Robust face recognition by an albedo based 3D morphable model

Large pose and illumination variations are very challenging for face recognition. The 3D Morphable Model (3DMM) approach is one of the effective methods for pose and illumination invariant face recognition. However, it is very difficult for the 3DMM to recover the illumination of the 2D input image because the ratio of the albedo and illumination contributions in a pixel intensity is ambiguous. Unlike the traditional idea of separating the albedo and illumination contributions using a 3DMM, we propose a novel Albedo Based 3D Morphable Model (AB3DMM), which removes the illumination component from the images using illumination normalisation in a preprocessing step. A comparative study of different illumination normalisation methods for this step is conducted on PIE and Multi-PIE databases. The results show that overall performance of our method outperforms state-of-the-art methods

A novel Markov logic rule induction strategy for characterizing sports video footage

The grounding of high-level semantic concepts is a key requirement of video annotation systems. Rule induction can thus constitute an invaluable intermediate step in characterizing protocol-governed domains, such as broadcast sports footage. We here set out a novel “clause grammar template” approach to the problem of rule-induction in video footage of court games that employs a second-order meta grammar for Markov Logic Network construction. The aim is to build an adaptive system for sports video annotation capable, in principle, both of learning ab initio and also adaptively transferring learning between distinct rule domains. The method is tested with respect to both a simulated game predicate generator and also real data derived from tennis footage via computer-vision based approaches including HOG3D based player-action classification, Hough-transform based court detection, and graph-theoretic ball-tracking. Experiments demonstrate that the method exhibits both error resilience and learning transfer in the court domain context. Moreover the clause template approach naturally generalizes to any suitably-constrained, protocol-governed video domain characterized by feature noise or detector error

Automatic annotation of tennis games: an integration of audio, vision, and learning

Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level

Domain anomaly detection in machine perception: a system architecture and taxonomy

We address the problem of anomaly detection in machine perception. The concept of domain anomaly is introduced as distinct from the conventional notion of anomaly used in the literature. We propose a unified framework for anomaly detection which exposes the multifacetted nature of anomalies and suggest effective mechanisms for identifying and distinguishing each facet as instruments for domain anomaly detection. The framework draws on the Bayesian probabilistic reasoning apparatus which clearly defines concepts such as outlier, noise, distribution drift, novelty detection (object, object primitive), rare events, and unexpected events. Based on these concepts we provide a taxonomy of domain anomaly events. One of the mechanisms helping to pinpoint the nature of anomaly is based on detecting incongruence between contextual and noncontextual sensor(y) data interpretation. The proposed methodology has wide applicability. It underpins in a unified way the anomaly detection applications found in the literature

Prediction in the Joint Modeling of Mixed Types of Multivariate Longitudinal Outcomes and a Time-to-Event Outcome

A common goal of longitudinal studies is to relate a set of repeated observations to a time-to-event endpoint. One example of such a design is in the area of a late-life depression research where repeated measurement of cognitive and functional outcomes can contribute to one's ability to predict whether or not an individual will have a major depressive episode over a period of time. This research proposes a novel model for the relationship between multivariate longitudinal measurements and a time-to-event outcome. The goal of this model is to improve prediction for the time-to-event outcome by considering all longitudinal measurements simultaneously. In this dissertation, we investigate a joint modeling approach for mixed types of multivariate longitudinal outcomes and a time-to-event outcome using a Bayesian paradigm. For the longitudinal model of continuous and binary outcomes, we formulate multivariate generalized linear mixed models with two types of random effects structures: shared random effects and correlated random effects. For the joint model, the longitudinal outcomes and the time-to-event outcome are assumed to be independent conditional on available covariates and the shared parameters, which are associated with the random effects of the longitudinal outcome processes. A Bayesian method using Markov chain Monte Carlo (MCMC) computed in OpenBUGS is implemented for parameter estimation. We illustrate the prediction of future event probabilities within a fixed time interval for patients based on our joint model, utilizing baseline data, post-baseline longitudinal measurements, and the time-to-event outcome. Prediction of event or mortality probabilities allows one to intervene clinically when appropriate. Hence, such methods provide a useful public health tool at both the individual and the population levels. The proposed joint model is applied to data sets on the maintenance therapies in a late-life depression study and the mortality in idiopathic pulmonary fibrosis. The performance of the method is also evaluated in extensive simulation studies

THE B LYMPHOCYTE DIFFERENTIATION FACTOR (BAFF) IS EXPRESSED IN THE AIRWAYS OF CHILDREN WITH CF AND IN LUNGS OF MICE INFECTED WITH PSEUDOMONAS AERUGINOSA

Background Chronic lung infection with Pseudomonas aeruginosa remains a major cause of mortality and morbidity among individuals with CF. Expression of mediators promoting recruitment and differentiation of B cells, or supporting antibody production is poorly understood yet could be key to controlling infection. Methods BAFF was measured in BAL from children with CF, both with and without P. aeruginosa, and controls. Mice were intra-nasally infected with P. aeruginosa strain LESB65 for up to 7 days. Cellular infiltration and expression of B cell chemoattractants and B cell differentiation factor, BAFF were measured in lung tissue. Results BAFF expression was elevated in both P. aeruginosa negative and positive CF patients and in P. aeruginosa infected mice post infection. Expression of the B cell chemoattractants CXCL13, CCL19 and CCL21 increased progressively post infection. Conclusions In a mouse model, infection with P. aeruginosa was associated with elevated expression of BAFF and other B cell chemoattractants suggesting a role for airway B cell recruitment and differentiation in the local adaptive immune response to P. aeruginosa. The paediatric CF airway, irrespective of pseudomonal infection, was found to be associated with an elevated level of BAFF implying that BAFF expression is not specific to pseudomonas infection and may be a feature of the CF airway. Despite the observed presence of a potent B cell activator, chronic colonisation is common suggesting that this response is ineffective

Religion as practices of attachment and materiality: the making of Buddhism in contemporary London

This article aims to explore Buddhism’s often-overlooked presence on London’s urban landscape, showing how its quietness and subtlety of approach has allowed the faith to grow largely beneath the radar. It argues that Buddhism makes claims to urban space in much the same way as it produces its faith, being as much about the practices performed and the spaces where they are enacted as it is about faith or beliefs. The research across a number of Buddhist sites in London reveals that number of people declaring themselves as Buddhists has indeed risen in recent years, following the rise of other non-traditional religions in the UK; however, this research suggests that Buddhism differs from these in several ways. Drawing on Baumann’s (2002) distinction between traditionalist and modernist approaches to Buddhism, our research reveals a growth in each of these. Nevertheless, Buddhism remains largely invisible in the urban and suburban landscape of London, adapting buildings that are already in place, with little material impact on the built environment, and has thus been less subject to contestation than other religious movements and traditions. This research contributes to a growing literature which foregrounds the importance of religion in making contemporary urban and social worlds