Sign Language Recognition System Simulated for Video Captured with Smart Phone Front Camera

This works objective is to bring sign language closer to real time implementation on mobile platforms. A video database of Indian sign language is created with a mobile front camera in selfie mode. This video is processed on a personal computer by constraining the computing power to that of a smart phone with 2GB ram. Pre-filtering, segmentation and feature extraction on video frames creates a sign language feature space. Minimum distance classification of the sign feature space converts signs to text or speech. ASUS smart phone with 5M pixel front camera captures continuous sign videos containing around 240 frames at a frame rate of 30fps. Sobel edge operator’s power is enhanced with morphology and adaptive thresholding giving a near perfect segmentation of hand and head portions. Word matching score (WMS) estimates performance of the proposed method with an average WMS of around 90.58%

A Framework for Vision-based Static Hand Gesture Recognition

In today’s technical world, the intellectual computing of a efficient human-computer interaction (HCI) or human alternative and augmentative communication (HAAC) is essential in our lives. Hand gesture recognition is one of the most important techniques that can be used to build up a gesture based interface system for HCI or HAAC application. Therefore, suitable development of gesture recognition method is necessary to design advance hand gesture recognition system for successful applications like robotics, assistive systems, sign language communication, virtual reality etc. However, the variation of illumination, rotation, position and size of gesture images, efficient feature representation, and classification are the main challenges towards the development of a real time gesture recognition system. The aim of this work is to develop a framework for vision based static hand gesture recognition which overcomes the challenges of illumination, rotation, size and position variation of the gesture images. In general, a framework for gesture recognition system which consists of preprocessing, feature extraction, feature selection, and classification stages is developed in this thesis work. The preprocessing stage involves the following sub-stages: image enhancement which enhances the image by compensating illumination variation; segmentation, which segments hand region from its background image and transforms it into binary silhouette; image rotation that makes the segmented gesture as rotation invariant; filtering that effectively removes background noise and object noise from binary image and provides a well defined segmented hand gesture. This work proposes an image rotation technique by coinciding the first principal component of the segmented hand gesture with vertical axes to make it as rotation invariant. In the feature extraction stage, this work extracts xi localized contour sequence (LCS) and block based features, and proposes a combined feature set by appending LCS features with block-based features to represent static hand gesture images. A discrete wavelets transform (DWT) and Fisher ratio (F-ratio) based feature set is also proposed for better representation of static hand gesture image. To extract this feature set, DWT is applied on resized and enhanced grayscale image and then the important DWT coefficient matrices are selected as features using proposed F-ratio based coefficient matrices selection technique. In sequel, a modified radial basis function neural network (RBF-NN) classifier based on k-mean and least mean square (LMS) algorithms is proposed in this work. In the proposed RBF-NN classifier, the centers are automatically selected using k-means algorithm and estimated weight matrix is updated utilizing LMS algorithm for better recognition of hand gesture images. A sigmoidal activation function based RBF-NN classifier is also proposed here for further improvement of recognition performance. The activation function of the proposed RBF-NN classifier is formed using a set of composite sigmoidal functions. Finally, the extracted features are applied as input to the classifier to recognize the class of static hand gesture images. Subsequently, a feature vector optimization technique based on genetic algorithm (GA) is also proposed to remove the redundant and irrelevant features. The proposed algorithms are tested on three static hand gesture databases which include grayscale images with uniform background (Database I and Database II) and color images with non-uniform background (Database III). Database I is a repository database which consists of hand gesture images of 25 Danish/international sign language (D/ISL) hand alphabets. Database II and III are indigenously developed using VGA Logitech Webcam (C120) with 24 American Sign Language (ASL) hand alphabets

Machine learning methods for sign language recognition: a critical review and analysis.

Sign language is an essential tool to bridge the communication gap between normal and hearing-impaired people. However, the diversity of over 7000 present-day sign languages with variability in motion position, hand shape, and position of body parts making automatic sign language recognition (ASLR) a complex system. In order to overcome such complexity, researchers are investigating better ways of developing ASLR systems to seek intelligent solutions and have demonstrated remarkable success. This paper aims to analyse the research published on intelligent systems in sign language recognition over the past two decades. A total of 649 publications related to decision support and intelligent systems on sign language recognition (SLR) are extracted from the Scopus database and analysed. The extracted publications are analysed using bibliometric VOSViewer software to (1) obtain the publications temporal and regional distributions, (2) create the cooperation networks between affiliations and authors and identify productive institutions in this context. Moreover, reviews of techniques for vision-based sign language recognition are presented. Various features extraction and classification techniques used in SLR to achieve good results are discussed. The literature review presented in this paper shows the importance of incorporating intelligent solutions into the sign language recognition systems and reveals that perfect intelligent systems for sign language recognition are still an open problem. Overall, it is expected that this study will facilitate knowledge accumulation and creation of intelligent-based SLR and provide readers, researchers, and practitioners a roadmap to guide future direction

Mod-ϕ\phi convergence: Approximation of discrete measures and harmonic analysis on the torus

In this paper, we relate the framework of mod-φ convergence to the construction of approximation schemes for lattice-distributed random variables. The point of view taken here is the one of Fourier analysis in the Wiener algebra, allowing the computation of asymptotic equivalents of the local, Kolmogorov and total variation distances. By using signed measures instead of probability measures, we are able to construct better approximations of discrete lattice distributions than the standard Poisson approximation. This theory applies to various examples arising from combinatorics and number theory: number of cycles in permutations, number of prime divisors of a random integer, number of irreducible factors of a random polynomial, etc. Our approach allows us to deal with approximations in higher dimensions as well. In this setting, we bring out the influence of the correlations between the components of the random vectors in our asymptotic formulas

Characterization of measurements in quantum communications.

