9,909 research outputs found

    Facial expression recognition with emotion-based feature fusion

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    © 2015 Asia-Pacific Signal and Information Processing Association. In this paper, we propose an emotion-based feature fusion method using the Discriminant-Analysis of Canonical Correlations (DCC) for facial expression recognition. There have been many image features or descriptors proposed for facial expression recognition. For the different features, they may be more accurate for the recognition of different expressions. In our proposed method, four effective descriptors for facial expression representation, namely Local Binary Pattern (LBP), Local Phase Quantization (LPQ), Weber Local Descriptor (WLD), and Pyramid of Histogram of Oriented Gradients (PHOG), are considered. Supervised Locality Preserving Projection (SLPP) is applied to the respective features for dimensionality reduction and manifold learning. Experiments show that descriptors are also sensitive to the conditions of images, such as race, lighting, pose, etc. Thus, an adaptive descriptor selection algorithm is proposed, which determines the best two features for each expression class on a given training set. These two features are fused, so as to achieve a higher recognition rate for each expression. In our experiments, the JAFFE and BAUM-2 databases are used, and experiment results show that the descriptor selection step increases the recognition rate up to 2%

    Shape-appearance-correlated active appearance model

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    © 2016 Elsevier Ltd Among the challenges faced by current active shape or appearance models, facial-feature localization in the wild, with occlusion in a novel face image, i.e. in a generic environment, is regarded as one of the most difficult computer-vision tasks. In this paper, we propose an Active Appearance Model (AAM) to tackle the problem of generic environment. Firstly, a fast face-model initialization scheme is proposed, based on the idea that the local appearance of feature points can be accurately approximated with locality constraints. Nearest neighbors, which have similar poses and textures to a test face, are retrieved from a training set for constructing the initial face model. To further improve the fitting of the initial model to the test face, an orthogonal CCA (oCCA) is employed to increase the correlation between shape features and appearance features represented by Principal Component Analysis (PCA). With these two contributions, we propose a novel AAM, namely the shape-appearance-correlated AAM (SAC-AAM), and the optimization is solved by using the recently proposed fast simultaneous inverse compositional (Fast-SIC) algorithm. Experiment results demonstrate a 5–10% improvement on controlled and semi-controlled datasets, and with around 10% improvement on wild face datasets in terms of fitting accuracy compared to other state-of-the-art AAM models

    Survey of Error Concealment techniques: Research directions and open issues

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    © 2015 IEEE. Error Concealment (EC) techniques use either spatial, temporal or a combination of both types of information to recover the data lost in transmitted video. In this paper, existing EC techniques are reviewed, which are divided into three categories, namely Intra-frame EC, Inter-frame EC, and Hybrid EC techniques. We first focus on the EC techniques developed for the H.264/AVC standard. The advantages and disadvantages of these EC techniques are summarized with respect to the features in H.264. Then, the EC algorithms are also analyzed. These EC algorithms have been recently adopted in the newly introduced H.265/HEVC standard. A performance comparison between the classic EC techniques developed for H.264 and H.265 is performed in terms of the average PSNR. Lastly, open issues in the EC domain are addressed for future research consideration

    Mental health problems: Are they or are they not a risk factor for dropout from drug treatment? A systematic review of the evidence

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    Background: A sizeable number of recent studies investigating whether clients with substance misuse and mental health problems (dual diagnosis clients) are at heightened risk of dropout from drug treatment have been published. It is timely that their findings are brought together in a comprehensive review of the current evidence. Aims: The aim of the review is to examine whether dually diagnosed clients are less likely to be retained in drug treatment than clients without mental health problems, and, if so, whether this varies for clients diagnosed with different types of mental health problems. Methods: The review considers peer-reviewed research published after 1 January 1990, which was located using the literature databases Medline and PsycInfo. Predefined search terms were used. Further papers were identified from the bibliographies of relevant publications. Findings: 58 studies (84% from the USA) met the inclusion criteria for the review. The findings suggest that for most clients, having a past history of mental health problems does not influence the likelihood of being retained in drug treatment. The body of evidence regarding concurrent mental health problems is contradictory. On the whole, the majority of studies suggest that neither presence nor severity of depressive, anxiety, or other Axis-I disorders is related to retention, but these findings are not entirely unequivocal, as a few studies report strong positive or negative associations between depression and anxiety disorders and retention. Few researchers looked separately at psychotic spectrum disorders hence no conclusions could be drawn. The presence of most personality disorders also did not appear to affect treatment tenure, with the exception of antisocial personality disorder, for which the evidence points towards a greater risk of dropout. Conclusions: The balance of evidence suggests that, overall, dual diagnosis clients with Axis-I disorders who seek treatment in drug treatment services are retained as well as clients without dual diagnosis. Subgroups of clients who appear more vulnerable to premature dropout include those with antisocial personality disorder. Methodological shortcomings of the reviewed studies and resulting implications for this review and future research are discussed

