Physica A: Statistical Mechanics and its Applications, 2004
We apply Random Matrix Theory (RMT) on an empirically-measured nancial correlation matrix, C, and... more We apply Random Matrix Theory (RMT) on an empirically-measured nancial correlation matrix, C, and show that this matrix contains a large amount of noise. In order to determine the sensitivity of the spectral properties of a random matrix to noise, we simulate a set of data and add di erent volumes of random noise. Having ascertained that the eigenspectrum is independent of the standard deviation of added noise, we use RMT to determine the noise percentage in a correlation matrix based on real data from S&P500. Eigenvalue and eigenvector analyses are applied and the experimental results for each of them are presented to identify qualitatively and quantitatively di erent spectral properties of the empirical correlation matrix to a random counterpart. Finally we attempt to separate the noisy part from the non-noisy part of C. We apply an existing technique to cleaning C and then discuss its associated problems. We propose a technique of ltering C that has many advantages, from the stability point of view, over the existing method of cleaning.
Occlusion is an unavoidable subject in most machine vision areas. Recognition of partially-occlud... more Occlusion is an unavoidable subject in most machine vision areas. Recognition of partially-occluded hand gestures is an important problem. In this paper a new algorithm is proposed for the recognition of occluded and non-occluded hand gestures based on matching the graphs of gestures in an eigenspace.
The problem of hand tracking in the presence of occlusion is addressed. In bimanual movements the... more The problem of hand tracking in the presence of occlusion is addressed. In bimanual movements the hands tend to be synchronised effortlessly. Different aspects of this synchronisation are the basis of our research to track the hands. We propose a Kalman filtering-based dynamic model to catch the movement of the hands. The spatial synchronisation in bimanual movements is modelled by the position and the temporal synchronisation by the velocity and acceleration of each hand. Based on this model, we introduce algorithms for occlusion detection and hand tracking during occlusion.
A general hand-tracking algorithm is presented for tracking hands in bimanual movements. The prob... more A general hand-tracking algorithm is presented for tracking hands in bimanual movements. The problem is approached from a neuroscience point of view. Using a dynamic model and some motor control phenomena, the movement of the hands during a bimanual movement ...
A robust complete system for the recognition of hand gestures is the main goal of this research. ... more A robust complete system for the recognition of hand gestures is the main goal of this research. One of the most difficult tasks for such a system is the recognition of co-articulated and partially occluded hand gestures. Co-articulation is the influence of one gesture on the ...
A hierarchical gesture recognition algorithm is introduced to recognise a large number of gesture... more A hierarchical gesture recognition algorithm is introduced to recognise a large number of gestures. Three stages of the proposed algorithm are based on a new hand tracking technique to recognise the actual beginning of a gesture using a Kalman filtering process, hidden Markov models and graph matching. Processing time is important in working with large databases. Therefore, special cares are taken to deal with the large number of gestures, which are partially similar.
Human-Computer Interaction. A part of this can be the recognition of movements in which the two h... more Human-Computer Interaction. A part of this can be the recognition of movements in which the two hands move simultaneously to do something or imply a meaning. We present a Bayesian network for fusing Hidden Markov Models in order to recognise a bimanual movement. A bimanual movement is tracked and segmented by a tracking algorithm. Hidden Markov Models are assigned to the segments in order to learn and recognize the partial movement within each segment. A Bayesian network fuses the HMMs in order to perceive the movement of the two hands as a single entity.
Physica A: Statistical Mechanics and its Applications, 2004
We apply Random Matrix Theory (RMT) on an empirically-measured nancial correlation matrix, C, and... more We apply Random Matrix Theory (RMT) on an empirically-measured nancial correlation matrix, C, and show that this matrix contains a large amount of noise. In order to determine the sensitivity of the spectral properties of a random matrix to noise, we simulate a set of data and add di erent volumes of random noise. Having ascertained that the eigenspectrum is independent of the standard deviation of added noise, we use RMT to determine the noise percentage in a correlation matrix based on real data from S&P500. Eigenvalue and eigenvector analyses are applied and the experimental results for each of them are presented to identify qualitatively and quantitatively di erent spectral properties of the empirical correlation matrix to a random counterpart. Finally we attempt to separate the noisy part from the non-noisy part of C. We apply an existing technique to cleaning C and then discuss its associated problems. We propose a technique of ltering C that has many advantages, from the stability point of view, over the existing method of cleaning.
Occlusion is an unavoidable subject in most machine vision areas. Recognition of partially-occlud... more Occlusion is an unavoidable subject in most machine vision areas. Recognition of partially-occluded hand gestures is an important problem. In this paper a new algorithm is proposed for the recognition of occluded and non-occluded hand gestures based on matching the graphs of gestures in an eigenspace.
The problem of hand tracking in the presence of occlusion is addressed. In bimanual movements the... more The problem of hand tracking in the presence of occlusion is addressed. In bimanual movements the hands tend to be synchronised effortlessly. Different aspects of this synchronisation are the basis of our research to track the hands. We propose a Kalman filtering-based dynamic model to catch the movement of the hands. The spatial synchronisation in bimanual movements is modelled by the position and the temporal synchronisation by the velocity and acceleration of each hand. Based on this model, we introduce algorithms for occlusion detection and hand tracking during occlusion.
A general hand-tracking algorithm is presented for tracking hands in bimanual movements. The prob... more A general hand-tracking algorithm is presented for tracking hands in bimanual movements. The problem is approached from a neuroscience point of view. Using a dynamic model and some motor control phenomena, the movement of the hands during a bimanual movement ...
A robust complete system for the recognition of hand gestures is the main goal of this research. ... more A robust complete system for the recognition of hand gestures is the main goal of this research. One of the most difficult tasks for such a system is the recognition of co-articulated and partially occluded hand gestures. Co-articulation is the influence of one gesture on the ...
A hierarchical gesture recognition algorithm is introduced to recognise a large number of gesture... more A hierarchical gesture recognition algorithm is introduced to recognise a large number of gestures. Three stages of the proposed algorithm are based on a new hand tracking technique to recognise the actual beginning of a gesture using a Kalman filtering process, hidden Markov models and graph matching. Processing time is important in working with large databases. Therefore, special cares are taken to deal with the large number of gestures, which are partially similar.
Human-Computer Interaction. A part of this can be the recognition of movements in which the two h... more Human-Computer Interaction. A part of this can be the recognition of movements in which the two hands move simultaneously to do something or imply a meaning. We present a Bayesian network for fusing Hidden Markov Models in order to recognise a bimanual movement. A bimanual movement is tracked and segmented by a tracking algorithm. Hidden Markov Models are assigned to the segments in order to learn and recognize the partial movement within each segment. A Bayesian network fuses the HMMs in order to perceive the movement of the two hands as a single entity.
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Papers by A. Shamaie