PETS2010 and PETS2009 evaluation of results using individual ground truthed single views

This paper presents the results of the crowd image analysis challenge of the PETS2010 workshop. The evaluation was carried out using a selection of the metrics developed in the Video Analysis and Content Extraction (VACE) program and the CLassification of Events, Activities, and Relationships (CLEAR) consortium. The PETS 2010 evaluation was performed using new ground truthing create from each independant two dimensional view. In addition, the performance of the submissions to the PETS 2009 and Winter-PETS 2009 were evaluated and included in the results. The evaluation highlights the detection and tracking performance of the authors’ systems in areas such as precision, accuracy and robustness.

Dimetallic complexes of macrocycles with two rigid dibenzofuran units as receptors for detection of anionic substrates

The hexaazamacrocycles [28](DBF)2N6 {cyclo[bis(4,6-dimethyldibenzo[b,d]furaniminoethyleneiminoethylene]} and [32](DBF)2N6 {cyclo[bis(4,6-dimethyldibenzo[b,d]furaniminopropyleneiminopropylene]} form stable dinuclear copper(II) complexes suitable to behave as receptors for several anionic substrates. These two receptors were used to study the binding interactions with several substrates, such as imidazole (Him) and some carboxylates [benzoate (bz−), oxalate (ox2−), malonate (mal2−), phthalate (ph2−), isophthalate (iph2−), and terephthalate (tph2−)] by spectrophotometric titrations and EPR spectroscopy in MeOH (or H2O):DMSO (1:1 v/v) solution. The largest association constant was found for ox2− with Cu2[32](DBF)2N64+, whereas for the aromatic dicarboxylate anions the binding constants follow the trend ph2− > iph2− > tph2−, i.e. decrease with the increase of the distance of the two binding sites of the substrate. On the other hand, the large blue shift of 68 nm observed by addition of Him to Cu2[32](DBF)2N64+ points out for the formation of the bridged CuimCu cascade complex, indicating this receptor as a potential sensor for the detection and determination of imidazole in solution. The X-band EPR spectra of the Cu2[28](DBF)2N64+ and Cu2[32](DBF)2N6]4+ complexes and the cascade complexes with the substrates, performed in H2O:DMSO (1:1 v/v) at 5 to 15 K, showed that the CuCu distance is slightly larger than the one found in crystal state and that this distance increases when the substrate is accommodated between the two copper centres. The crystal structure of [Cu2[28](DBF)2N6(ph)2]·CH3OH was determined by X-ray diffraction and revealed the two copper centres bridged by two ph2− anions at a Cu···Cu distance of 5.419(1) Å. Each copper centre is surrounded by three carboxylate oxygen atoms from two phthalate anions and three contiguous nitrogen atoms of the macrocycle in a pseudo octahedral coordination environment.

Combined bias and outlier identification in dynamic data reconciliation

Measured process data normally contain inaccuracies because the measurements are obtained using imperfect instruments. As well as random errors one can expect systematic bias caused by miscalibrated instruments or outliers caused by process peaks such as sudden power fluctuations. Data reconciliation is the adjustment of a set of process data based on a model of the process so that the derived estimates conform to natural laws. In this paper, techniques for the detection and identification of both systematic bias and outliers in dynamic process data are presented. A novel technique for the detection and identification of systematic bias is formulated and presented. The problem of detection, identification and elimination of outliers is also treated using a modified version of a previously available clustering technique. These techniques are also combined to provide a global dynamic data reconciliation (DDR) strategy. The algorithms presented are tested in isolation and in combination using dynamic simulations of two continuous stirred tank reactors (CSTR).

Identification of metabolites in human hepatic bile using 800 MHz 1H NMR spectroscopy, HPLC-NMR/MS and UPLC-MS

The first application of high field NMR spectroscopy (800 MHz for 1H observation) to human hepatic bile (as opposed to gall bladder bile) is reported. The bile sample used for detailed investigation was from a donor liver with mild fat infiltration, collected during organ retrieval prior to transplantation. In addition, to focus on the detection of bile acids in particular, a bile extract was analysed by 800 MHz 1H NMR spectroscopy, HPLC-NMR/MS and UPLC-MS. In the whole bile sample, 40 compounds have been assigned with the aid of two-dimensional 1H–1H TOCSY and 1H–13C HSQC spectra. These include phosphatidylcholine, 14 amino acids, 10 organic acids, 4 carbohydrates and polyols (glucose, glucuronate, glycerol and myo-inositol), choline, phosphocholine, betaine, trimethylamine-N-oxide and other small molecules. An initial NMR-based assessment of the concentration range of some key metabolites has been made. Some observed chemical shifts differ from expected database values, probably due to a difference in bulk diamagnetic susceptibility. The NMR spectra of the whole extract gave identification of the major bile acids (cholic, deoxycholic and chenodeoxycholic), but the glycine and taurine conjugates of a given bile acid could not be distinguished. However, this was achieved by HPLC-NMR/MS, which enabled the separation and identification of ten conjugated bile acids with relative abundances varying from approximately 0.1% (taurolithocholic acid) to 34.0% (glycocholic acid), of which, only the five most abundant acids could be detected by NMR, including the isomers glycodeoxycholic acid and glycochenodeoxycholic acid, which are difficult to distinguish by conventional LC-MS analysis. In a separate experiment, the use of UPLC-MS allowed the detection and identification of 13 bile acids. This work has shown the complementary potential of NMR spectroscopy, MS and hyphenated NMR/MS for elucidating the complex metabolic profile of human hepatic bile. This will be useful baseline information in ongoing studies of liver excretory function and organ transplantation.

