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Winchester Science Centre in Southern England hosts a significant number of informative exhibits pertaining to principles of acoustics; one such example being on the subject of euphony. In a period of time between the first and third COVID lockdowns, visitors to this exhibit were asked what sounds they noticed more or less of, and what their favourite and least favourite sounds were during this period.The results are shared with two-fold benefit: to assist the public perception of noise and euphony, and to capture the aural response of the public to the 2020 COVID restrictions.This paper explores these results, but also the potential for conducting research from a larger cohort of visitors using a citizen science approach to deliver big data sets, to further explore the perception of sound in the environment with regard to its context.

The stoichiometric coupling of carbon to limiting nutrients in marine phytoplankton determines that of the main biogeochemical cycles through the process of biomass production by the phytoplankton. While clear links between phytoplankton stoichiometry and environmental drivers have been identified, the nature and direction of these links, as well as the underlying physiological and ecological mechanisms, remain uncertain. Here we compare the predictions of a well-constrained mechanistic model of plankton ecophysiology to multiple observational data sets to investigate the specific case of the C:N phytoplankton stoichiometry in the North-Atlantic. We show that N availability and temperature emerge as the main drivers of phytoplankton stoichiometry. The biological mechanisms involved however vary depending on the spatiotemporal scale and region considered, leading to opposite predictions regarding the evolution of phytoplankton primary productivity in response to environmental changes. At low to intermediate latitudes phytoplankton stoichiometry is predominantly driven by ecoevolutionary shifts in the functional composition of the phytoplankton communities while phytoplankton stoichiometric plasticity in response to dropping temperatures and increased grazing pressure dominates at higher latitudes. Those results shine a new light on what currently influences the circulation of elements through marine ecosystems but also have great implications regarding the evolution of oceans’ primary productivity and of the main biogeochemical cycles under a regime of climate change.

Course design institutes (CDIs), which systematically guide faculty through the (re)design of courses, often transpire in an intensive residency or learning community format. Little is known, to date, of the long-term impact of such initiatives, particularly in the context of faculty motivation. This longitudinal study explores changes in faculty attitudes toward teaching, offering insight into the multifaceted gains and limiting factors influencing motivation as conceptualized by the expectancy-value-cost model (Barron & Hulleman, 2015). Findings reveal that CDI engagement bolsters the value placed on teaching, but arrives at a noteworthy cost to faculty. Implications for CTLs and instructional faculty are explored.

The magnitude and efficiency of particulate carbon export from the ocean surface depends not only on net primary production (NPP) but also on how carbon is consumed, respired, and repackaged by organisms. We contend that several of these processes can be captured by the size spectrum of the plankton community. However, most global models have relatively simple food-web structures that are unable to generate plankton sizespectra. Moreover, the life-cycles of multicellular zooplankton are typically not resolved, restricting the ability of models to represent time-lags that are known to impact carbon export and its efficiency (pe-ratio). Here, we use a global mechanistic size-spectrum model of the marine plankton community to investigate how particulate export and pe-ratio relate to the community size spectrum, community composition, and time-lags between predators and prey. The model generates emergent food-webs with associated size distributions for organisms and detrital particles. To resolve time-lags between phytoplankton and zooplankton, we implement the life-cycle of multicellular zooplankton (here represented by copepods). The simulation successfully captures observed patterns in biomass and energy fluxes across regions. We find that carbon export correlates best with copepod biomass and trophic level, whereas the pe-ratio correlates best with the exponent of the size spectrum and sea surface temperature (SST). Community metrics performed better than NPP or SST for both deep export and pe-ratio. Time-lags between phytoplankton and copepods did not strongly affect export or pe-ratio. We conclude by discussing how can we reconcile size-spectrum theory with field sampling.Plain Language SummaryPlankton are tiny but extremely abundant aquatic organisms. Plankton lock CO2 away from the atmosphere as they sink to the deep ocean, where carbon can be stored for hundreds of years. However, how much carbon is locked away and for how long depends on how organisms eat, defecate, and respire. We argue that these processes are reflected in the size composition of the plankton community. The size composition shows a clear relationship between the number of organisms and their body-size. The steepness of this “size-abundance relationship” describes the balance between small vs. large organisms, and has been argued to reflect how energy is transferred from small to large organisms. Since large organisms create fast-sinking particles, the size-abundance relationship could be used to estimate how much carbon is being stored in the deep ocean. Here we use a computer simulation of the global plankton community to investigate how the removal of carbon relates to the plankton community and the steepness of the sizeabundance relationship. The model successfully captures patterns observed in nature. We found that the size-abundance relationship, together with the quantity of large zooplankton better explained carbon export than other measures typically used, such as photosynthesis and temperature.Key PointsWe use a global mechanistic size-spectrum model to investigate the relation between particulate export and plankton community metrics.We find a good correlation between export efficiency and the exponent of the size spectrum.Total carbon export correlated well with copepod biomass and trophic level of active copepods in the model.

