433 research outputs found
Hand gesture spotting and recognition using HMMs and CRFs in color image sequences
Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2010von Mahmoud Othman Selim Mahmoud Elmezai
Review of constraints on vision-based gesture recognition for human–computer interaction
The ability of computers to recognise hand gestures visually is essential for progress in human-computer interaction. Gesture recognition has applications ranging from sign language to medical assistance to virtual reality. However, gesture recognition is extremely challenging not only because of its diverse contexts, multiple interpretations, and spatio-temporal variations but also because of the complex non-rigid properties of the hand. This study surveys major constraints on vision-based gesture recognition occurring in detection and pre-processing, representation and feature extraction, and recognition. Current challenges are explored in detail
Deep Learning-Based Action Recognition
The classification of human action or behavior patterns is very important for analyzing situations in the field and maintaining social safety. This book focuses on recent research findings on recognizing human action patterns. Technology for the recognition of human action pattern includes the processing technology of human behavior data for learning, technology of expressing feature values ​​of images, technology of extracting spatiotemporal information of images, technology of recognizing human posture, and technology of gesture recognition. Research on these technologies has recently been conducted using general deep learning network modeling of artificial intelligence technology, and excellent research results have been included in this edition
Computational Models for the Automatic Learning and Recognition of Irish Sign Language
This thesis presents a framework for the automatic recognition of Sign Language
sentences. In previous sign language recognition works, the issues of;
user independent recognition, movement epenthesis modeling and automatic
or weakly supervised training have not been fully addressed in a single recognition
framework. This work presents three main contributions in order to
address these issues.
The first contribution is a technique for user independent hand posture
recognition. We present a novel eigenspace Size Function feature which is
implemented to perform user independent recognition of sign language hand
postures.
The second contribution is a framework for the classification and spotting
of spatiotemporal gestures which appear in sign language. We propose a
Gesture Threshold Hidden Markov Model (GT-HMM) to classify gestures
and to identify movement epenthesis without the need for explicit epenthesis
training.
The third contribution is a framework to train the hand posture and spatiotemporal
models using only the weak supervision of sign language videos
and their corresponding text translations. This is achieved through our proposed
Multiple Instance Learning Density Matrix algorithm which automatically
extracts isolated signs from full sentences using the weak and noisy
supervision of text translations. The automatically extracted isolated samples
are then utilised to train our spatiotemporal gesture and hand posture
classifiers.
The work we present in this thesis is an important and significant contribution
to the area of natural sign language recognition as we propose a
robust framework for training a recognition system without the need for
manual labeling
Portuguese sign language recognition via computer vision and depth sensor
Sign languages are used worldwide by a multitude of individuals. They are mostly used by the deaf communities and their teachers, or people associated with them by ties of friendship or family. Speakers are a minority of citizens, often segregated, and over the years not much attention has been given to this form of communication, even by the scientific community. In fact, in Computer Science there is some, but limited, research and development in this area. In the particular case of sign Portuguese Sign Language-PSL that fact is more evident and, to our knowledge there isn’t yet an efficient system to perform the automatic recognition of PSL signs. With the advent and wide spreading of devices such as depth sensors, there are new possibilities to address this problem.
In this thesis, we have specified, developed, tested and preliminary evaluated, solutions that we think will bring valuable contributions to the problem of Automatic Gesture Recognition, applied to Sign Languages, such as the case of Portuguese Sign Language.
In the context of this work, Computer Vision techniques were adapted to the case of Depth Sensors. A proper gesture taxonomy for this problem was proposed, and techniques for feature extraction, representation, storing and classification were presented. Two novel algorithms to solve the problem of real-time recognition of isolated static poses were specified, developed, tested and evaluated. Two other algorithms for isolated dynamic movements for gesture recognition (one of them novel), have been also specified, developed, tested and evaluated. Analyzed results compare well with the literature.As LÃnguas Gestuais são utilizadas em todo o Mundo por uma imensidão de indivÃduos. Trata-se na sua grande maioria de surdos e/ou mudos, ou pessoas a eles associados por laços familiares de amizade ou professores de LÃngua Gestual. Tratando-se de uma minoria, muitas vezes segregada, não tem vindo a ser dada ao longo dos anos pela comunidade cientÃfica, a devida atenção a esta forma de comunicação.
Na área das Ciências da Computação existem alguns, mas poucos trabalhos de investigação e desenvolvimento. No caso particular da LÃngua Gestual Portuguesa - LGP esse facto é ainda mais evidente não sendo nosso conhecimento a existência de um sistema eficaz e efetivo para fazer o reconhecimento automático de gestos da LGP.
Com o aparecimento ou massificação de dispositivos, tais como sensores de profundidade, surgem novas possibilidades para abordar este problema.
Nesta tese, foram especificadas, desenvolvidas, testadas e efectuada a avaliação preliminar de soluções que acreditamos que trarão valiosas contribuições para o problema do Reconhecimento Automático de Gestos, aplicado à s LÃnguas Gestuais, como é o caso da LÃngua Gestual Portuguesa.
Foram adaptadas técnicas de Visão por Computador ao caso dos Sensores de Profundidade.
Foi proposta uma taxonomia adequada ao problema, e apresentadas técnicas para a extração, representação e armazenamento de caracterÃsticas. Foram especificados, desenvolvidos, testados e avaliados dois algoritmos para resolver o problema do reconhecimento em tempo real de poses estáticas isoladas. Foram também especificados, desenvolvidos, testados e avaliados outros dois algoritmos para o Reconhecimento de Movimentos Dinâmicos Isolados de Gestos(um deles novo).Os resultados analisados são comparáveis à literatura.Las lenguas de Signos se utilizan en todo el Mundo por una multitud de personas. En su mayorÃa son personas sordas y/o mudas, o personas asociadas con ellos por vÃnculos de amistad o familiares y profesores de Lengua de Signos. Es una minorÃa de personas, a menudo segregadas, y no se ha dado en los últimos años por la comunidad cientÃfica, la atención debida a esta forma de comunicación.
En el área de Ciencias de la Computación hay alguna pero poca investigación y desarrollo. En el caso particular de la Lengua de Signos Portuguesa - LSP, no es de nuestro conocimiento la existencia de un sistema eficiente y eficaz para el reconocimiento automático.
Con la llegada en masa de dispositivos tales como Sensores de Profundidad, hay nuevas posibilidades para abordar el problema del Reconocimiento de Gestos.
En esta tesis se han especificado, desarrollado, probado y hecha una evaluación preliminar de soluciones, aplicada a las Lenguas de Signos como el caso de la Lengua de Signos Portuguesa - LSP.
Se han adaptado las técnicas de Visión por Ordenador para el caso de los Sensores de Profundidad.
Se propone una taxonomÃa apropiada para el problema y se presentan técnicas para la extracción, representación y el almacenamiento de caracterÃsticas.
Se desarrollaran, probaran, compararan y analizan los resultados de dos nuevos algoritmos para resolver el problema del Reconocimiento Aislado y Estático de Posturas. Otros dos algoritmos (uno de ellos nuevo) fueran también desarrollados, probados, comparados y analizados los resultados, para el Reconocimiento de Movimientos Dinámicos Aislados de los Gestos
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