A comparison of two identification and tracking methods for polar lows

In this study, we compare two different cyclone-tracking algorithms to detect North Atlantic polar lows, which are very intense mesoscale cyclones. Both approaches include spatial filtering, detection, tracking and constraints specific to polar lows. The first method uses digital bandpass-filtered mean sea level pressure (MSLP) fieldsin the spatial range of 200�600 km and is especially designed for polar lows. The second method also uses a bandpass filter but is based on the discrete cosine transforms (DCT) and can be applied to MSLP and vorticity fields. The latter was originally designed for cyclones in general and has been adapted to polar lows for this study. Both algorithms are applied to the same regional climate model output fields from October 1993 to September 1995 produced from dynamical downscaling of the NCEP/NCAR reanalysis data. Comparisons between these two methods show that different filters lead to different numbers and locations of tracks. The DCT is more precise in scale separation than the digital filter and the results of this study suggest that it is more suited for the bandpass filtering of MSLP fields. The detection and tracking parts also influence the numbers of tracks although less critically. After a selection process that applies criteria to identify tracks of potential polar lows, differences between both methods are still visible though the major systems are identified in both.

Assessment of inter-examiner agreement and variability in the manual classification of auditory brainstem response

Abstract Background: The analysis of the Auditory Brainstem Response (ABR) is of fundamental importance to the investigation of the auditory system behaviour, though its interpretation has a subjective nature because of the manual process employed in its study and the clinical experience required for its analysis. When analysing the ABR, clinicians are often interested in the identification of ABR signal components referred to as Jewett waves. In particular, the detection and study of the time when these waves occur (i.e., the wave latency) is a practical tool for the diagnosis of disorders affecting the auditory system. Significant differences in inter-examiner results may lead to completely distinct clinical interpretations of the state of the auditory system. In this context, the aim of this research was to evaluate the inter-examiner agreement and variability in the manual classification of ABR. Methods: A total of 160 ABR data samples were collected, for four different stimulus intensity (80dBHL, 60dBHL, 40dBHL and 20dBHL), from 10 normal-hearing subjects (5 men and 5 women, from 20 to 52 years). Four examiners with expertise in the manual classification of ABR components participated in the study. The Bland-Altman statistical method was employed for the assessment of inter-examiner agreement and variability. The mean, standard deviation and error for the bias, which is the difference between examiners’ annotations, were estimated for each pair of examiners. Scatter plots and histograms were employed for data visualization and analysis. Results: In most comparisons the differences between examiner’s annotations were below 0.1 ms, which is clinically acceptable. In four cases, it was found a large error and standard deviation (>0.1 ms) that indicate the presence of outliers and thus, discrepancies between examiners. Conclusions: Our results quantify the inter-examiner agreement and variability of the manual analysis of ABR data, and they also allows for the determination of different patterns of manual ABR analysis.

Promoter analysis and immunolocalisation show that puroindoline genes are exclusively expressed in starchy endosperm cells of wheat grain

The purolindolines are small cysteine-rich proteins which are present in the grain of wheat. They have a major impact on the utilisation of the grain as they are the major determinants of grain texture, which affects both milling and baking properties. Bread and durum wheats were transformed with constructs comprising the promoter regions of the Puroindoline a (Pina) and Puroindoline b (Pinb) genes fused to the uidA (GUS) reporter gene. Nine lines showing 3:1 segregation for the transgene and comprising all transgene/species combinations were selected for detailed analysis of transgene expression during grain development. This showed that transgene expression occurred only in the starchy endosperm cells and was not observed in any other seed or vegetative tissues. The location of the puroindoline proteins in these cells was confirmed by tissue printing of developing grain, using a highly specific monoclonal antibody for detection and an antibody to the aleurone-localised 8S globulin as a control. This provides clear evidence that puroindolines are only synthesised and accumulated in the starchy endosperm cells of the wheat grain.