Background Maternal cortisol during pregnancy has the potential to influence rapidly developing fetal brain systems that are commonly altered in neurodevelopmental and psychiatric disorders. Research examining maternal cortisol concentrations across pregnancy and offspring neurodevelopment proximal to birth is needed to advance understanding in this area and lead to insight into the etiology of these disorders. Methods Participants were 70 adult women recruited during early pregnancy and their infants born after 34 weeks gestation. Maternal cortisol concentrations were assessed serially over 4 days in early, mid, and late gestation. Resting state functional connectivity magnetic resonance imaging of the neonatal amygdala was examined. Mothers reported on children’s internalizing behavior problems at 24 months of age. Results Maternal cortisol concentrations during pregnancy were significantly associated with neonatal amygdala connectivity in a sex-specific manner. Elevated maternal cortisol was associated with stronger amygdala connectivity to brain regions involved in sensory processing and integration, as well as the default mode network in girls, and with weaker connectivity to these brain regions in boys. Elevated maternal cortisol was associated with higher internalizing symptoms in girls only, and this association was mediated by stronger neonatal amygdala connectivity. Conclusions Normative variation in maternal cortisol during pregnancy is associated with the coordinated functioning of the amygdala soon after birth in a sex-specific manner. The identified pathway from maternal cortisol to higher internalizing symptoms in girls via alterations in neonatal amygdala connectivity may be relevant for the etiology of sex differences in internalizing psychiatric disorders, which are more prevalent in women.

Context Cardiometabolic conditions increase in midlife, but early customized prevention strategies are not established for such women. Objective To characterize and identify factors longitudinally related to constellations of cardiometabolic risk components in multiracial/ethnic women in midlife. Design We conducted a prospective, longitudinal, multiethnic cohort study of 3003 midlife women undergoing menopausal transition (MT). Metabolic syndrome (MetS) was defined as having at least three of five components: high fasting triglyceride (hTG) level, low high-density lipoprotein cholesterol (lHDL-C) level, high fasting plasma glucose (hGluc) level, large waist circumference (abdominal obesity; Ob), and hypertension (HTN). We described the patterns of constellations and estimated hazard ratios (HRs) for constellations at (i) incident MetS and (ii) recovery from MetS, using multivariable-adjusted Cox regression. Setting Seven US sites. Participants In all, 1412 non-Hispanic white, 851 black, 272 Japanese, 237 Hispanic, and 231 Chinese women. Exposures Race/ethnicity, lifestyle factors, and MT stage. Main outcomes measures Cardiometabolic constellations, incident MetS, and MetS recovery. Results Central obesity was the most frequent component. Having no components was the most frequent (31%) baseline constellation. Physical activity (HR = 1.68; 95% CI: 1.06, 2.68) and lower caloric intake (HR = 0.96; 95% CI: 0.93, 0.99 per 100 cal/d) were associated with recovery from MetS. Ob/hTG/lHDL-C (18%), Ob/HTN/lHDL-C (16%), and Ob/HTN/hGluc (14%) were frequent incident constellations. Physically active women had 26% to 62% lower hazards of incident MetS than inactive women. Conclusions Modifiable lifestyle behaviors were related to recovery from MetS and decreased risk of the most frequent MetS constellations in midlife women.

Neutrophils are implicated in the pathogenesis of atherosclerosis but are seldom detected in atherosclerotic plaques. We investigated whether neutrophil-derived microvesicles may influence arterial pathophysiology. Here we report that levels of circulating neutrophil microvesicles are enhanced by exposure to a high fat diet, a known risk factor for atherosclerosis. Neutrophil microvesicles accumulate at disease-prone regions of arteries exposed to disturbed flow patterns, and promote vascular inflammation and atherosclerosis in a murine model. Using cultured endothelial cells exposed to disturbed flow, we demonstrate that neutrophil microvesicles promote inflammatory gene expression by delivering miR-155, enhancing NF-κB activation. Similarly, neutrophil microvesicles increase miR-155 and enhance NF-κB at disease-prone sites of disturbed flow in vivo. Enhancement of atherosclerotic plaque formation and increase in macrophage content by neutrophil microvesicles is dependent on miR-155. We conclude that neutrophils contribute to vascular inflammation and atherogenesis through delivery of microvesicles carrying miR-155 to disease-prone regions., The role of neutrophils in the development of atherosclerosis has long been an enigma, with few neutrophils detected within the plaque. Here, the authors show that microvesicles released from neutrophils increase vascular inflammation and enhance atherosclerotic plaque formation through delivery of miR-155.