Massachusetts Institute of Technology. Dept. of Electrical Engineering. Thesis. 1974. Ph.D.MICROFICHE COPY ALSO AVAILABLE IN BARKER ENGINEERING LIBRARY.Vita.Includes bibliographical references.Ph.D

Order-Theoretic Methods for Space-Time Coding: Symmetric and Asymmetric Designs

Siirretty Doriast

Image Restoration

This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with

Effet de l'angle de flèche sur le bruit à large bande de ventilateur

Cette étude vise à comprendre la mécanique de réduction de bruit afin de mitiger le bruit large bande en utilisant l’angle de flèche tout en préservant le rendement aérodynamique. Nous avons choisi des modèles et outils de calculs afin de comprendre le comportement aérodynamique ainsi que le bruit généré par l’angle de flèche. En premier lieu, une simulation Reynolds Averaged Navier Stokes (RANS) est utilisée afin d’évaluer le champ d’écoulement. Ensuite, une méthode Lattice Boltzmann (LBM) haute-fidélité est utilisée afin de prédire la radiation sonore. LBM nous permet de déterminer la source des bruits combinés. Finalement, afin de séparer le bruit large bande généré par les turbulences, nous avons adapté le modèle d’Amiet's leading-edge afin de représenter l’angle de flèche d’un ventilateur axial. Nos résultats indiquent que le dévers de pale avant surpasse le dévers de pale arrière pour la région décrochage, la radiation sonore et la consommation énergétique lorsque les performances aérodynamique est restaurée.Le bruit produit par le ventilateur de radiateur devient une préoccupation croissante. En effet, les véhicules électriques modernes ne produisent pas le bruit engendré par les groupes motopropulseurs et moteurs traditionnels. Fondé sur une revue de littérature, nous avons classé les différentes sources de bruit ainsi que leur contribution sur le spectre acoustique. Les concepts de dévers de pale avant et arrière ont démontré un potentiel avantage de réduction de bruit large bande aux détriments du rendement aérodynamique. Par conséquent, cette approche est très peu utilisée dans l'industrie. Cette étude vise à comprendre la mécanique de réduction de bruit afin de mitiger le bruit large bande en utilisant l'angle de flèche tout en préservant le rendement aérodynamique. Nous avons choisi des modèles et outils de calculs afin de comprendre le comportement aérodynamique ainsi que le bruit généré par l'angle de flèche. En premier lieu, une simulation Reynolds Averaged Navier Stokes (RANS) est utilisée afin d'évaluer le champ d'écoulement. Ensuite, une méthode Lattice Boltzmann (LBM) haute-fidélité est utilisée afin de prédire la radiation sonore. LBM nous permet de déterminer la source des bruits combinés. Finalement, afin de séparer le bruit large bande généré par les turbulences, nous avons adapté le modèle d'Amiet's leading edge afin de représenter l'angle de flèche d'un ventilateur axial. Nos résultats indiquent que le dévers de pale avant surpasse le dévers de pale arrière pour la région décrochage, la radiation sonore et la consommation énergétique lorsque les performances aérodynamique est restaurée. Nous recommandons le dévers de pale avant afin de réduire le bruit de large bande émis par le ventilateur du radiateur. Cependant, des recherches additionnelles seront nécessaires afin d'évaluer le bruit tonal. Ces recherches pourront renforcer l'utilisation de l'angle de flèche dans la conception de pales.Abstract : The radiator fan noise is becoming a growing concern since other noise sources radiated from traditional powertrains and combustion engines are omitted in modern electric vehicles. Based on a literature review, we classified the noise sources and their contribution in noise spectra. The forward sweep and backward sweep showed a strong potential in broadband noise reduction but at the cost of loss in aerodynamic efficiency. Hence, this skepticism restrained from its wide usage in fan design. Therefore, this study aims at understanding the noise reduction mechanism so that to mitigate broadband noise using blade sweep by preserving its aerodynamic performance. The various computational tools are used to investigate the aerodynamic behavior and its associated noise in swept blades. First, an industry-friendly steady Reynolds Averaged Navier Stokes (RANS) simulation technique is assessed to investigate the flow field and later a high-fidelity, unsteady Lattice Boltzmann method (LBM) is evaluated to predict the noise radiation. LBM provides the combined knowledge of all noise sources. So, finally, to segregate broadband noise generated due to turbulence interaction, we adapted Amiet's leading-edge noise prediction tool to the swept blade of an axial fan. The results indicate that forward sweep has improved pressure rise by almost 25% than backward sweep and unswept blade when designed for similar loadings. In addition, the forward sweep has reduced noise levels by 12 dB than unswept blade. We recommend using a forward sweep to reduce broadband noise emitted by the radiator fan based on our findings. However, further research is needed to investigate tonal noise that could strengthen the usage of sweep in blade design