Hidden Markov model based visual speech recognition

Ph.DDOCTOR OF PHILOSOPH

Human Identification Using Gait

Keeping in view the growing importance of biometric signatures in automated security and surveillance systems, human gait recognition provides a low-cost non-obtrusive method for reliable person identification and is a promising area for research. This work employs a gait recognition process with binary silhouette-based input images and Hidden Markov Model (HMM)-based classification. The performance of the recognition method depends significantly on the quality of the extracted binary silhouettes. In this work, a computationally low-cost fuzzy correlogram based method is employed for background subtraction. Even highly robust background subtraction and shadow elimination algorithms produce erroneous outputs at times with missing body portions, which consequently affect the recognition performance. Frame Difference Energy Image (FDEI) reconstruction is performed to alleviate the detrimental effect of improperly extracted silhouettes and to make the recognition method robust to partial incompleteness. Subsequently, features are extracted via two methods and fed to the HMM based classifier which uses Viterbi decoding and Baum-Welch algorithm to compute similarity scores and carry out identification. The direct method uses extracted wavelet features directly for classification while the indirect method maps the higher-dimensional features into a lower dimensional space by means of a Frame-to-Exemplar-Distance (FED) vector. The FED uses the distance measure between pre-determined exemplars and the feature vectors of the current frame as an identification criterion. This work achieves an overall sensitivity of 86.44 % and 71.39 % using the direct and indirect approaches respectively. Also, variation in recognition performance is observed with change in the viewing angle and N and optimal performance is obtained when the path of subject parallel to camera axis (viewing angle of 0 degree) and at N = 5. The maximum recognition accuracy levels of 86.44 % and 80.93 % with and without FDEI reconstruction respectively also demonstrate the significance of FDEI reconstruction step

Likelihood-Based Inference and Model Selection for Discrete-Time Finite State-Space Hidden Markov Models

Τα Κρυμμένα Μαρκοβιανά Μοντέλα (ΚΜΜ) είναι μία από τις πιο καρποφόρες ιδέες στατιστικής μοντελοποίησης που έχει αναπτυχθεί τα τελευταία πενήντα χρόνια. Η χρήση λανθανουσών καταστάσεων καθιστά τα ΚΜΜ αρκετά γενικού χαρακτήρα για να διαχειριστούν ένα ευρύ φάσμα πολύπλοκων πραγματικών χρονοσειρών, ενώ η σχετικά απλή δομή εξάρτησής τους επιτρέπει τη χρήση αποτελεσματικών υπολογιστικών διαδικασιών. Αυτή η διπλωματική εργασία ασχολείται με την παρουσίαση Κλασικών και Μπεϋζιανών μεθόδων συμπερασματολογίας και επιλογής μοντέλου για ΚΜΜ. Στη συνέχεια, αυτές οι μέθοδοι εφαρμόζονται σε πραγματικά και προσομοιωμένα δεδομένα με στόχο να διαπιστωθεί η ακρίβεια και η αποτελεσματικότητά τους. Τα ΚΜΜ ανήκουν σε μια γενικότερη κλάση μοντέλων που αναφέρονται ως προβλήματα ελλιπών δεδομένων. Στο πλαίσιο της κλασικής στατιστικής, ο αλγόριθμος Expectation-Maximisation (EM) προσεγγίζει την εκτιμήτρια μέγιστης πιθανοφάνειας (ΕΜΠ) του διανύσματος των παραμέτρων, ενώ, στο πλαίσιο της Μπεϋζιανής στατιστικής, οι μέθοδοι Markov Chain Monte Carlo (MCMC) και συγκεκριμένα ο πλήρως δεσμευμένος δειγματολήπτης Gibbs, εφαρμόζονται για την προσέγγιση της εκ των υστέρων κατανομής του διανύσματος των παραμέτρων. Αυτές οι μέθοδοι εφαρμόζονται πρώτα για την εκτίμηση παραμέτρων σε μοντέλα πεπερασμένων μίξεων κατανομών, τα οποία μπορούν να θεωρηθούν ως ειδικές περιπτώσεις ΚΜΜ, όπου δεν επιτρέπεται καμία εξάρτηση μεταξύ διαδοχικών παρατηρήσεων. Στην περίπτωση των ΚΜΜ μια μορφή αμφίδρομης αναδρομικής διαδικασίας είναι επιπλέον απαραίτητη για τον υπολογισμό της δεσμευμένης κατανομής των κρυφών μεταβλητών, δεδομένων των παρατηρήσεων. Αυτός ο αλγόριθμος Forward-Backward μπορεί να συνδυαστεί είτε με τον αλγόριθμο EM είτε με κάποια μέθοδο MCMC για εκτίμηση παραμέτρων. Τέλος, εξετάζουμε μεθόδους για επιλογή του αριθμού των κρυφών καταστάσεων σε ένα ΚΜΜ. Η κλασική προσέγγιση συνήθως περιλαμβάνει την προσέγγιση των γενικευμένων λόγων πιθανοφανειών μέσω κάποιας τεχνικής bootstrap, ενώ η Μπεϋζιανή προσέγγιση στηρίζεται είτε σε δια-διαστατικές μεθόδους MCMC, οι οποίες περιλαμβάνουν κινήσεις μεταξύ διαφορετικών μοντέλων μαζί με εκτίμηση παραμέτρων, είτε σε μεθόδους προσομοίωσης για την προσέγγιση των περιθωρίων πιθανοφανειών των συγκρινόμενων μοντέλων.Hidden Markov Μodels (HMMs) are one of the most fruitful statistical modelling concepts that have appeared in the last fifty years. The use of latent states makes HMMs generic enough to handle a wide array of complex real-world time series, while the relatively straightforward dependence structure still allows for the use of efficient computational procedures. This dissertation concerns itself with the presentation of frequentist and Bayesian methods for statistical inference and model selection in the context of HMMs. These methods are, then, applied on real and simulated data in order to gauge their accuracy and efficiency. HMMs belong in a general class of models referred to as missing data problems. In the context of frequentist statistics, the Expectation-Maximisation (EM) algorithm approximates the maximum likelihood estimator (MLE) of the parameter vector in a missing data problem, whereas, in the framework of Bayesian statistics, Markov Chain Monte Carlo (MCMC) methods, especially the full-conditional Gibbs sampler, are applicable to approximate the posterior distribution of the parameter vector. These methods are first applied for parameter estimation in finite mixture models, which may be regarded as special cases of HMMs, where no dependence is allowed whatsoever between subsequent observations. In the case of HMMs some form of forward-backward recursion is additionally required in order to compute the conditional distribution of the hidden variables, given the observations. This so called Forward-Backward algorithm may be combined either with the EM algorithm or some MCMC method for parameter estimation. Lastly, we examine methods for selecting the number of hidden states in an HMM. The frequentist approach usually entails the approximation of the generalised likelihood-ratio (LR) statistics through some bootstrap technique, while the Bayesian approach relies either on trans-dimensional MCMC methods, which incorporate moves between different models along with parameter estimation, or on simulation methods to approximate the marginal likelihoods of the competing models