Have greenhouse gases intensified the contrast between wet and dry regions?

While changes in land precipitation during the last 50 years have been attributed in part to human influences, results vary by season, are affected by data uncertainty and do not account for changes over ocean. One of the more physically robust responses of the water cycle to warming is the expected amplification of existing patterns of precipitation minus evaporation. Here, precipitation changes in wet and dry regions are analyzed from satellite data for 1988–2010, covering land and ocean. We derive fingerprints for the expected change from climate model simulations that separately track changes in wet and dry regions. The simulations used are driven with anthropogenic and natural forcings combined, and greenhouse gas forcing or natural forcing only. Results of detection and attribution analysis show that the fingerprint of combined external forcing is detectable in observations and that this intensification of the water cycle is partly attributable to greenhouse gas forcing.

Detection of flooded urban areas in high resolution Synthetic Aperture Radar images using double scattering

Flooding is a particular hazard in urban areas worldwide due to the increased risks to life and property in these regions. Synthetic Aperture Radar (SAR) sensors are often used to image flooding because of their all-weather day-night capability, and now possess sufficient resolution to image urban flooding. The flood extents extracted from the images may be used for flood relief management and improved urban flood inundation modelling. A difficulty with using SAR for urban flood detection is that, due to its side-looking nature, substantial areas of urban ground surface may not be visible to the SAR due to radar layover and shadow caused by buildings and taller vegetation. This paper investigates whether urban flooding can be detected in layover regions (where flooding may not normally be apparent) using double scattering between the (possibly flooded) ground surface and the walls of adjacent buildings. The method estimates double scattering strengths using a SAR image in conjunction with a high resolution LiDAR (Light Detection and Ranging) height map of the urban area. A SAR simulator is applied to the LiDAR data to generate maps of layover and shadow, and estimate the positions of double scattering curves in the SAR image. Observations of double scattering strengths were compared to the predictions from an electromagnetic scattering model, for both the case of a single image containing flooding, and a change detection case in which the flooded image was compared to an un-flooded image of the same area acquired with the same radar parameters. The method proved successful in detecting double scattering due to flooding in the single-image case, for which flooded double scattering curves were detected with 100% classification accuracy (albeit using a small sample set) and un-flooded curves with 91% classification accuracy. The same measures of success were achieved using change detection between flooded and un-flooded images. Depending on the particular flooding situation, the method could lead to improved detection of flooding in urban areas.

Extraction of unexpected rules from Twitter hashtags and its application to sport events

Twitter has become a dependable microblogging tool for real time information dissemination and newsworthy events broadcast. Its users sometimes break news on the network faster than traditional newsagents due to their presence at ongoing real life events at most times. Different topic detection methods are currently used to match Twitter posts to real life news of mainstream media. In this paper, we analyse tweets relating to the English FA Cup finals 2012 by applying our novel method named TRCM to extract association rules present in hash tag keywords of tweets in different time-slots. Our system identify evolving hash tag keywords with strong association rules in each time-slot. We then map the identified hash tag keywords to event highlights of the game as reported in the ground truth of the main stream media. The performance effectiveness measure of our experiments show that our method perform well as a Topic Detection and Tracking approach.

The potential of flood forecasting using a variable-resolution global Digital Terrain Model and flood extents from Synthetic Aperture Radar images

A basic data requirement of a river flood inundation model is a Digital Terrain Model (DTM) of the reach being studied. The scale at which modeling is required determines the accuracy required of the DTM. For modeling floods in urban areas, a high resolution DTM such as that produced by airborne LiDAR (Light Detection And Ranging) is most useful, and large parts of many developed countries have now been mapped using LiDAR. In remoter areas, it is possible to model flooding on a larger scale using a lower resolution DTM, and in the near future the DTM of choice is likely to be that derived from the TanDEM-X Digital Elevation Model (DEM). A variable-resolution global DTM obtained by combining existing high and low resolution data sets would be useful for modeling flood water dynamics globally, at high resolution wherever possible and at lower resolution over larger rivers in remote areas. A further important data resource used in flood modeling is the flood extent, commonly derived from Synthetic Aperture Radar (SAR) images. Flood extents become more useful if they are intersected with the DTM, when water level observations (WLOs) at the flood boundary can be estimated at various points along the river reach. To illustrate the utility of such a global DTM, two examples of recent research involving WLOs at opposite ends of the spatial scale are discussed. The first requires high resolution spatial data, and involves the assimilation of WLOs from a real sequence of high resolution SAR images into a flood model to update the model state with observations over time, and to estimate river discharge and model parameters, including river bathymetry and friction. The results indicate the feasibility of such an Earth Observation-based flood forecasting system. The second example is at a larger scale, and uses SAR-derived WLOs to improve the lower-resolution TanDEM-X DEM in the area covered by the flood extents. The resulting reduction in random height error is significant.