Background Although the linkage between psychological stress and cortisol is believed to mediate the association of stress with health outcomes, several studies have been unable to demonstrate this association. We suggest this inability may be a consequence of limitations in the measurement approach and/or reliance on analytic strategies that focus on associations across, rather than within individuals. The link between psychological stress and cortisol is of particular interest in the context of pregnancy and fetal development. Using an ecological momentary assessment (EMA) design, we examined the association between psychological stress and cortisol at the between- and the within-person level. Methods 152 participants completed a 4-day long EMA protocol serially in early, mid and late pregnancy to provide momentary stress appraisals (average of 150 measures/subject) and saliva samples (average of 55 samples/subject) for quantification of cortisol. The association between stress and cortisol was estimated using linear mixed models. Results After accounting for the effects of key determinants of variation in cortisol, momentary stress was significantly and positively associated with cortisol at the within-person level (B = .030, p = .031), but not at the between-person level. No association was evident for traditional retrospective measures of stress with cortisol at either the between- or the within-person level. Conclusions Our study highlights the value of EMA methods and linear mixed-modeling approaches in linking maternal psychological and physiological states across pregnancy. These findings may have important implications for the development of personalized risk identification and “just-in-time” intervention strategies to optimize maternal and child health.

Low value and high volume buried infrastructure assets in the water distribution network are typically less well understood and often sub-optimally managed in comparison to more critical or higher value assets. This is despite attracting an estimated yearly expenditure from water utilities operating in the developed world in excess of £4.42 billion per annum. To address this problem the authors have developed a novel deterioration modelling framework founded on latest geospatial technologies and statistical analysis. The modelling framework is specifically applied to truly small diameter water distribution assets of 25–50 mm diameter. Reliability curves are developed from failure data provided by two UK Water Companies that have captured communication pipe failure records since 2001. Failure projections based on deterioration modelling curves are compared and contrasted to demonstrate the robustness of this modelling approach. A high degree of accuracy is observed for all pipe materials achieving ...

Background: Maternal cortisol during pregnancy has the potential to influence rapidly developing fetal brain systems that are commonly altered in neurodevelopmental and psychiatric disorders. Research examining maternal cortisol concentrations across pregnancy and offspring neurodevelopment proximal to birth is needed to advance understanding in this area and lead to insight into the etiology of these disorders. Methods: Participants were 70 adult women recruited during early pregnancy and their infants born after 34 weeks gestation. Maternal cortisol concentrations were assessed serially over 4 days in early, mid, and late gestation. Resting state functional connectivity magnetic resonance imaging of the neonatal amygdala was examined. Mothers reported on children's internalizing behavior problems at 24 months of age. Results: Maternal cortisol concentrations during pregnancy were significantly associated with neonatal amygdala connectivity in a sex-specific manner. Elevated maternal cortisol was associated with stronger amygdala connectivity to brain regions involved in sensory processing and integration, as well as the default mode network in girls, and with weaker connectivity to these brain regions in boys. Elevated maternal cortisol was associated with higher internalizing symptoms in girls only, and this association was mediated by stronger neonatal amygdala connectivity. Conclusions: Normative variation in maternal cortisol during pregnancy is associated with the coordinated functioning of the amygdala soon after birth in a sex-specific manner. The identified pathway from maternal cortisol to higher internalizing symptoms in girls via alterations in neonatal amygdala connectivity may be relevant for the etiology of sex differences in internalizing psychiatric disorders, which are more prevalent in women.

Understanding the mechanisms driving lineage-specific evolution in both primates and rodents has been hindered by the lack of sister clades with a similar phylogenetic structure having high-quality genome assemblies. Here, we have created chromosome-level assemblies of the

This paper explores the potential savings that can be realized through the identification of optimal asset management business policies for small-diameter (25–50 mm) water communication pip...

