Combining deformable templates and neural networks for HCR

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Instantiating deformable models with a neural net

Deformable models are an attractive approach to recognizing objects which have considerable within-class variability such as handwritten characters. However, there are severe search problems associated with fitting the models to data which could be reduced if a better starting point for the search were available. We show that by training a neural network to predict how a deformable model should be instantiated from an input image, such improved starting points can be obtained. This method has been implemented for a system that recognizes handwritten digits using deformable models, and the results show that the search time can be significantly reduced without compromising recognition performance. © 1997 Academic Press.

Simulation process templates for e-business modelling

This paper argues the use of reusable simulation templates as a tool that can help to predict the effect of e-business introduction on business processes. First, a set of requirements for e-business modelling is introduced and modelling options described. Traditional business process mapping techniques are examined as a way of identifying potential changes. Whilst paper-based process mapping may not highlight significant differences between traditional and e-business processes, simulation does allow the real effects of e-business to be identified. Simulation has the advantage of capturing the dynamic characteristics of the process, thus reflecting more accurately the changes in behaviour. This paper shows the value of using generic process maps as a starting point for collecting the data that is needed to build the simulation and proposes the use of reusable templates/components for the speedier building of e-business simulation models.

Seeing edge blur: receptive fields as multiscale neural templates

Edge blur is an important perceptual cue, but how does the visual system encode the degree of blur at edges? Blur could be measured by the width of the luminance gradient profile, peak ^ trough separation in the 2nd derivative profile, or the ratio of 1st-to-3rd derivative magnitudes. In template models, the system would store a set of templates of different sizes and find which one best fits the `signature' of the edge. The signature could be the luminance profile itself, or one of its spatial derivatives. I tested these possibilities in blur-matching experiments. In a 2AFC staircase procedure, observers adjusted the blur of Gaussian edges (30% contrast) to match the perceived blur of various non-Gaussian test edges. In experiment 1, test stimuli were mixtures of 2 Gaussian edges (eg 10 and 30 min of arc blur) at the same location, while in experiment 2, test stimuli were formed from a blurred edge sharpened to different extents by a compressive transformation. Predictions of the various models were tested against the blur-matching data, but only one model was strongly supported. This was the template model, in which the input signature is the 2nd derivative of the luminance profile, and the templates are applied to this signature at the zero-crossings. The templates are Gaussian derivative receptive fields that covary in width and length to form a self-similar set (ie same shape, different sizes). This naturally predicts that shorter edges should look sharper. As edge length gets shorter, responses of longer templates drop more than shorter ones, and so the response distribution shifts towards shorter (smaller) templates, signalling a sharper edge. The data confirmed this, including the scale-invariance implied by self-similarity, and a good fit was obtained from templates with a length-to-width ratio of about 1. The simultaneous analysis of edge blur and edge location may offer a new solution to the multiscale problem in edge detection.

Grid-texture mechanisms in human vision:contrast detection of regular sparse micro-patterns requires specialist templates

Previous work has shown that human vision performs spatial integration of luminance contrast energy, where signals are squared and summed (with internal noise) over area at detection threshold. We tested that model here in an experiment using arrays of micro-pattern textures that varied in overall stimulus area and sparseness of their target elements, where the contrast of each element was normalised for sensitivity across the visual field. We found a power-law improvement in performance with stimulus area, and a decrease in sensitivity with sparseness. While the contrast integrator model performed well when target elements constituted 50–100% of the target area (replicating previous results), observers outperformed the model when texture elements were sparser than this. This result required the inclusion of further templates in our model, selective for grids of various regular texture densities. By assuming a MAX operation across these noisy mechanisms the model also accounted for the increase in the slope of the psychometric function that occurred as texture density decreased. Thus, for the first time, mechanisms that are selective for texture density have been revealed at contrast detection threshold. We suggest that these mechanisms have a role to play in the perception of visual textures.

Using generative models for handwritten digit recognition

We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable B-splines with Gaussian ``ink generators'' spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the Expectation Maximization (EM) algorithm that maximizes the likelihood of the model generating the data. This approach has many advantages. (1) After identifying the model most likely to have generated the data, the system not only produces a classification of the digit but also a rich description of the instantiation parameters which can yield information such as the writing style. (2) During the process of explaining the image, generative models can perform recognition driven segmentation. (3) The method involves a relatively small number of parameters and hence training is relatively easy and fast. (4) Unlike many other recognition schemes it does not rely on some form of pre-normalization of input images, but can handle arbitrary scalings, translations and a limited degree of image rotation. We have demonstrated our method of fitting models to images does not get trapped in poor local minima. The main disadvantage of the method is it requires much more computation than more standard OCR techniques.