    Diverse proteomic alterations in gastric adenocarcinoma

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    Gastric adenocarcinoma is one of the most common cancers in Asian countries including China. Although its incidence rates in the West are lower than that in Asia, gastric cancer is still a major health problem worldwide, being second only to lung cancers in the number of deaths it causes. Helicobacter pylori infection has been identified as the major pathogen, but the detailed pathogenesis of gastric carcinoma remains elusive. Due to the lack of suitable and specific biomarkers for early detection, most cases of the disease are diagnosed at late stages and the survival rate is low. In this study, we used a proteomic approach to globally analyze the protein profiles of paired surgical specimens of primary gastric adenocarcinoma and nontumor mucosa aiming at identifying specific disease-associated proteins as potential clinical biomarkers and for carcinogenetic study. Compared to nontumor tissues, multiple protein alterations were found in tumor tissues. Some of these alterations involve variations in the expression of cytoskeleton proteins, including an increase in cytokeratin 8 and tropomyosin isoform and a decrease in cytokeratin 20. Co-up-regulations of heat-shock proteins and glycolytic enzymes were observed in tumor tissues, indicating self-protective efforts of cells and the growing energy requirement during malignant transformation. Diverse regulations also occurred with proteins involved in cell proliferation and differentiation, such as GMP reductase 2 and creatine kinase B, and proteins bearing potential tumor suppressor activities, including prohibitin and selenium binding protein 1. More interestingly, a human stomach-specific protein, 18 kDa antrum mucosa protein, was found to be dramatically under-expressed in cancer tissues, implicating a possible special pathological role for this protein in gastric carcinogenesis. Further comprehensive evaluation by globally considering the altered factors may result in the discovery of a biomarker index for effective assessment of the disease and may provide in-depth information for better understanding the pathogenesis of gastric cancer.postprin

    Label-invariant models for the analysis of meta-epidemiological data.

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    Rich meta-epidemiological data sets have been collected to explore associations between intervention effect estimates and study-level characteristics. Welton et al proposed models for the analysis of meta-epidemiological data, but these models are restrictive because they force heterogeneity among studies with a particular characteristic to be at least as large as that among studies without the characteristic. In this paper we present alternative models that are invariant to the labels defining the 2 categories of studies. To exemplify the methods, we use a collection of meta-analyses in which the Cochrane Risk of Bias tool has been implemented. We first investigate the influence of small trial sample sizes (less than 100 participants), before investigating the influence of multiple methodological flaws (inadequate or unclear sequence generation, allocation concealment, and blinding). We fit both the Welton et al model and our proposed label-invariant model and compare the results. Estimates of mean bias associated with the trial characteristics and of between-trial variances are not very sensitive to the choice of model. Results from fitting a univariable model show that heterogeneity variance is, on average, 88% greater among trials with less than 100 participants. On the basis of a multivariable model, heterogeneity variance is, on average, 25% greater among trials with inadequate/unclear sequence generation, 51% greater among trials with inadequate/unclear blinding, and 23% lower among trials with inadequate/unclear allocation concealment, although the 95% intervals for these ratios are very wide. Our proposed label-invariant models for meta-epidemiological data analysis facilitate investigations of between-study heterogeneity attributable to certain study characteristics

    Frame Interpolation for Cloud-Based Mobile Video Streaming

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    © 2016 IEEE. Cloud-based High Definition (HD) video streaming is becoming popular day by day. On one hand, it is important for both end users and large storage servers to store their huge amount of data at different locations and servers. On the other hand, it is becoming a big challenge for network service providers to provide reliable connectivity to the network users. There have been many studies over cloud-based video streaming for Quality of Experience (QoE) for services like YouTube. Packet losses and bit errors are very common in transmission networks, which affect the user feedback over cloud-based media services. To cover up packet losses and bit errors, Error Concealment (EC) techniques are usually applied at the decoder/receiver side to estimate the lost information. This paper proposes a time-efficient and quality-oriented EC method. The proposed method considers H.265/HEVC based intra-encoded videos for the estimation of whole intra-frame loss. The main emphasis in the proposed approach is the recovery of Motion Vectors (MVs) of a lost frame in real-time. To boost-up the search process for the lost MVs, a bigger block size and searching in parallel are both considered. The simulation results clearly show that our proposed method outperforms the traditional Block Matching Algorithm (BMA) by approximately 2.5 dB and Frame Copy (FC) by up to 12 dB at a packet loss rate of 1%, 3%, and 5% with different Quantization Parameters (QPs). The computational time of the proposed approach outperforms the BMA by approximately 1788 seconds

    Nutrition Profile of Products with Cartoon Animations on the Packaging: A UK Cross-Sectional Survey of Foods and Drinks.

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    BACKGROUND: Marketing, including the use of cartoon animations on packaging, has been shown to influence the food children choose to eat. This paper aims to determine the nutritional quality of UK food and drink products featuring child-friendly characters on pack. METHODS: A comprehensive cross-sectional survey of food and drink with packaging appealing to children available in the UK. Products were classified high in fat, salt and/or sugar (HFSS) according to the UK nutrient profiling model and guidance for front of pack nutrition labelling. Logistic regression was used to determine whether there was a significant relationship between nutritional quality of products, and animation type. RESULTS: Over half (51%) of 532 products with animations on packaging were classified as HFSS. Food products featuring unlicensed characters were significantly more likely to be deemed HFSS than those with licensed characters, according to both the nutrient profiling model (odds ratio (OR) 2.1, 95% CI: 1.3 to 3.4) and front of pack nutrition labelling system (OR 2.3, 95% confidence interval CI: 1.4 to 3.7). CONCLUSIONS: The use of cartoon characters on HFSS products is widespread. Policies to restrict the use of such marketing tactics should be considered to prevent children being targeted with unhealthy foods and drinks

    Phase Transitions of Charged Scalars at Finite Temperature and Chemical Potential

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    We calculate the grand canonical partition function at the one-loop level for scalar quantum electrodynamics at finite temperature and chemical potential. A classical background charge density with a charge opposite that of the scalars ensures the neutrality of the system. For low density systems we find evidence of a first order phase transition. We find upper and lower bounds on the transition temperature below which the charged scalars form a condensate. A first order phase transition may have consequences for helium-core white dwarf stars in which it has been argued that such a condensate of charged helium-4 nuclei could exist.Comment: 20 pages, 3 figures. Version accepted for publication in JHE
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