A biologically inspired appearance model for robust visual tracking

In this work, we propose a biologically inspired appearance model for robust visual tracking. Motivated in part by the success of the hierarchical organization of the primary visual cortex (area V1), we establish an architecture consisting of five layers: whitening, rectification, normalization, coding and polling. The first three layers stem from the models developed for object recognition. In this paper, our attention focuses on the coding and pooling layers. In particular, we use a discriminative sparse coding method in the coding layer along with spatial pyramid representation in the pooling layer, which makes it easier to distinguish the target to be tracked from its background in the presence of appearance variations. An extensive experimental study shows that the proposed method has higher tracking accuracy than several state-of-the-art trackers.

Face recognition using a unified 3D morphable model

We address the problem of 3D-assisted 2D face recognition in scenarios when the input image is subject to degradations or exhibits intra-personal variations not captured by the 3D model. The proposed solution involves a novel approach to learn a subspace spanned by perturbations caused by the missing modes of variation and image degradations, using 3D face data reconstructed from 2D images rather than 3D capture. This is accomplished by modelling the difference in the texture map of the 3D aligned input and reference images. A training set of these texture maps then defines a perturbation space which can be represented using PCA bases. Assuming that the image perturbation subspace is orthogonal to the 3D face model space, then these additive components can be recovered from an unseen input image, resulting in an improved fit of the 3D face model. The linearity of the model leads to efficient fitting. Experiments show that our method achieves very competitive face recognition performance on Multi-PIE and AR databases. We also present baseline face recognition results on a new data set exhibiting combined pose and illumination variations as well as occlusion.

The Family Model - A transgenerational approach to mental health in families: personal, clinical, educational, research, systems and policy perspectives

The Family Model – A transgenerational approach to mental health in families This workshop will provide an overview on The Family Model (TFM) and its use in promoting and facilitating a trans­generational family focus in Mental Health services, over the past 10­ - 15 years. Each of the speakers will address a different perspective, including service user/consumer, clinical practice, education & training, research and policy. Adrian Falkov (chair) will provide an overview of TFM to set the scene and a ‘policy to practice’ perspective, based on use of TFM in Australia. Author: Heide Lloyd. The Family Model ­ A personal (consumer/patient) perspective | United Kingdom Heide will provide a description of her experiences as a child, adult, parent & grandparent, using TFM as the structure around which to ‘weave’ her story and demonstrate how TFM has assisted her in understanding the impact of symptoms on her & family and how she has used it in her management of symptoms and recovery (personal perspective). The Family Model ­ Education & training perspective ­ Marie Diggins | United Kingdom PhD Bente Weimand | Norway Authors: ­ Marie Diggins | United Kingdom PhD Bente Weimand | Norway This combined (UK & Norwegian) presentation will cover historical background to TFM and its use in eLearning (the Social Care Institute for Excellence)and a number of other UK initiatives, together with a description of the postgraduate masters course at the University Oslo/Akershus, using TFM. The Family Model ­ A research perspective PhD Anne Grant | Northern Ireland Author: PhD Anne Grant | Ireland Anne Grant will describe how she used TFM as the theoretical framework for her PhD looking at family focused (nursing) practice in Ireland. The Family Model ­ A service systems perspective ­ Mary Donaghy | Northern Ireland Authors: PhD Adrian Falkov | Australia ­ Mary Donaghy | N Ireland Mary Donaghy will discuss how TFM has been used to support & facilitate a cross service ‘whole of system’ change program in Belfast (NI) to achieve improved family focused practice. She will demonstrate its utility in achieving a broader approach to service design, delivery and evaluation.

New Method Based on the UNIFAC–VISCO Model for the Estimation of Ionic Liquids Viscosity Using the Experimental Data Recommended by Mathematical Gnostics

The viscosity of ionic liquids (ILs) has been modeled as a function of temperature and at atmospheric pressure using a new method based on the UNIFAC–VISCO method. This model extends the calculations previously reported by our group (see Zhao et al. J. Chem. Eng. Data 2016, 61, 2160–2169) which used 154 experimental viscosity data points of 25 ionic liquids for regression of a set of binary interaction parameters and ion Vogel–Fulcher–Tammann (VFT) parameters. Discrepancies in the experimental data of the same IL affect the quality of the correlation and thus the development of the predictive method. In this work, mathematical gnostics was used to analyze the experimental data from different sources and recommend one set of reliable data for each IL. These recommended data (totally 819 data points) for 70 ILs were correlated using this model to obtain an extended set of binary interaction parameters and ion VFT parameters, with a regression accuracy of 1.4%. In addition, 966 experimental viscosity data points for 11 binary mixtures of ILs were collected from literature to establish this model. All the binary data consist of 128 training data points used for the optimization of binary interaction parameters and 838 test data points used for the comparison of the pure evaluated values. The relative average absolute deviation (RAAD) for training and test is 2.9% and 3.9%, respectively.