Inspired by recent developments in natural language processing, we propose a novel approach to sign language processing based on phonological properties validated by American Sign Language users. By taking advantage of datasets composed of phonological data and people speaking sign language, we use a pretrained deep model based on mesh reconstruction to extract the 3D coordinates of the signers keypoints. Then, we train standard statistical and deep machine learning models in order to assign phonological classes to each temporal sequence of coordinates.Our paper introduces the idea of exploiting the phonological properties manually assigned by sign language users to classify videos of people performing signs by regressing a 3D mesh. We establish a new baseline for this problem based on the statistical distribution of 725 different signs. Our best-performing models achieve a micro-averaged F1-score of 58% for the major location class and 70% for the sign type using statistical and deep learning algorithms, compared to their corresponding baselines of 35% and 39%.

Discriminative human pose estimation is the problem of inferring the 3D articulated pose of a human directly from an image feature. This is a challenging problem due to the highly non-linear and multi-modal mapping from the image feature space to the pose space. To address this problem, we propose a model employing a mixture of Gaussian processes where each Gaussian process models a local region of the pose space. By employing the models in this way we are able to overcome the limitations of Gaussian processes applied to human pose estimation - their O(N^3) time complexity and their uni-modal predictive distribution. Our model is able to give a multi-modal predictive distribution where each mode is represented by a different Gaussian process prediction. A logistic regression model is used to give a prior over each expert prediction in a similar fashion to previous mixture of expert models. We show that this technique outperforms existing state of the art regression techniques on human pose estimation data sets for ballet dancing, sign language and the HumanEva data set.

In this paper we propose a novel method for discriminative monocular human pose tracking using a mixture of Gaussian processes and a dynamic programming algorithm for selecting the optimal expert at each frame. The proposed tracking mechanism incorporates a dynamical model into the predictive distribution which is combined with the appearance model in a principled manner. This model is able to give a smoother predicted pose and resolves ambiguities in the image to pose mapping. We introduce a mixture of Gaussian processes model which optimises the size and location of each expert ensuring that each expert models a coherent region of the dataset resulting in an accurate predictive density. We compare our method to other state of the art methods on 2D and 3D monocular pose estimation on ballet and sign language data sets.

Gaussian processes have been widely used as a method for inferring the pose of articulated bodies directly from image data. While able to model complex non-linear functions, they are limited due to their inability to model multi-modality caused by ambiguities and varying noise in the data set. For this reason techniques employing mixtures of local Gaussian processes have been proposed to allow multi-modal functions to be predicted accurately [11]. These techniques rely on the calculation of nearest neighbours in the input space to make accurate predictions. However, this becomes a limiting factor when image features are noisy due to changing backgrounds. In this paper we propose a novel method that overcomes this limitation by learning a logistic regression model over the input space to select between the local Gaussian processes. Our proposed method is more robust to a noisy input space than a nearest neighbour approach and provides a better prior over each Gaussian process prediction. Results are demonstrated using synthetic and real data from a sign language data set and HumanEva [9].

In this paper, we introduce a 3-D human-body tracker capable of handling fast and complex motions in real-time. We build upon the Monte–Carlo Bayesian framework, and propose novel prediction and evaluation methods improving the robustness and efficiency of the tracker. The parameter space, augmented with first order derivatives, is automatically partitioned into Gaussian clusters each representing an elementary motion: hypothesis propagation inside each cluster is therefore accurate and efficient. The transitions between clusters use the predictions of a variable length Markov model which can explain high-level behaviours over a long history. Using Monte–Carlo methods, evaluation of model candidates is critical for both speed and robustness. We present a new evaluation scheme based on hierarchical 3-D reconstruction and blob-fitting, where appearance models and image evidences are represented by mixtures of Gaussian blobs. Our tracker is also capable of automatic-initialisation and self-recovery. We demonstrate the application of our tracker to long video sequences exhibiting rapid and diverse movements.

We propose a variational bayes approach to the problem of robust estimation of gaussian mixtures from noisy input data. The proposed algorithm explicitly takes into account the uncertainty associated with each data point, makes no assumptions about the structure of the covariance matrices and is able to automatically determine the number of the gaussian mixture components. Through the use of both synthetic and real world data examples, we show that by incorporating uncertainty information into the clustering algorithm, we get better results at recovering the true distribution of the training data compared to other variational bayesian clustering algorithms.

