Facial image processing

[No abstract available

ChoiceNet: CNN learning through choice of multiple feature map representations

From Springer Nature via Jisc Publications RouterHistory: received 2020-10-12, accepted 2021-06-03, registration 2021-06-25, pub-electronic 2021-07-11, online 2021-07-11, pub-print 2021-11Publication status: PublishedFunder: Toyota Motor Europe; doi: http://dx.doi.org/10.13039/501100010577Funder: Faculty of Science and Engineering, University of Manchester (GB); Grant(s): 1Abstract: We introduce a new architecture called ChoiceNet where each layer of the network is highly connected with skip connections and channelwise concatenations. This enables the network to alleviate the problem of vanishing gradients, reduces the number of parameters without sacrificing performance and encourages feature reuse. We evaluate our proposed architecture on three independent tasks: classification, segmentation and facial landmark localisation. For this, we use benchmark datasets such as ImageNet, CIFAR-10, CIFAR-100, SVHN CamVid and 300W

Machine-learning derived acetabular dysplasia and cam morphology are features of severe hip osteoarthritis : findings from UK Biobank

Acknowledgements and disclosures The authors would like to thank Dr Martin Williams, Consultant Musculoskeletal Radiologist North Bristol NHS Trust, who provided substantial training and expertise in osteophyte assessment on DXA images. This research has been conducted using the UK Biobank Resource (application number 17295). Financial Support: RE, MF, FS are supported, and this work is funded by a Wellcome Trust collaborative award (reference number 209233). BGF is supported by a Medical Research Council (MRC) clinical research training fellowship (MR/S021280/1). CL was funded by the MRC, UK (MR/S00405X/1) as well as a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (223267/Z/21/Z). NCH acknowledges support from the MRC and NIHR Southampton Biomedical Research Centre, University of Southampton, and University Hospital Southampton. This research was funded in whole, or in part, by the Wellcome Trust [Grant number 223267/Z/21/Z]. NCH has received consultancy, lecture fees and honoraria from Alliance for Better Bone Health, AMGEN, MSD, Eli Lilly, Servier, UCB, Shire, Consilient Healthcare, Kyowa Kirin and Internis Pharma. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.Peer reviewedPublisher PD

On Environmental Grounds : Outdoor Recreation, Place Relations and Environmental Sustainability

This thesis examines the relationship between outdoor recreation and environmental concern as part of the wider issue of environmental sustainability in late-modern societies. It includes studies of environmentalists’ (that is environmentally committed individuals’) preferences and motivations with regard to outdoor recreation, and covers the inquiries of whether and how outdoor recreation can influence levels of environmental concern. The questions addressed are how environmentalists engage in outdoor recreation, with what motives, and whether participation in outdoor recreation can influence levels of environmental concern. Empirically, the thesis is based on a mixed methods approach, including analyses of data from a national survey on outdoor recreation and a qualitative case study of the organization Nature and Youth Sweden (Fältbiologerna). Theoretically, it is based on the concepts of place, habitus and field. Study results show that environmentally committed individuals favor participation in appreciative activities in areas perceived as pristine, preferably away from urban environments. Motivations refer to these preferences, but also to aspects of discursive context, social identity and social position. These aspects are also found to be crucial regarding the influence of outdoor recreation on environmental concern. Thus, study results also show a lack of support for environmental concern as an automatic outcome of outdoor recreation. It is rather a combination of interconnected conditions referred to as: favorable place relations, adequate outdoor experience and appropriate social context. The thesis contributes to new knowledge on the relationships and connections between outdoor recreation and environmental sustainability. While the results are of importance with regard to planning for outdoor recreation and development of nature-based tourism, they are of particular interest for environmental organizations, schools and other institutions working for a more sustainable society.This thesis examines the relationship between outdoor recreation and environmental concern as part of the wider quest for environmental sustainability in late-modern societies. Generally, outdoor recreation contributes to an increased use of resources and a growing impact on the environment. At the same time, outdoor recreation is also part of a wider narrative of fostering environmental concern, where forms of nature encounter are seen as potential pathways to pro-environmental attitudes and behavior. Thus, the thesis addresses themes of recreational participation and preferences among environmentalists - and the inquiry into the ways outdoor recreation may influence levels of environmental concern.

Boosted Regression Active Shape Models

We present an efficient method of fitting a set of local feature models to an image within the popular Active Shape Model (ASM) framework [3]. We compare two different types of non-linear boosted feature models trained using GentleBoost [9]. The first type is a conventional feature detector classifier, which learns a discrimination function between the appearance of a feature and the local neighbourhood. The second local model type is a boosted regression predictor which learns the relationship between the local neighbourhood appearance and the displacement from the true feature location. At run-time the second regression model is much more efficient as only the current feature patch needs to be processed. We show that within the local iterative search of the ASM the local feature regression provides improved localisation on two publicly available human face test sets as well as increasing the search speed by a factor of eight.