Mike Grant

On a Large Sequence-Based Human Gait Database

Biometrics today include recognition by characteristic and by behaviour. Of these, face recognition is the most established with databases having evolved from small single shot single view databases, through multi-shot multi-view and on to current video-sequence databases. Results and potential of a new biometric are revealed primarily by the database on which new techniques are evaluated. Clearly, to ascertain the potential of gait as a biometric, a sequence-based database consisting of many subjects with multiple samples is needed. A large database enables the study of inter-subject variation. Further, issues concerning scene noise (or non-ideal conditions) need to be studied, ideally with a link between ground truth and application based analysis. Thus, we have designed and built a large human gait database, providing a large multi-purpose dataset enabling the investigation of gait as a biometric. In addition, it is also a useful database for many still and sequence based vision applications.

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.

Automatic recognition by gait: progress and prospects

Recognising people by their gait is a biometric of increasing interest. Recently, analysis has progressed from evaluation by few techniques on small databases with encouraging results to large databases and still with encouraging results. The potential of gait as a biometric was encouraged by the considerable amount of evidence available, especially in biomechanics and literature. This potential motivated the development of new databases, new technique and more rigorous evaluation procedures. We adumbrate some of the new techniques we have developed and their evaluation to gain insight into the potential for gait as a biometric. In particular, we consider implications for the future. Our work, as with others, continues to provide encouraging results for gait as a biometric, let alone as a human identifier, with a special regard for recognition at a distance.

WPS orchestration using the Taverna workbench: The eScience approach

eScience is an umbrella concept which covers internet technologies, such as web service orchestration that involves manipulation and processing of high volumes of data, using simple and efficient methodologies. This concept is normally associated with bioinformatics, but nothing prevents the use of an identical approach for geoinfomatics and OGC (Open Geospatial Consortium) web services like WPS (Web Processing Service). In this paper we present an extended WPS implementation based on the PyWPS framework using an automatically generated WSDL (Web Service Description Language) XML document that replicates the WPS input/output document structure used during an Execute request to a server. Services are accessed using a modified SOAP (Simple Object Access Protocol) interface provided by PyWPS, that uses service and input/outputs identifiers as element names. The WSDL XML document is dynamically generated by applying XSLT (Extensible Stylesheet Language Transformation) to the getCapabilities XML document that is generated by PyWPS. The availability of the SOAP interface and WSDL description allows WPS instances to be accessible to workflow development software like Taverna, enabling users to build complex workflows using web services represented by interconnecting graphics. Taverna will transform the visual representation of the workflow into a SCUFL (Simple Conceptual Unified Flow Language) based XML document that can be run internally or sent to a Taverna orchestration server. SCUFL uses a dataflow-centric orchestration model as opposed to the more commonly used orchestration language BPEL (Business Process Execution Language) which is process-centric.

An adaptive approach to detect high-biomass algal blooms from EO chlorophyll-a data in support of harmful algal bloom monitoring

High-biomass harmful algal blooms can kill farmed fish through toxicity, physical effects or de-oxygenation of the water column. These blooms often form over spatially large areas meaning that Earth observation is well placed to monitor and study them. In this letter, we present a statistical-based background subtraction technique that has been modified to detect high-biomass algal blooms. The method builds upon previous work and uses a statistical framework to combine spatial and temporal information to produce maps of bloom extent. Its statistical nature allows the approach to characterize the region of interest meaning that region-specific tuning is not needed. The accuracy of the approach has been evaluated using Moderate Resolution Imaging Spectroradiometer (MODIS) data and an in situ cell concentration dataset, resulting in a correct classification rate of 68.0% with a false alarm rate of 0.24 (n = 25). The method is then used to study the surface coverage of a large high-biomass harmful algal bloom of Karenia mikimotoi. The approach shows promise for the early warning of spatially large high-biomass algal blooms, providing valuable information to support in situ sampling campaigns.