The design of each component of the Multipurpose Superconducting ECR Ion Source (MS-ECRIS) has been completed and some items are ready. The magnets and the cryostat are under construction at ACCEL and the commissioning is scheduled for March 2007. The mechanical have been optimized and their construction is under way, the microwave system is under refurbishment and the 65kV power supply is available and upgraded for afterglow operations. Pumping and extraction system were adapted to the EIS testbench of GSI Darmstadt. The description of,each part will be given in the paper along with a schedule of the forthcoming development and experiments.

In this paper, we present a tracking framework for capturing articulated human motions in real-time, without the need for attaching markers onto the subject's body. This is achieved by first obtaining a low dimensional representation of the training motion data, using a nonlinear dimensionality reduction technique called back-constrained GPLVM. A prior dynamics model is then learnt from this low dimensional representation by partitioning the motion sequences into elementary movements using an unsupervised EM clustering algorithm. The temporal dependencies between these elementary movements are efficiently captured by a Variable Length Markov Model. The learnt dynamics model is used to bias the propagation of candidate pose feature vectors in the low dimensional space. By combining this with an efficient volumetric reconstruction algorithm, our framework can quickly evaluate each candidate pose against image evidence captured from multiple views. We present results that show our system can accurately track complex structured activities such as ballet dancing in real-time.

We present a novel approach for visual tracking of structured behaviour as observed in human–computer interaction. An automatically acquired variable-length Markov model is used to represent the high-level structure and temporal ordering of gestures. Continuous estimation of hand posture is handled by combining the model with annealed particle filtering. The stochastic simulation updates and automatically switches between different model representations of hand posture that correspond to distinct gestures. The implementation executes in real time and demonstrates significant improvement in robustness over comparable methods. We provide a measurement of user performance when our method is applied to a Fitts’ law drag-and-drop task, and an analysis of the effects of latency that it introduces.

In recent years there has been increasing interest in constructing cognitive vision systems capable of interpreting the high level semantics of dynamic scenes. Purely quantitative approaches to the task of constructing such systems have met with some success. However, qualitative analysis of dynamic scenes has the advantage of allowing easier generalisation of classes of di.erent behaviours and guarding against the propagation of errors caused by uncertainty and noise in the quantitative data. Our aim is to integrate quantitative and qualitative modes of representation and reasoning for the analysis of dynamic scenes. In particular, in this paper we outline an approach for constructing cognitive vision systems using qualitative spatial-temporal representations including prototypical spatial relations and spatio-temporal event descriptors automatically inferred from input data. The overall architecture relies on abduction: the system searches for explanations, phrased in terms of the learned spatio-temporal event descriptors, to account for the video data.

Commercial organisations that are in dispute will sometimes seek to settle their differences without resorting to legal proceedings through a process of structured and facilitated negotiation called Alternate Dispute Resolution (ADR). Traditionally ADR negotiations are conducted by co-locating the mediator, witnesses, supporting evidence and representatives of each party. This paper describes a novel experiment that has been conducted at the University of Leeds to investigate whether an augmented collaborative environment could provide a realistic alternative to co-locating individuals for ADR negotiations. The individuals that took part in the experiment included an experienced mediator and practising legal representatives. The augmented collaborative environment was based on a video conferencing system that was enhanced to enable virtual artefacts representing items of evidence to be blended directly into the video streams. This experiment sits within the framework of the Court 21 Project, that is seeking to identify how new technology can be used to improve the different aspects of the legal system [10].

This report discusses the problem of hand pose estimation. This is one of several areas of interest within the field of Computer Vision. The main focus is the approach that ClassMap takes. The report goes on to explain the work involved in a substantial project, implementing the ClassMap "embeddings" as part of a system designed to recognise hand poses using a camera. The Design and Implementation stages of the project are discussed in detail, with some explanation of the image processing techniques used, including the Chamfer Distance and the Canny Edge Detector. There is an explanation of how the system is used and how well it works, with suggestions for improvements that could be made.