Noise modeling for standard CENELEC A-band power line communication channel

Power line communications (PLC) usage of low-voltage electrical power supply network as a medium of communication provides an alternative for the telecommunication access and in-house communication. Historically, power lines were majorly used for controlling appliances, however, with recent technology advancements power lines are now able to compete favorably and successfully with other relatively stable home automation and networking technologies like fixed line and wireless. Regardless of the advantages PLC has to offer, like every other communication technology, it has its own technical challenges it must overcome to be fully deployed and maximize its full potential. Such challenges includes noise, which can originate from appliances connected across the network or can be coupled unto the network. Harmful interference to other wireless spectrum users such as broadcast stations, and signal attenuation are other challenges faced by usage of the power line as a communication medium. PLC suffers the risk of not living up to its full development as a reliable means of communication if proper understanding of the channel potential and characteristic is not known. Therefore, understanding of the channel potential and characteristics can be obtained through measurement and modeling of the PLC channel. This model and measurements of the channel characteristics can then be utilized in designing a good PLC system which is able to withstand and mitigate the effect of the different kind of noise and disturbance present on the PLC network. This research therefore aims at formulizing and modeling the error pattern/behavior of noise and disturbances of an in-house CENELEC A-band based on experimental measurements. This is achieved by carrying out a real time experimental measurement of noise over a complete day to show the noise behavior. Error sequences are then generated from the measurement for the different classes of noise present on the CENELEC A-band and the use of Fritchman model, a Markovian chain model, is then employed to model the CENELEC A-band channel. This involves the use of Baum-Welch algorithm (an iterative algorithm) to estimate the model parameters of the three-state Markovian Fritchman model assumed. This precise channel model can then be used to design a good PLC system and facilitate the design of efficient coding and/or modulation schemes to enhance reliable communication on the PLC network. Therefore, answering the question of “how to formulize and model the error pattern/behavior of noise and disturbances of an in-house CENELEC A-band based on experimental measurements”

