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

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

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

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

Frequency Analysis and Sheared Reconstruction for Rendering Motion Blur

International audienceMotion blur is crucial for high-quality rendering but is also very expensive. Our first contribution is a frequency analysis of motion-blurred scenes, including moving objects, specular reflections, and shadows. We show that motion induces a shear in the frequency domain, and that the spectrum of moving scenes is usually contained in a wedge. This allows us to compute adaptive space-time sampling rates, to accelerate rendering. For uniform velocities and standard axis-aligned reconstruction, we show that the product of spatial and temporal bandlimits or sampling rates is constant, independent of velocity. Our second contribution is a novel sheared reconstruction filter that tightly packs the wedge of frequencies in the Fourier domain, and enables even lower sampling rates. We present a rendering algorithm that computes a sheared reconstruction filter per pixel, without any intermediate Fourier representation. This often permits synthesis of motion-blurred images with far fewer rendering samples than standard techniques require

Serotonin depletion amplifies distinct human social emotions as a function of individual differences in personality

Funder: Gates Cambridge Trust; doi: https://doi.org/10.13039/501100005370Abstract: Serotonin is involved in a wide range of mental capacities essential for navigating the social world, including emotion and impulse control. Much recent work on serotonin and social functioning has focused on decision-making. Here we investigated the influence of serotonin on human emotional reactions to social conflict. We used a novel computerised task that required mentally simulating social situations involving unjust harm and found that depleting the serotonin precursor tryptophan—in a double-blind randomised placebo-controlled design—enhanced emotional responses to the scenarios in a large sample of healthy volunteers (n = 73), and interacted with individual differences in trait personality to produce distinctive human emotions. Whereas guilt was preferentially elevated in highly empathic participants, annoyance was potentiated in those high in trait psychopathy, with medium to large effect sizes. Our findings show how individual differences in personality, when combined with fluctuations of serotonin, may produce diverse emotional phenotypes. This has implications for understanding vulnerability to psychopathology, determining who may be more sensitive to serotonin-modulating treatments, and casts new light on the functions of serotonin in emotional processing

Vagus nerve stimulation for depression: efficacy and safety in a European study

Background Vagus nerve stimulation (VNS) therapy is associated with a decrease in seizure frequency in partial-onset seizure patients. Initial trials suggest that it may be an effective treatment, with few side-effects, for intractable depression. Method An open, uncontrolled European multi-centre study (D03) of VNS therapy was conducted, in addition to stable pharmacotherapy, in 74 patients with treatment-resistant depression (TRD). Treatment remained unchanged for the first 3 months; in the subsequent 9 months, medications and VNS dosing parameters were altered as indicated clinically. Results The baseline 28-item Hamilton Depression Rating Scale (HAMD-28) score averaged 34. After 3 months of VNS, response rates (50% reduction in baseline scores) reached 37% and remission rates (HAMD-28 score <10) 17%. Response rates increased to 53% after 1 year of VNS, and remission rates reached 33%. Response was defined as sustained if no relapse occurred during the first year of VNS after response onset; 44% of patients met these criteria. Median time to response was 9 months. Most frequent side-effects were voice alteration (63% at 3 months of stimulation) and coughing (23%). Conclusions VNS therapy was effective in reducing severity of depression; efficacy increased over time. Efficacy ratings were in the same range as those previously reported from a USA study using a similar protocol; at 12 months, reduction of symptom severity was significantly higher in the European sample. This might be explained by a small but significant difference in the baseline HAMD-28 score and the lower number of treatments in the current episode in the European stud