Time-lapse geophysical investigations over known archaeological features using electrical resistivity imaging and earth resistance

Electrical methods of geophysical survey are known to produce results that are hard to predict at different times of the year, and under differing weather conditions. This is a problem which can lead to misinterpretation of archaeological features under investigation. The dynamic relationship between a ‘natural’ soil matrix and an archaeological feature is a complex one, which greatly affects the success of the feature’s detection when using active electrical methods of geophysical survey. This study has monitored the gradual variation of measured resistivity over a selection of study areas. By targeting difficult to find, and often ‘missing’ electrical anomalies of known archaeological features, this study has increased the understanding of both the detection and interpretation capabilities of such geophysical surveys. A 16 month time-lapse study over 4 archaeological features has taken place to investigate the aforementioned detection problem across different soils and environments. In addition to the commonly used Twin-Probe earth resistance survey, electrical resistivity imaging (ERI) and quadrature electro-magnetic induction (EMI) were also utilised to explore the problem. Statistical analyses have provided a novel interpretation, which has yielded new insights into how the detection of archaeological features is influenced by the relationship between the target feature and the surrounding ‘natural’ soils. The study has highlighted both the complexity and previous misconceptions around the predictability of the electrical methods. The analysis has confirmed that each site provides an individual and nuanced situation, the variation clearly relating to the composition of the soils (particularly pore size) and the local weather history. The wide range of reasons behind survey success at each specific study site has been revealed. The outcomes have shown that a simplistic model of seasonality is not universally applicable to the electrical detection of archaeological features. This has led to the development of a method for quantifying survey success, enabling a deeper understanding of the unique way in which each site is affected by the interaction of local environmental and geological conditions.

PETS 2015: datasets and challenge

This paper presents the two datasets (ARENA and P5) and the challenge that form a part of the PETS 2015 workshop. The datasets consist of scenarios recorded by us- ing multiple visual and thermal sensors. The scenarios in ARENA dataset involve different staged activities around a parked vehicle in a parking lot in UK and those in P5 dataset involve different staged activities around the perimeter of a nuclear power plant in Sweden. The scenarios of each dataset are grouped into ‘Normal’, ‘Warning’ and ‘Alarm’ categories. The Challenge specifically includes tasks that account for different steps in a video understanding system: Low-Level Video Analysis (object detection and tracking), Mid-Level Video Analysis (‘atomic’ event detection) and High-Level Video Analysis (‘complex’ event detection). The evaluation methodology used for the Challenge includes well-established measures.

PETS 2014: dataset and challenge

This paper describes the dataset and vision challenges that form part of the PETS 2014 workshop. The datasets are multisensor sequences containing different activities around a parked vehicle in a parking lot. The dataset scenarios were filmed from multiple cameras mounted on the vehicle itself and involve multiple actors. In PETS2014 workshop, 22 acted scenarios are provided of abnormal behaviour around the parked vehicle. The aim in PETS 2014 is to provide a standard benchmark that indicates how detection, tracking, abnormality and behaviour analysis systems perform against a common database. The dataset specifically addresses several vision challenges corresponding to different steps in a video understanding system: Low-Level Video Analysis (object detection and tracking), Mid-Level Video Analysis (‘simple’ event detection: the behaviour recognition of a single actor) and High-Level Video Analysis (‘complex’ event detection: the behaviour and interaction recognition of several actors).

Robust multimodal face and fingerprint fusion in the presence of spoofing attacks

Anti-spoofing is attracting growing interest in biometrics, considering the variety of fake materials and new means to attack biometric recognition systems. New unseen materials continuously challenge state-of-the-art spoofing detectors, suggesting for additional systematic approaches to target anti-spoofing. By incorporating liveness scores into the biometric fusion process, recognition accuracy can be enhanced, but traditional sum-rule based fusion algorithms are known to be highly sensitive to single spoofed instances. This paper investigates 1-median filtering as a spoofing-resistant generalised alternative to the sum-rule targeting the problem of partial multibiometric spoofing where m out of n biometric sources to be combined are attacked. Augmenting previous work, this paper investigates the dynamic detection and rejection of livenessrecognition pair outliers for spoofed samples in true multi-modal configuration with its inherent challenge of normalisation. As a further contribution, bootstrap aggregating (bagging) classifiers for fingerprint spoof-detection algorithm is presented. Experiments on the latest face video databases (Idiap Replay- Attack Database and CASIA Face Anti-Spoofing Database), and fingerprint spoofing database (Fingerprint Liveness Detection Competition 2013) illustrate the efficiency of proposed techniques.