The primal sketch revisited: locating and representing edges in human vision via Gaussian-derivative filtering

Marr's work offered guidelines on how to investigate vision (the theory - algorithm - implementation distinction), as well as specific proposals on how vision is done. Many of the latter have inevitably been superseded, but the approach was inspirational and remains so. Marr saw the computational study of vision as tightly linked to psychophysics and neurophysiology, but the last twenty years have seen some weakening of that integration. Because feature detection is a key stage in early human vision, we have returned to basic questions about representation of edges at coarse and fine scales. We describe an explicit model in the spirit of the primal sketch, but tightly constrained by psychophysical data. Results from two tasks (location-marking and blur-matching) point strongly to the central role played by second-derivative operators, as proposed by Marr and Hildreth. Edge location and blur are evaluated by finding the location and scale of the Gaussian-derivative `template' that best matches the second-derivative profile (`signature') of the edge. The system is scale-invariant, and accurately predicts blur-matching data for a wide variety of 1-D and 2-D images. By finding the best-fitting scale, it implements a form of local scale selection and circumvents the knotty problem of integrating filter outputs across scales. [Supported by BBSRC and the Wellcome Trust]

Stock selection with full-scale optimization and differential evolution

When composing stock portfolios, managers frequently choose among hundreds of stocks. The stocks' risk properties are analyzed with statistical tools, and managers try to combine these to meet the investors' risk profiles. A recently developed tool for performing such optimization is called full-scale optimization (FSO). This methodology is very flexible for investor preferences, but because of computational limitations it has until now been infeasible to use when many stocks are considered. We apply the artificial intelligence technique of differential evolution to solve FSO-type stock selection problems of 97 assets. Differential evolution finds the optimal solutions by self-learning from randomly drawn candidate solutions. We show that this search technique makes large scale problem computationally feasible and that the solutions retrieved are stable. The study also gives further merit to the FSO technique, as it shows that the solutions suit investor risk profiles better than portfolios retrieved from traditional methods.

Fuel supplier selection for large scale UK bioenergy schemes

This article presents a potential method to assist developers of future bioenergy schemes when selecting from available suppliers of biomass materials. The method aims to allow tacit requirements made on biomass suppliers to be considered at the design stage of new developments. The method used is a combination of the Analytical Hierarchy Process and the Quality Function Deployment methods (AHP-QFD). The output of the method is a ranking and relative weighting of the available suppliers which could be used to improve optimization algorithms such as linear and goal programming. The paper is at a conceptual stage and no results have been obtained. The aim is to use the AHP-QFD method to bridge the gap between treatment of explicit and tacit requirements of bioenergy schemes; allowing decision makers to identify the most successful supply strategy available.

Internal versus external R&D:a study of R&D choice with sample selection

This paper extends previous analyses of the choice between internal and external R&D to consider the costs of internal R&D. The Heckman two-stage estimator is used to estimate the determinants of internal R&D unit cost (i.e. cost per product innovation) allowing for sample selection effects. Theory indicates that R&D unit cost will be influenced by scale issues and by the technological opportunities faced by the firm. Transaction costs encountered in research activities are allowed for and, in addition, consideration is given to issues of market structure which influence the choice of R&D mode without affecting the unit cost of internal or external R&D. The model is tested on data from a sample of over 500 UK manufacturing plants which have engaged in product innovation. The key determinants of R&D mode are the scale of plant and R&D input, and market structure conditions. In terms of the R&D cost equation, scale factors are again important and have a non-linear relationship with R&D unit cost. Specificities in physical and human capital also affect unit cost, but have no clear impact on the choice of R&D mode. There is no evidence of technological opportunity affecting either R&D cost or the internal/external decision.

The selection of biodiversity indicators for business landholdings

Businesses are seen as the next stage in delivering biodiversity improvements linked to local and UK Biodiversity Action Plans. Global discussion of biodiversity continues to grow, with the Millennium Ecosystem Assessment, updates to the Convention on Biological Diversity and The Economics of Ecosystems and Biodiversity being published during the time of this project. These publications and others detail the importance of biodiversity protection and also the lack of strategies to deliver this at an operational level. Pressure on UK landholding businesses is combined with significant business opportunities associated with biodiversity engagement. However, the measurement and reporting of biodiversity by business is currently limited by the complexity of the term and the lack of suitable procedures for the selection of metrics. Literature reviews identified confusion surrounding biodiversity as a term, limited academic literature regarding business and choice of biodiversity indicators. The aim of the project was to develop a methodology to enable companies to identify, quantify and monitor biodiversity. Research case studies interviews were undertaken with 10 collaborating organisations, selected to represent =best practice‘ examples and various situations. Information gained through case studies was combined with that from existing literature. This was used to develop a methodology for the selection of biodiversity indicators for company landholdings. The indicator selection methodology was discussed during a second stage of case study interviews with 4 collaborating companies. The information and opinions gained during this research was used to modify the methodology and provide the final biodiversity indicator selection methodology. The methodology was then tested through implementation at a mineral extraction site operated by a multi-national aggregates company. It was found that the methodology was a suitable process for implementation of global and national systems and conceptual frameworks at the practitioner scale. Further testing of robustness by independent parties is recommended to improve the system.

Large volume metrology process model:measurability analysis with integration of metrology classification model and feature-based selection model

Aircraft manufacturing industries are looking for solutions in order to increase their productivity. One of the solutions is to apply the metrology systems during the production and assembly processes. Metrology Process Model (MPM) (Maropoulos et al, 2007) has been introduced which emphasises metrology applications with assembly planning, manufacturing processes and product designing. Measurability analysis is part of the MPM and the aim of this analysis is to check the feasibility for measuring the designed large scale components. Measurability Analysis has been integrated in order to provide an efficient matching system. Metrology database is structured by developing the Metrology Classification Model. Furthermore, the feature-based selection model is also explained. By combining two classification models, a novel approach and selection processes for integrated measurability analysis system (MAS) are introduced and such integrated MAS could provide much more meaningful matching results for the operators. © Springer-Verlag Berlin Heidelberg 2010.