Assessment of MERIS reflectance data as processed with SeaDAS over the European seas

The uncertainties associated with MERIS remote sensing reflectance (RRS) data derived from the SeaWiFS Data Analysis System (SeaDAS) are assessed with field observations. In agreement with the strategy applied for other sensors, a vicarious calibration is conducted using in situ data from the Marine Optical BuoY offshore Hawaii, and leads to vicarious adjustment factors departing from 1 by 0.2% to 1.6%. The three field data sets used for validation have been collected at fixed stations in the northern Adriatic Sea and the Baltic Sea, and in a variety of European waters in the Baltic, Black, Mediterranean and North Seas. Excluding Baltic waters, the mean absolute relative difference |ψ| between satellite and field data is 10-14% for the spectral interval 490-560 nm, 16-18% at 443 nm, and 24-26% at 413 nm. In the Baltic Sea, the |ψ| values are much higher for the blue bands characterized by low RRS amplitudes, but similar or lower at 560 and 665 nm. For the three validation sets, the root-mean-square differences decrease from approximately 0.0013 sr-1 at 413 nm to 0.0002 sr-1 at 665 nm, and are found similar or lower than those obtained for SeaWiFS or MODIS-Aqua. As derived from SeaDAS, the RRS records associated with these three missions thus provide a multi-mission data stream of consistent accuracy.

Extracting moving shapes by evidence gathering

Many approaches can track objects moving in sequences of images but can suffer in occlusion and noise, and often require initialisation. These factors can be handled by techniques that extract objects from image sequences, especially when phrased in terms of evidence gathering. Since the template approach is proven for arbitrary shapes, we re-deploy it for moving arbitrary shapes, but in a way aimed to avoid discretisation problems. In this way, the discrete mapping operation is deferred as far as possible, by using continuous shape descriptions. A further advantage is reduction in computational demand, as seen in use of templates for shape extraction. This prior specification of motion avoids the need to use an expensive parametric model to capture data that is already known. Furthermore, the complexity of the motion template model remains unchanged with increase in the complexity of motion, whereas a parametric model would require increasingly more parameters leading to an enormous increase in computational requirements. The new approach combining moving arbitrary shape description with motion templates permits us to achieve the objective of low dimensionality extraction of arbitrarily moving arbitrary shapes with performance advantage as reflected by the results this new technique can achieve.

The NERC DataGrid services

This short paper outlines the key components of the NERC DataGrid: a discovery service, a vocabulary service and a software stack deployed both centrally to provide a data discovery portal, and at data providers to provide local portals and data and metadata services.

Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL)

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircrafts position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points. HighlightsWe describe the novel and generic open-source Airborne Processing Library.The processing library can be used on Windows or Linux platforms either through the command line or a graphical user interface.We describe the novel algorithms and explain how the processing library can be used to process data from raw through to georectified.We evaluate the spatial accuracy of the geolocation algorithm within the Airborne Processing Library.

Assessing the fitness-for-purpose of satellite multi-mission ocean color climate data records: A protocol applied to OC-CCI chlorophyll-a data

In this work, trend estimates are used as indicators to compare the multi-annual variability of different satellite chlorophyll-a (Chla) data and to assess the fitness-for-purpose of multi-mission Chla products as climate data records (CDR). Under the assumption that single-mission products are free from spurious temporal artifacts and can be used as benchmark time series, multi-mission CDRs should reproduce the main trend patterns observed by single-mission series when computed over their respective periods. This study introduces and applies quantitative metrics to compare trend distributions from different data records. First, contingency matrices compare the trend diagnostics associated with two satellite products when expressed in binary categories such as existence, significance and signs of trends. Contingency matrices can be further summarized by metrics such as Cohens κ index that rates the overall agreement between the two distributions of diagnostics. A more quantitative measure of the discrepancies between trends is provided by the distributions of differences between trend slopes. Thirdly, maps of the level of significance P of a t-test quantifying the degree to which two trend estimates differ provide a statistical, spatially-resolved, evaluation. The proposed methodology is applied to the multi-mission Ocean Colour-Climate Change Initiative (OC-CCI) Chla data. The agreement between trend distributions associated with OC-CCI data and single-mission products usually appears as good as when single-mission products are compared. As the period of analysis is extended beyond 2012 to 2015, the level of agreement tends to be degraded, which might be at least partly due to the aging of the MODIS sensor on-board Aqua. On the other hand, the trends displayed by the OC-CCI series over the short period 2012–2015 are very consistent with those observed with VIIRS. These results overall suggest that the OC-CCI Chla data can be used for multi-annual time series analysis (including trend detection), but with some caution required if recent years are included, particularly in the central tropical Pacific. The study also recalls the challenges associated with creating a multi-mission ocean color data record suitable for climate research.