Perceiving edge contrast

We have shown previously that a template model for edge perception successfully predicts perceived blur for a variety of edge profiles (Georgeson, 2001 Journal of Vision 1 438a; Barbieri-Hesse and Georgeson, 2002 Perception 31 Supplement, 54). This study concerns the perceived contrast of edges. Our model spatially differentiates the luminance profile, half-wave rectifies this first derivative, and then differentiates again to create the edge's 'signature'. The spatial scale of the signature is evaluated by filtering it with a set of Gaussian derivative operators. This process finds the correlation between the signature and each operator kernel at each position. These kernels therefore act as templates, and the position and scale of the best-fitting template indicate the position and blur of the edge. Our previous finding, that reducing edge contrast reduces perceived blur, can be explained by replacing the half-wave rectifier with a smooth, biased rectifier function (May and Georgeson, 2003 Perception 32 388; May and Georgeson, 2003 Perception 32 Supplement, 46). With the half-wave rectifier, the peak template response R to a Gaussian edge with contrast C and scale s is given by: R=Cp-1/4s-3/2. Hence, edge contrast can be estimated from response magnitude and blur: C=Rp1/4s3/2. Use of this equation with the modified rectifier predicts that perceived contrast will decrease with increasing blur, particularly at low contrasts. Contrast-matching experiments supported this prediction. In addition, the model correctly predicts the perceived contrast of Gaussian edges modified either by spatial truncation or by the addition of a ramp.

Perceiving edge blur: Gaussian-derivative filtering and a rectifying nonlinearity

We studied the visual mechanisms that encode edge blur in images. Our previous work suggested that the visual system spatially differentiates the luminance profile twice to create the `signature' of the edge, and then evaluates the spatial scale of this signature profile by applying Gaussian derivative templates of different sizes. The scale of the best-fitting template indicates the blur of the edge. In blur-matching experiments, a staircase procedure was used to adjust the blur of a comparison edge (40% contrast, 0.3 s duration) until it appeared to match the blur of test edges at different contrasts (5% - 40%) and blurs (6 - 32 min of arc). Results showed that lower-contrast edges looked progressively sharper. We also added a linear luminance gradient to blurred test edges. When the added gradient was of opposite polarity to the edge gradient, it made the edge look progressively sharper. Both effects can be explained quantitatively by the action of a half-wave rectifying nonlinearity that sits between the first and second (linear) differentiating stages. This rectifier was introduced to account for a range of other effects on perceived blur (Barbieri-Hesse and Georgeson, 2002 Perception 31 Supplement, 54), but it readily predicts the influence of the negative ramp. The effect of contrast arises because the rectifier has a threshold: it not only suppresses negative values but also small positive values. At low contrasts, more of the gradient profile falls below threshold and its effective spatial scale shrinks in size, leading to perceived sharpening.

Perceiving edge blur: linear filtering and a rectifying nonlinearity

We studied the visual mechanisms that encode edge blur in images. Our previous work suggested that the visual system spatially differentiates the luminance profile twice to create the 'signature' of the edge, and then evaluates the spatial scale of this signature profile by applying Gaussian derivative templates of different sizes. The scale of the best-fitting template indicates the blur of the edge. In blur-matching experiments, a staircase procedure was used to adjust the blur of a comparison edge (40% contrast, 0.3 s duration) until it appeared to match the blur of test edges at different contrasts (5% - 40%) and blurs (6 - 32 min of arc). Results showed that lower-contrast edges looked progressively sharper.We also added a linear luminance gradient to blurred test edges. When the added gradient was of opposite polarity to the edge gradient, it made the edge look progressively sharper. Both effects can be explained quantitatively by the action of a half-wave rectifying nonlinearity that sits between the first and second (linear) differentiating stages. This rectifier was introduced to account for a range of other effects on perceived blur (Barbieri-Hesse and Georgeson, 2002 Perception 31 Supplement, 54), but it readily predicts the influence of the negative ramp. The effect of contrast arises because the rectifier has a threshold: it not only suppresses negative values but also small positive values. At low contrasts, more of the gradient profile falls below threshold and its effective spatial scale shrinks in size, leading to perceived sharpening.