The WTFast's Gamers Private Network (GPN®) is a client/server solution that makes online games faster. GPN® connects online video-game players with a common game service across a wide-area network. Online games are interactive competitions by individual players who compete in a virtual environment. Response time, latency and its predictability are keys to GPn® success and runs against the vast complexity of internet-wide systems. We have built an experimental network of virtualized GPN® components so as to carefully measure the statistics of latency for distributed Minecraft games and to do so in a controlled laboratory environment. This has led to a better understanding of the coupling between parameters such as: the number of players, the subset of players that are idle or active, the volume of packets exchanged, the size of packets, latency to and from the game servers, and time-series for most of those parameters. In this paper we investigate specific game traffic connection types and show how we could collect very large-scale data sets of gaming metadata to control gaming network performance in real time. Such investigation and conducted experimentations will improve quality of service for GPN® systems and their reliability.

It is generally accepted by Ofwat, the water industry regulator for England and Wales, that the current investment planning system whereby capital and operational expenditure are accounted for separately is complex and burdensome. In a move towards realising a total expenditure approach, a previously successful sewer rehabilitation optimisation model has been adapted to provide a mechanism for users to evaluate the trade-offs that exist between the capital and operational benefits associated with different sewer rehabilitation schemes. A series of geographic information system tools has been integrated within the model to help prioritise high-benefit sewer rehabilitation schemes by evaluating the potential serviceability improvements that can be realised in addition to the purely structural condition improvements. As a result, the new sewer rehabilitation model can be referred to as a strategic decision support tool that is capable of helping sewerage engineers and planners in the evaluation of different intervention programmes of work. The benefits of adopting this approach are demonstrated in a UK sewerage case study that uses a multi-objective genetic algorithm to consider the three-way trade-off that exists between minimising investment cost against maximising asset life (capital benefit) compared with proactively addressing serviceability problems (operational benefit).

In October 2011, Section 105A (S105A) of the Water Act of 2003 transferred the ownership of previously privately owned sewers to the ten water and sewerage companies operating in England and Wales. In light of this recent legislative change, this paper discusses the asset management challenges associated with the private sewer transfer before exploring a decision-making framework used by South West Water to establish a more accurate understanding of the extent and likely condition of their newly transferred network. The framework has allowed South West Water to initiate a proactive asset management programme with the aim of addressing the deteriorating condition of these assets while also tackling their associated serviceability performance. Initially, a number of geospatial (GIS) tools are used to provide an estimate of the likely extent of the transferred network before a well-established public sewer deterioration model is used to predict the condition and operational performance of these S105A assets over time. The outputs from this study have better equipped decision makers with information surrounding the deterioration and collapse rates of these newly transferred sewers over time. The information has been used to help formulate strategic business planning decisions, for example, by using cost vs. benefit analysis tools to model different investment scenarios across the network.

In this chapter we consider the application of Bayesian methods to several statistical problems that arise in the health sciences. We show that the Bayesian approach is straightforward to apply for a broad array of commonly encountered statistical problems, as well as many that are more complex, using freely available statistical software. We begin by contrasting frequentist estimation and inferential procedures with the Bayesian framework. Approaches to Bayesian inference for one- and two-sample Binomial problems are illustrated, along with extensions to logistic regression and hierarchical linear mixed effects models. Throughout, examples based on real applications are presented and the importance of specifying realistic and meaningful prior distributions, along with the scientific interpretability and importance of inferential results, is emphasized.

