14,921 research outputs found
A TMS320C54 system for effective online Signature Verification using Hidden Markov Models
In this paper we present a scheme for real time implementation of a Hidden Markov Model based Signature Verification System on a TMS320C54 processor. Here we explain in detail our overall methodology and the subsequent DSP implementation. We also propose two new algorithms which would further facilitate real-time operation. We use the Baum-We1ch Algorithm for training the HMM and the Viterbi Algorithm for the testing of our proposed system. It may be noted that the technique of HMMs have hitherto been applied for speech modelling and only recently has its application to the field of Signature Verification been considered. Our proposed system has an overall accuracy of 11.64% FAR and 0.64% FRR
Robust Watermarking using Hidden Markov Models
Software piracy is the unauthorized copying or distribution of software. It is a growing problem that results in annual losses in the billions of dollars. Prevention is a difficult problem since digital documents are easy to copy and distribute. Watermarking is a possible defense against software piracy. A software watermark consists of information embedded in the software, which allows it to be identified. A watermark can act as a deterrent to unauthorized copying, since it can be used to provide evidence for legal action against those responsible for piracy.In this project, we present a novel software watermarking scheme that is inspired by the success of previous research focused on detecting metamorphic viruses. We use a trained hidden Markov model (HMM) to detect a specific copy of software. We give experimental results that show our scheme is robust. That is, we can identify the original software even after it has been extensively modified, as might occur as part of an attack on the watermarking scheme
Combining Multiple Views for Visual Speech Recognition
Visual speech recognition is a challenging research problem with a particular
practical application of aiding audio speech recognition in noisy scenarios.
Multiple camera setups can be beneficial for the visual speech recognition
systems in terms of improved performance and robustness. In this paper, we
explore this aspect and provide a comprehensive study on combining multiple
views for visual speech recognition. The thorough analysis covers fusion of all
possible view angle combinations both at feature level and decision level. The
employed visual speech recognition system in this study extracts features
through a PCA-based convolutional neural network, followed by an LSTM network.
Finally, these features are processed in a tandem system, being fed into a
GMM-HMM scheme. The decision fusion acts after this point by combining the
Viterbi path log-likelihoods. The results show that the complementary
information contained in recordings from different view angles improves the
results significantly. For example, the sentence correctness on the test set is
increased from 76% for the highest performing single view () to up to
83% when combining this view with the frontal and view angles
Adaptive probability scheme for behaviour monitoring of the elderly using a specialised ambient device
A Hidden Markov Model (HMM) modified to work in combination with a Fuzzy System is utilised to determine the current behavioural state of the user from information obtained with specialised hardware. Due to the high dimensionality and not-linearly-separable nature of the Fuzzy System and the sensor data obtained with the hardware which informs the state decision, a new method is devised to update the HMM and replace the initial Fuzzy System such that subsequent state decisions are based on the most recent information. The resultant system first reduces the dimensionality of the original information by using a manifold representation in the high dimension which is unfolded in the lower dimension. The data is then linearly separable in the lower dimension where a simple linear classifier, such as the perceptron used here, is applied to determine the probability of the observations belonging to a state. Experiments using the new system verify its applicability in a real scenario
An audio-based sports video segmentation and event detection algorithm
In this paper, we present an audio-based event detection algorithm shown to be effective when applied to Soccer video. The main benefit of this approach is the ability to recognise patterns that display high levels of crowd response correlated to key events. The soundtrack from a Soccer sequence is first parameterised using Mel-frequency Cepstral coefficients. It is then segmented into homogenous components using a windowing algorithm with a decision process based on Bayesian model selection. This decision process eliminated the need for defining a heuristic set of rules for segmentation. Each audio segment is then labelled using a series of Hidden Markov model (HMM) classifiers, each a representation of one of 6 predefined semantic content classes found in Soccer video. Exciting events are identified as those segments belonging to a crowd cheering class. Experimentation indicated that the algorithm was more effective for classifying crowd response when compared to traditional model-based segmentation and classification techniques
Tagging the Teleman Corpus
Experiments were carried out comparing the Swedish Teleman and the English
Susanne corpora using an HMM-based and a novel reductionistic statistical
part-of-speech tagger. They indicate that tagging the Teleman corpus is the
more difficult task, and that the performance of the two different taggers is
comparable.Comment: 14 pages, LaTeX, to appear in Proceedings of the 10th Nordic
Conference of Computational Linguistics, Helsinki, Finland, 199
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