Short-Term Visual Object Tracking in Real-Time

In the thesis, we propose two novel short-term object tracking methods, the Flock of Trackers (FoT) and the Scale-Adaptive Mean-Shift (ASMS), a framework for fusion of multiple trackers and detector and contributions to the problem of tracker evaluation within the Visual Object Tracking (VOT) initiative. The Flock of Trackers partitions the object of interest to an equally sized parts. For each part, the FoT computes an optical flow correspondence and estimates its reliability. Reliable correspondences are used to robustly estimates a target pose using RANSAC technique, which allows for range of complex rigid transformation (e.g. affine transformation) of a target. The scale-adaptive mean-shift tracker is a gradient optimization method that iteratively moves a search window to the position which minimizes a distance of a appearance model extracted from the search window to the target model. The ASMS propose a theoretically justified modification of the mean-shift framework that addresses one of the drawbacks of the mean-shift trackers which is the fixed size search window, i.e. target scale. Moreover, the ASMS introduce a technique that incorporates a background information into the gradient optimization to reduce tracker failures in presence of background clutter. To take advantage of strengths of the previous methods, we introduce a novel tracking framework HMMTxD that fuses multiple tracking methods together with a proposed feature-based online detector. The framework utilizes a hidden Markov model (HMM) to learn online how well each tracking method performs using sparsely ”annotated” data provided by a detector, which are assumed to be correct, and confidence provided by the trackers. The HMM estimates the probability that a tracker is correct in the current frame given the previously learned HMM model and the current tracker confidence. This tracker fusion alleviates the drawbacks of the individual tracking methods since the HMMTxD learns which trackers are performing well and switch off the rest. All of the proposed trackers were extensively evaluated on several benchmarks and publicly available tracking sequences and achieve excellent results in various evaluation criteria. The FoT achieved state-of-the-art performance in the VOT2013 benchmark, finishing second. Today, the FoT is used as a building block in complex applications such as multi-object tracking frameworks. The ASMS achieved state-of-the-art results in the VOT2015 benchmark and was chosen as the best performing method in terms of a trade-off between performance and running time. The HMMTxD demonstrated state-of-the-art performance in multiple benchmarks (VOT2014, VOT2015 and OTB). The thesis also contributes, and provides an overview, to the Visual Object Tracking (VOT) evaluation methodology. This methodology provides a means for unbiased comparison of different tracking methods across publication, which is crucial for advancement of the state-of-the-art over a longer timespan and also provides a tools for deeper performance analysis of tracking methods. Furthermore, a annual workshops are organized on major computer vision conferences, where the authors are encouraged to submit their novel methods to compete against each other and where the advances in the visual object tracking are discussed.Katedra kybernetik

Meshless Direct Numerical Simulation of Turbulent Incompressible Flows

A meshless direct pressure-velocity coupling procedure is presented to perform Direct Numerical Simulations (DNS) and Large Eddy Simulations (LES) of turbulent incompressible flows in regular and irregular geometries. The proposed method is a combination of several efficient techniques found in different Computational Fluid Dynamic (CFD) procedures and it is a major improvement of the algorithm published in 2007 by this author. This new procedure has very low numerical diffusion and some preliminary calculations with 2D steady state flows show that viscous effects become negligible faster that ever predicted numerically. The fundamental idea of this proposal lays on several important inconsistencies found in three of the most popular techniques used in CFD, segregated procedures, streamline-vorticity formulation for 2D viscous flows and the fractional-step method, very popular in DNS/LES. The inconsistencies found become important in elliptic flows and they might lead to some wrong solutions if coarse grids are used. In all methods studied, the mathematical basement was found to be correct in most cases, but inconsistencies were found when writing the boundary conditions. In all methods analyzed, it was found that it is basically impossible to satisfy the exact set of boundary conditions and all formulations use a reduced set, valid for parabolic flows only. For example, for segregated methods, boundary condition of normal derivative for pressure zero is valid only in parabolic flows. Additionally, the complete proposal for mass balance correction is right exclusively for parabolic flows. In the streamline-vorticity formulation, the boundary conditions normally used for the streamline function, violates the no-slip condition for viscous flow. Finally, in the fractional-step method, the boundary condition for pseudo-velocity implies a zero normal derivative for pressure in the wall (correct in parabolic flows only) and, when the flows reaches steady state, the procedure does not guarantee mass balance. The proposed procedure is validated in two cases of 2D flow in steady state, backward-facing step and lid-driven cavity. Comparisons are performed with experiments and excellent agreement was obtained in the solutions that were free from numerical instabilities. A study on grid usage is done. It was found that if the discretized equations are written in terms of a local Reynolds number, a strong criterion can be developed to determine, in advance, the grid requirements for any fluid flow calculation. The 2D-DNS on parallel plates is presented to study the basic features present in the simulation of any turbulent flow. Calculations were performed on a short geometry, using a uniform and very fine grid to avoid any numerical instability. Inflow conditions were white noise and high frequency oscillations. Results suggest that, if no numerical instability is present, inflow conditions alone are not enough to sustain permanently the turbulent regime. Finally, the 2D-DNS on a backward-facing step is studied. Expansion ratios of 1.14 and 1.40 are used and calculations are performed in the transitional regime. Inflow conditions were white noise and high frequency oscillations. In general, good agreement is found on most variables when comparing with experimental data