Template model for blur coding: the role of early nonlinearity in edge segmentation

We describe a template model for perception of edge blur and identify a crucial early nonlinearity in this process. The main principle is to spatially filter the edge image to produce a 'signature', and then find which of a set of templates best fits that signature. Psychophysical blur-matching data strongly support the use of a second-derivative signature, coupled to Gaussian first-derivative templates. The spatial scale of the best-fitting template signals the edge blur. This model predicts blur-matching data accurately for a wide variety of Gaussian and non-Gaussian edges, but it suffers a bias when edges of opposite sign come close together in sine-wave gratings and other periodic images. This anomaly suggests a second general principle: the region of an image that 'belongs' to a given edge should have a consistent sign or direction of luminance gradient. Segmentation of the gradient profile into regions of common sign is achieved by implementing the second-derivative 'signature' operator as two first-derivative operators separated by a half-wave rectifier. This multiscale system of nonlinear filters predicts perceived blur accurately for periodic and aperiodic waveforms. We also outline its extension to 2-D images and infer the 2-D shape of the receptive fields.

Molecularly imprinted polymers in the drug discovery process

Since molecularly imprinted polymers (MIPs) are designed to have a memory for their molecular templates it is easy to draw parallels with the affinity between biological receptors and their substrates. Could MIPs take the place of natural receptors in the selection of potential drug molecules from synthetic compound libraries? To answer that question this review discusses the results of MIP studies which attempt to emulate natural receptors. In addition the possible use of MIPs to guide a compound library synthesis towards a desired biological activity is highlighted. © 2005 Elsevier B.V. All rights reserved.

Towards a polymeric binding mimic for cytochrome CYP2D6

A series of fluorescent molecularly imprinted polymers has been prepared with a view to generating material capable of mimicking the binding characteristics of the metabolically important cytochrome isoform CYP2D6. Such polymers would have the possibility to form the sensing element in a high-throughput assay for the prediction of CYP2D6 affinity. The imprinted polymers possessed binding-dependent fluorescence. They re-bound their templates and various cross-reactivities were encountered for test compound/drug recognition. One polymer in particular exhibited a rational discrimination amongst the related synthetic templates and was reasonably successful in recognising CYP2D6 substrates from a drug panel. © 2005 Elsevier B.V. All rights reserved.

Spatially orthogonal chemical functionalization of a hierarchical pore network for catalytic cascade reactions

The chemical functionality within porous architectures dictates their performance as heterogeneous catalysts; however, synthetic routes to control the spatial distribution of individual functions within porous solids are limited. Here we report the fabrication of spatially orthogonal bifunctional porous catalysts, through the stepwise template removal and chemical functionalization of an interconnected silica framework. Selective removal of polystyrene nanosphere templates from a lyotropic liquid crystal-templated silica sol–gel matrix, followed by extraction of the liquid crystal template, affords a hierarchical macroporous–mesoporous architecture. Decoupling of the individual template extractions allows independent functionalization of macropore and mesopore networks on the basis of chemical and/or size specificity. Spatial compartmentalization of, and directed molecular transport between, chemical functionalities affords control over the reaction sequence in catalytic cascades; herein illustrated by the Pd/Pt-catalysed oxidation of cinnamyl alcohol to cinnamic acid. We anticipate that our methodology will prompt further design of multifunctional materials comprising spatially compartmentalized functions.

Mass transfer characteristics of two-aqueous-phase liquid-liquid mixtures

Mass transfer rates were studied using the falling drop method. Cibacron Blue 3 GA dye was the transferring solute from the salt phase to the PEG phase. Measurements were undertaken for several concentrations of the dye and the phase-forming solutes and with a range of different drop sizes, e.g. 2.8, 3.0 and 3.7 mm. The dye was observed to be present in the salt phase as finely dispersed solids but a model confirmed that the mass transfer process could still be described by an equation based upon the Whitman two-film model. The overall mass transfer coefficient increased with increasing concentration of the dye. The apparent mass transfer coefficient ranged from 1 x 10-5 to 2 x 10 -4 m/s. Further experiments suggested that mass transfer was enhanced at high concentration by several mechanisms. The dye was found to change the equilibrium composition of the two phases, leading to transfer of salt between the drop and continuous phases. It also lowered the interfacial tension (i.e. from 1.43 x 10-4 N/m for 0.01% w/w dye concentration to 1.07 x 10-4 N/m for 0.2% w/w dye concentration) between the two phases, which could have caused interfacial instabilities (Marangoni effects). The largest drops were deformable, which resulted in a significant increase in the mass transfer rate. Drop size distribution and Sauter mean drop diameter were studied on-line in a 1 litre agitated vessel using a laser diffraction technique. The effects of phase concentration, dispersed phase hold-up and impeller speed were investigated for the salt-PEG system. An increase in agitation speed in the range 300 rpm to 1000 rpm caused a decrease in mean drop diameter, e.g. from 50 m to 15 m. A characteristic bimodal drop size distribution was established within a very short time. An increase in agitation rate caused a shift of the larger drop size peak to a smaller size.

