral density (BMD), in asthmatic children treated over a period of six months. BMD and growth were studied in two age- and sex-matched groups of asthmatic children. These comprised: 14 asthmatic children (Group 1) who had taken BDP in a dosage of 300-400 micrograms daily through a 145 ml spacer device for at least 6 months (mean age 9.1 yrs); and a control group of 16 age- and sex-matched asthmatic patients (Group 2) not treated with ICS (mean age 9.5 yrs). Mean duration of asthma was 5.7 yrs in Group 1 and 5.5 yrs in Group 2. Vertebral BMD (L2-L4) was measured by dual energy X-ray absorptiometry (DEXA) at the beginning (baseline) of the study and 6 months later. There were no significant differences in the baseline bone mass (mean +/- SEM) between the two groups (0.63 +/- 0.03 and 0.64 +/- 0.02 g.cm-2 in Group 1 and 2, respectively). During the observation period, bone density increased, by 4% (95% confidence interval (95% CI) 2-6) in the control group and by 2.3% (95% CI 0.4-4.2) in the group under BDP treatment, showing no significant influence of the treatment. No difference was found in height velocity evaluated before starting BDP and after 6 months of therapy.(ABSTRACT TRUNCATED AT 250 WORDS)     

Two novel procedures for aggregating randomized model ensemble outcomes for robust signal reconstruction in nuclear power plants monitoring systems

Detecting anomalies in sensors and reconstructing the correct values of the measured signals is of paramount importance for the safe and reliable operation of nuclear power plants. Auto-associative regression models can be used for the signal reconstruction task but in real applications the number of sensors signals may be too large to be handled effectively by one single model. In these cases, one may resort to an ensemble of reconstruction models, each one handling a small group of sensor signals; the outcomes of the individual models are then combined to produce the final reconstruction. In this work, three methods for aggregating the outcomes of a feature-randomized ensemble of Principal Components Analysis (PCA)-based regression models are analyzed and applied to two case studies concerning the reconstruction of 215 signals monitored at a Finnish nuclear Pressurized Water Reactor (PWR) and 920 simulated signals of the Swedish Forsmark-3 Boiling Water Reactor (BWR). Based on the insights gained, two novel aggregation procedures are developed for optimal signal reconstruction.     

Development of risk mitigation guidance for sensor placement inside mechanically ventilated enclosures – Phase 1

Guidance on Sensor Placement was identified as the top research priority for hydrogen sensors at the 2018 HySafe Research Priority Workshop on hydrogen safety in the category Mitigation, Sensors, Hazard Prevention, and Risk Reduction. This paper discusses the initial steps (Phase 1) to develop such guidance for mechanically ventilated enclosures. This work was initiated as an international collaborative effort to respond to emerging market needs related to the design and deployment equipment for hydrogen infrastructure that is often installed in individual equipment cabinets or ventilated enclosures. The ultimate objective of this effort is to develop guidance for an optimal sensor placement such that, when integrated into a facility design and operation, will allow earlier detection at lower levels of incipient leaks, leading to significant hazard reduction. Reliable and consistent early warning of hydrogen leaks will allow for the risk mitigation by reducing or even eliminating the probability of escalation of small leaks into large and uncontrolled events. To address this issue, a study of a real-world mechanically ventilated enclosure containing GH₂ equipment was conducted, where CFD modeling of the hydrogen dispersion (performed by AVT and UQTR, and independently by the JRC) was validated by the NREL Sensor laboratory using a Hydrogen Wide Area Monitor (HyWAM) consisting of a 10-point gas and temperature measurement analyzer. In the release test, helium was used as a hydrogen surrogate. Expansion of indoor releases to other larger facilities (including parking structures, vehicle maintenance facilities and potentially tunnels) and incorporation into QRA tools, such as HyRAM is planned for Phase 2. It is anticipated that results of this work will be used to inform national and international standards such as NFPA 2 Hydrogen Technologies Code, Canadian Hydrogen Installation Code (CHIC) and relevant ISO/TC 197 and CEN documents.     

HIAD – hydrogen incident and accident database

The Hydrogen Incident and Accident Database (HIAD) is being developed as a repository of systematic data describing in detail hydrogen-related undesired events (incidents or accidents). It is an open web-based information system serving various purposes such as a data source for lessons learnt, risk communication and partly risk assessment. The paper describes the features of the three HIAD modules – the Data Entry Module (DEM), the Data Retrieval Module (DRM) and the Data Analysis Module (DAM) – and the potential impact the database may have on hydrogen safety. The importance of data quality assurance process is also addressed.