1171329
Hidden Conditional Random Fields for Gesture Recognition
We introduce a discriminative hidden-state approach for the recognition of human gestures. Gesture sequences often have a complex underlying structure, and models that can incorporate hidden structures have proven to be advantageous for recognition tasks. Most existing approaches to gesture recognition with hidden states employ a Hidden Markov Model or suitable variant (e.g., a factored or coupled state model) to model gesture streams; a significant limitation of these models is the requirement of conditional independence of observations. In addition, hidden states in a generative model are selected to maximize the likelihood of generating all the examples of a given gesture class, which is not necessarily optimal for discriminating the gesture class against other gestures. Previous discriminative approaches to gesture sequence recognition have shown promising results, but have not incorporated hidden states nor addressed the problem of predicting the label of an entire sequence. In this paper, we derive a discriminative sequence model with a hidden state structure, and demonstrate its utility both in a detection and in a multi-way classification formulation. We evaluate our method on the task of recognizing human arm and head gestures, and compare the performance of our method to both generative hidden state and discriminative fully-observable models.
14715864
Inferring body pose using speech content
Untethered multimodal interfaces are more attractive than tethered ones because they are more natural and expressive for interaction. Such interfaces usually require robust vision-based body pose estimation and gesture recognition. In interfaces where a user is interacting with a computer using speech and arm gestures, the user's spoken keywords can be recognized in conjuction with a hypothesis of body poses. This co-occurence can reduce the number of body pose hypothesis for the vision based tracker. In this paper we show that incorporating speech-based body pose constraints can increase the robustness and accuracy of vision-based tracking systems.Next, we describe an approach for gesture recognition. We show how Linear Discriminant Analysis (LDA), can be employed to estimate 'good features' that can be used in a standard HMM-based gesture recognition system. We show that, by applying our LDA scheme, recognition errors can be significantly reduced over a standard HMM-based technique.We applied both techniques in a Virtual Home Desktop scenario. Experiments where the users controlled a desktop system using gestures and speech were conducted and the results show that the speech recognised in conjunction with body poses has increased the accuracy of the vision-based tracking system.
11079160
An Anonymous Endorsement System
The expression of one’s opinion through endorsement is one of the simplest methods of democratic participation. The result of an endorsement can be used to evaluate whether a certain subject should deserve a higher attention. In some cases, the endorsers desire privacy protection. However, conventional paper-based endorsement systems provide neither convenience nor well privacy protection for the endorsers. In addition, current electronic anonymous voting schemes are unsuitable for anonymous endorsement. This motivates us to develop an anonymous endorsement system that can be realized on computer networks. The proposed system satisfies completeness, soundness, privacy, unreusability, eligibility, and verifiability. In practice, the proposed system can be integrated with the conventional paper-based endorsement system.
122573148
Shape correspondence through landmark sliding
Motivated by improving statistical shape analysis, this paper presents a novel landmark-based method for accurate shape correspondence, where the general goal is to align multiple shape instances by corresponding a set of given landmark points along those shapes. Different from previous methods, we consider both global shape deformation and local geometric features in defining the shape-correspondence cost function to achieve a consistency between the landmark correspondence and the underlying shape correspondence. According to this cost function, we develop a novel landmark-sliding algorithm to achieve optimal landmark-based shape correspondence with preserved shape topology. The proposed method can be applied to correspond various 2D shapes in the forms of single closed curves, single open curves, self-crossing curves, and multiple curves. We also discuss the practical issue of landmark initialization. The proposed method has been tested on various biological shapes arising from medical image analysis and validated in constructing statistical shape models.
22896034
Compiler Techniques for Extracting Loop-Level Parallelism
89684
Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling
We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled and unlabeled training data. Our approach is based on extending the minimum entropy regularization framework to the structured prediction case, yielding a training objective that combines unlabeled conditional entropy with labeled conditional likelihood. Although the training objective is no longer concave, it can still be used to improve an initial model (e.g. obtained from supervised training) by iterative ascent. We apply our new training algorithm to the problem of identifying gene and protein mentions in biological texts, and show that incorporating unlabeled data improves the performance of the supervised CRF in this case.
12212021
Variational Bayesian image modelling
We present a variational Bayesian framework for performing inference, density estimation and model selection in a special class of graphical models---Hidden Markov Random Fields (HMRFs). HMRFs are particularly well suited to image modelling and in this paper, we apply them to the problem of image segmentation. Unfortunately, HMRFs are notoriously hard to train and use because the exact inference problems they create are intractable. Our main contribution is to introduce an efficient variational approach for performing approximate inference of the Bayesian formulation of HMRFs, which we can then apply to the density estimation and model selection problems that arise when learning image models from data. With this variational approach, we can conveniently tackle the problem of image segmentation. We present experimental results which show that our technique outperforms recent HMRF-based segmentation methods on real world images.