A configuration framework of manufacturing strategy paradigm in state owned enterprises within China's petrochemical industry:building context-embedded configurations for the paradigm of strategic manufacturing management from exploratory case studies

The role of the production system as a key determinant of competitive performance of business operations- has long been the subject of industrial organization research, even predating the .explicit conceptua1isation of manufacturing, strategy in the literature. Particular emergent production issues such as the globalisation of production, global supply chain management, management of integrated manufacturing and a growing e~busjness environment are expected to critically influence the overall competitive performance and therefore the strategic success of the organization. More than ever, there is a critical need to configure and improve production system and operations competence in a strategic way so as to contribute to the long-term competitiveness of the organization. In order to operate competitively and profitably, manufacturing companies, no matter how well managed, all need a long-term 'strategic direction' for the development of operations competence in order to consistently produce more market value with less cost towards a leadership position. As to the long-term competitiveness, it is more important to establish a dynamic 'strategic perspective' for continuous operational improvements in pursuit of this direction, as well as ongoing reviews of the direction in relation to the overall operating context. However, it also clear that the 'existing paradigm of manufacturing strategy development' is incapable of adequately responding to the increasing complexities and variations of contemporary business operations. This has been factually reflected as many manufacturing companies are finding that methodologies advocated in the existing paradigm for developing manufacturing strategy have very limited scale and scope for contextual contingency in empirical application. More importantly, there has also emerged a deficiency in the multidimensional and integrative profile from a theoretical perspective when operationalising the underlying concept of strategic manufacturing management established in the literature. The point of departure for this study was a recognition of such contextual and unitary limitations in the existing paradigm of manufacturing strategy development when applied to contemporary industrial organizations in general, and Chinese State Owned Enterprises (SOEs) in particular. As China gradually becomes integrated into the world economy, the relevance of Western management theory and its paradigm becomes a practical matter as much as a theoretical issue. Since China markedly differs from Western countries in terms of culture, society, and political and economic systems, it presents promising grounds to test and refine existing management theories and paradigms with greater contextual contingency and wider theoretical perspective. Under China's ongoing programmes of SOE reform, there has been an increased recognition that strategy development is the very essence of the management task for managers of manufacturing companies in the same way as it is for their counterparts in Western economies. However, the Western paradigm often displays a rather naive and unitary perspective of the nature of strategic management decision-making, one which largely overlooks context-embedded factors and social/political influences on the development of manufacturing strategy. This thesis studies the successful experiences of developing manufacturing strategy from five high-performing large-scale SOEs within China’s petrochemical industry. China’s petrochemical industry constitutes a basic heavy industrial sector, which has always been a strategic focus for reform and development by the Chinese government. Using a confirmation approach, the study has focused on exploring and conceptualising the empirical paradigm of manufacturing strategy development practiced by management. That is examining the ‘empirical specifics’ and surfacing the ‘managerial perceptions’ of content configuration, context of consideration, and process organization for developing a manufacturing strategy during the practice. The research investigation adopts a qualitative exploratory case study methodology with a semi-structural front-end research design. Data collection follows a longitudinal and multiple-case design and triangulates case evidence from sources including qualitative interviews, direct observation, and a search of documentations and archival records. Data analysis follows an investigative progression from a within-case preliminary interpretation of facts to a cross-case search for patterns through theoretical comparison and analytical generalization. The underlying conceptions in both the literature of manufacturing strategy and related studies in business strategy were used to develop theoretical framework and analytical templates applied during data collection and analysis. The thesis makes both empirical and theoretical contributions to our understanding of 'contemporary management paradigm of manufacturing strategy development'. First, it provides a valuable contextual contingency of the 'subject' using the business setting of China's SOEs in petrochemical industry. This has been unpacked into empirical configurations developed for its context of consideration, its content and process respectively. Of special note, a lean paradigm of business operations and production management discovered at case companies has significant implications as an emerging alternative for high-volume capital intensive state manufacturing in China. Second, it provides a multidimensional and integrative theoretical profile of the 'subject' based upon managerial perspectives conceptualised at case companies when operationalising manufacturing strategy. This has been unpacked into conceptual frameworks developed for its context of consideration, its content constructs, and its process patterns respectively. Notably, a synergies perspective towards the operating context, competitive priorities and competence development of business operations and production management has significant implications for implementing a lean manufacturing paradigm. As a whole, in so doing, the thesis established a theoretical platform for future refinement and development of context-specific methodologies for developing manufacturing strategy.