Background: Atherosclerosis is the most common underlying cause of cardiovascular disease. Many cells types are involved in the initiation and progression of atherosclerotic plaques but, until recently, neutrophils were not considered to be among them. Despite their rare detection in plaques, neutrophil depletion has been shown to reduce plaque formation and monocyte recruitment to the vessel wall. Hypothesis: Neutrophils may influence plaque initiation and progression through the formation of microvesicles. Aim: To determine the role of neutrophil-derived-microvesicles in the early stage of atherosclerosis. Methods and Results: Mouse peripheral blood neutrophils were isolated by negative magnetic separation. Following stimulation for 1h with fMLP (3x105M), supernatants were subjected to differential centrifugation (twice at 300g for 6 min followed by 20,000g for 20 mins) to isolate the microvesicle population. Microvesicles were quantified using flow cytometry calibrated with MegaMix and Spherocount beads. Apo E mice were fed a Western diet for 6 weeks and injected twice a week with PBS or neutrophil microvesicles (4.52x106 ± 3.05x105) isolated from control mice. After 6 weeks, blood was collected for neutrophil and microvesicle quantification. The aorta was dissected and stained by conventional procedures using Oil Red. Areas of plaque formation was determined using computer based imaging and semi-quantitative scoring. Injection with microvesicles increased circulating neutrophil number (PBS treated = 67.3 ± 3.9 neutrophils per μl; microvesicle treated = 88.25 ± 4.0 neutrophils per μl, P = 0.031; n = 3) and neutrophil reactivity with respect to microvesicle formation (PBS treated = 4.5 ± 0.8 microvesicles per neutrophil; microvesicle treated = 8.3±0.7 microvesicles per neutrophil, P = 0.0249; n = 3). After 6 weeks, the plaque formation was more advanced in the MVs group (% lesion area in the arch in mice injected with PBS = 0.4563 ± 0.09452, with MVs = 1.643 ± 0.3891, P = 0.0118, n=7). Conclusion: Neutrophil microvesicles induce increased plaque formation and increase circulating neutrophil levels in a mouse model of atherosclerosis. This suggests neutrophils may be involved in atherogenesis through the release of microvesicles.

Background Many cell types are involved in the initiation and progression of atherosclerosis but neutrophils are rarely considered to be among them. However, despite their infrequent detection in plaques, neutrophils have been shown to facilitate the movement of monocytes into the vessel wall and increase plaque growth. One possible mechanism for this is through release of microvesicles that contain cargo such as microRNAs (miRNAs). Hypothesis Neutrophils influence plaque initiation and progression by releasing miRNA-containg microvesicles. Aim To determine whether neutrophil microvesicles i) influence atherosclerotic plaque development and ii) contain miRNAs relevant to atherosclerosis. Methods and results Mouse blood neutrophils were stimulated for 1h with fMLP (3 × 10 5 M) and supernatants subjected to differential centrifugation (twice at 300 g for 6 min followed by 20,000 g for 20 mins). Microvesicles were quantified using calibrated flow cytometry. ApoE –/– mice were fed Western diet for 6 weeks and injected twice a week with sterile PBS or microvesicles (4 × 10 6 ) isolated from wild type mice. After 6 weeks aortae were dissected and stained with Oil Red-O to identify areas of plaque formation. To detect miRNA, human neutrophils were isolated and stimulated with fMLP (10 –5 M), AcLDL (20 µg/ml) or PBS for 2 h and microvesicles pelleted by differential centrifugation. MiRNA extraction and quantitative RT-PCR were performed in microvesicles, HUVEC and HUVEC incubated with microvesicles for 2 h. The number of neutrophils isolated from mice injected with microvesicles was increased (PBS = 67.3 ± 3.9/μl; microvesicles = 88.25 ± 4.0/μl, P = 0.031; n = 3 in each group). In addition neutrophil reactivity with respect to microvesicle formation was also increased (PBS = 4.5 ± 0.8 microvesicles per neutrophil; microvesicle = 8.3 ± 0.7 microvesicles per neutrophil, P = 0.0249; n = 3). Consequently plaque formation was more advanced in mice injected with microvesicles (% lesion area in aortic arch: PBS = 0.4563 ± 0.09452; microvesicles = 1.643 ± 0.3891, P = 0.0118; n = 7 in each group). MiR-223 and miR-150, found to be constitutively expressed in microvesicles and not in HUVEC, are involved in inflammation and atherosclerosis. These miRNAs were increased in after fMLP and AcLDL stimulation compared to PBS and were found to be significantly increased in HUVEC after 2h incubation with microvesicles (miR-223: 1.74 ± 0.44, P = 0.0305; 2.68 ± 0.50, P = 0.0034 and 6.79 ± 0.99, P = 0.0012 fold increase with microvesicles from PBS, fMLP and AcLDL stimulated neutrophils respectively and for miR-150: 3.79 ± 0.21, P = 0.0131 and 3.89 ± 0.78, P = 0.0109 fold increase with microvesicles from fMLP and AcLDL stimulated neutrophils respectively; n = 5 for each group). Conclusion Neutrophil microvesicles induce increased plaque formation and contain miRNA that may be transferred to endothelial cells suggesting neutrophils may be involved in atherogenesis through the release of microvesicles.

