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This paper extracts statistical features using a novel approach. The feature set locally measure the characteristics of the image. The proposed approach encodes the extracted features, from a one-pixel width window that slides horizontally the word image. We then inject the feature vector set into a recognition engine. The recognition engine is built using Hidden Markov Models Tool Kit (HTK). The...
This article presents a new method for crowd behavior recognition, using dynamic features extracted from dense trajectories. The histogram of oriented gradient and motion boundary histogram descriptors are computed at dense points along motion trajectories, and tracked using median filtering and displacement information obtained from a dense optical flow field. Then a global representation of the...
Recently, mid-level features have shown promising performance in computer vision. Mid-level features learned by incorporating class-level information are potentially more discriminative than traditional low-level local features. In this paper, an effective method is proposed to extract mid-level features from Kinect skeletons for 3D human action recognition. Firstly, the orientations of limbs connected...
This paper proposes a model which approximates full covariance matrices in Gaussian mixture models (GMM) with a reduced number of parameters and computations required for likelihood evaluations. In the proposed model inverse covariance (precision) matrices are approximated using sparsely represented eigenvectors, i.e. each eigenvector of a covariance/precision matrix is represented as a linear combination...
Hidden Markov Models (HMMs) are now widely used for off-line Arabic handwriting recognition. Actually, classical HMMs are one-dimensional models, that is why to process an Arabic word image we have developed a discrete Dynamic Bayesian Network (DBN). The DBNs are an extension and a generalization of the classical HMMs, which can model the interaction between several observations and state sequences...
This paper proposes a probabilistic framework based on movement primitives for robots that work in collaboration with a human coworker. Since the human coworker can execute a variety of unforeseen tasks a requirement of our system is that the robot assistant must be able to adapt and learn new skills on-demand, without the need of an expert programmer. Thus, this paper leverages on the framework of...
The extensive use of virtualization in implementing cloud infrastructure brings unrivaled security concerns for cloud tenants or customers and introduces an additional layer that itself must be completely configured and secured. Intruders can exploit the large amount of cloud resources for their attacks. Most of the current security technologies do not provide the essential security features for cloud...
The use of deep neural networks (DNNs) has improved performance in several fields including computer vision, natural language processing, and automatic speech recognition (ASR). The increased use of DNNs in recent years has been largely due to performance afforded by GPUs, as the computational cost of training large networks on a CPU is prohibitive. Many training algorithms are well-suited to the...
This paper proposes a system on the basis of guidance information from music score and Bayesian harmonic model and a two-dimensional Hidden Markov (2D-HMM) states model with particle filtering to address the separation of single-channel polyphonic music source. It is showed in a large number of experiments that in recording and synthetic polyphonic music material, the informed separation method performs...
In this paper, we address an exemplar-based hidden markov model (HMM) that represents the lip motion activity using visual cues for lipreading. The discriminative visual features including the geometric shape parameters and contour-constrained spatial histogram are selected for representing each lip frame. Then, a set of exemplars associated with the HMM is learned jointly to serve as a typical representation...
In this paper, we present a method for human action recognition using local joints structure and histograms of 3D joints. Global features like histograms of 3D joints [12] ignore the local structure information of the human body joints, which is also essential for accurate action recognition. To address this problem, we propose a local joints structure feature as a complement, and combine both global...
In this paper we present a novel method for describing the EEG as a sequence of topographies, based on the notion of microstates. We use Hidden Markov Models (HMM) to model the temporal evolution of the topography of the average Event Related Potential (ERP) and we calculate the Fisher score of the sequence by taking the gradient of the trained model parameters given the sequence. In this context,...
Advances in sensor and ubiquitous technologies have contributed to the broad scale adoption of pervasive devices. Context or activity recognition from sensor signals is an emerging area that has garnered huge research interest. In this paper, we propose a novel predictive model that utilizes dyadic wavelet transform, vector quantization and Hidden Markov Model (HMM) to predict a high level activity...
A motor rehabilitation robot applied patient's intention can enhance the rehabilitation efficacy. Continuous hidden Markov models of knee flexion and extension are trained using autoregressive model coefficients of knee flexor and extensor electromyograms. The patient's intention of knee movement are recognized by the trained continuous hidden Markov models and the user's knee flexor and extensor...
This paper presents a new scheme for sensor fault detection and isolation. It uses a single Kalman filter and a Gaussian hidden Markov model for each of the monitored sensors. This combination is able to simultaneously detect single and multiple sensor faults, still guaranteeing optimal system state estimation. This algorithm also can run on systems with limited computational power. The efficiency...
Speech recognition is the important problem in pattern recognition research field. In this paper, the kernel ridge regression method is proposed to be applied to the MFCC feature vectors of the speech dataset available from IC Design lab at Faculty of Electricals-Electronics Engineering, University of Technology, Ho Chi Minh City. Experiment results show that the kernel ridge regression method outperforms...
In this paper, following the model-free approach for gait image representation, an individual recognition system is developed using the Gait Energy Image (GEI) templates. The GEI templates can easily be obtained from an image sequence of a walking person. Low dimensional feature vectors are extracted from the GEI templates using Principal Component Analysis (PCA) and Multiple Discriminant Analysis...
The study of social networks has become increasingly important in recent years. Previous implementations of multi-agent systems have observed a phenomenon called tolerance between agents through simulation studies, which is defined as an agent maintaining an unrewarding connection. This concept has also arisen in the social sciences through the study of networks. We aim to bridge this gap between...
In this paper, we propose a simple and fast method for evaluating the pathological voice (esophageal) by applying the continuous speech recognition in a speaker dependent mode, on our own database of the pathological voice, we call FPSD (French Pathological Speech Database). The recognition system used is implemented using the HTK platform, based on HMM/GMM monophone models. The acoustic vectors are...
Deep Neural Network (DNN), which can model hierarchical and complex relationship between input and output layer has recently been applied in speech synthesis. However, it is remained uncertain why DNN outperform traditional HMM-based synthesis. This paper describes several implementation details of DNN-based speech synthesis system and compares different impacting factors, e.g, F0 modeling method...
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