Based on the 1922-1991 Terman data of children with high ability, I investigate the effects of childhood cognitive skills, socioemotional skills, and post-compulsory education on longevity using factor-analytic methodology similar to that in Heckman et al. (2006). For men, I find strong effects of socioemotional skills and education on longevity and an interaction between education and the skills. In particular, the average treatment effect of a Bachelor’s degree on life expectancy is 8.6 additional years of life, is worth for a statistical man as large as $810,000 of 2012 US dollars as a conservative estimate. Results for the effect of education are in line with Buckles et al. (2013) paper that is based on IV approach. One decile increases in childhood Conscientiousness and Extraversion lead to increases in life expectancy by 0.75 and 0.63 years worth 81 and 69 thousand US dollars. For women, who are born around 1910 and live longer than educated men, I find no statistically significant effects of education and socioemotional skills on longevity.

We present a method for estimating the 3D shape of an object from a sequence of images captured by a hand-held device. The method is well suited to augmented reality applications in that minimal user interaction is required, and the models generated are of an appropriate form. The method proceeds by segmenting the object in every image as it is captured and using the calculated silhouette to update the current shape estimate. In contrast to previous silhouettebased modelling approaches, however, the segmentation process is informed by a 3D prior based on the previous shape estimate. A voting scheme is also introduced in order to compensate for the inevitable noise in the camera position estimates. The combination of the voting scheme with the closed-loop segmentation process provides a robust and flexible shape estimation method. We demonstrate the approach on a number of scenes where segmentation without a 3D prior would be challenging.

Automatic placement of surveillance cameras in arbitrary buildings is a challenging task, and also one that is essential for efficient deployment of large scale surveillance networks. Existing approaches for automatic camera placement are either limited to a small number of cameras, or constrained in terms of the building layouts to which they can be applied. This paper describes a new method for determining the best placement for large numbers of cameras within arbitrary building layouts. The method takes as input a 3D model of the building, and uses a genetic algorithm to find a placement that optimises coverage and (if desired) overlap between cameras. Results are reported for an implementation of the method, including its application to a wide variety of complex buildings, both real and synthetic.

We present an interactive image-based modelling method for generating 3D models within an augmented reality system. Applying real time camera tracking, and high-level automated image analysis, enables more powerful modelling interactions than have previously been possible. The result is an immersive modelling process which generates accurate three dimensional models of real objects efficiently and effectively. In demonstrating the modelling process on a range of indoor and outdoor scenes, we show the flexibility it offers in enabling augmented reality applications in previously unseen environments.

This paper describes an interactive method for generating a model of a scene from image data. The method uses the camera parameters and point cloud typically generated by structure-and-motion estimation as a starting point for developing a higher level model, in which the scene is represented as a set of parameterised shapes. Classes of shapes are represented in a hierarchy which defines their properties but also the method by which they are localised in the scene, using a combination of user interaction, sampling and optimisation. Relations between shapes, such as adjancency and alignment, are also specified interactively. The method thus provides a modelling process which requires the user to provide only high level scene information, the remaining detail being provided through geometric analysis of the image set. This mixture of guided, yet automated, fitting techniques allows a non-expert user to rapidly and intuitively create a visually convincing 3D model of a real world scene from an image set.

VideoTrace is a system for interactively generating realistic 3D models of objects from video---models that might be inserted into a video game, a simulation environment, or another video sequence. The user interacts with VideoTrace by tracing the shape of the object to be modelled over one or more frames of the video. By interpreting the sketch drawn by the user in light of 3D information obtained from computer vision techniques, a small number of simple 2D interactions can be used to generate a realistic 3D model. Each of the sketching operations in VideoTrace provides an intuitive and powerful means of modelling shape from video, and executes quickly enough to be used interactively. Immediate feedback allows the user to model rapidly those parts of the scene which are of interest and to the level of detail required. The combination of automated and manual reconstruction allows VideoTrace to model parts of the scene not visible, and to succeed in cases where purely automated approaches would fail.

We propose a method for interactively generating a model-based reconstruction of a scene from a set of images. The method facilitates the fitting of multiple object models to the data in a manner that provides the best overall fit to the image set. This requires that models are not fit independently, but rather collectively, each potentially impacting upon the fit of the other.

This paper describes a method for generating a model-based reconstruction of a scene from image data. The method uses the camera models and point cloud typically generated by a structure-and-motion process as a starting point for developing a higher level model of the scene. The method relies on the user to provide a minimal amount of structural seeding information from which more complex geometry is extrapolated. The regularity typically present in man-made environments is used to minimise the interaction required, but also to improve the accuracy of fit. We demonstrate model based reconstructions obtained using this method.