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Thesis (Ph. D.)--University of Texas at Austin, 2002. / Typescript. Includes bibliographical references and bibliography (p. 226-234).

Thesis (Ph. D.)--University of Washington, 1996. / Vita. Includes bibliographical references (leaves [367]-391).

Thesis (MA--Applied Language Studies) -- AUT University, 2009. / Includes bibliographical references. Also held in print (viii, 120 leaves ; 30 cm.) in the Archive at the City Campus (T 419.93 PIV)

Thesis (Ph. D.)--University of Texas at Austin, 2002. / Vita. Includes bibliographical references. Available also from UMI Company.

Philosophiae Doctor - PhD / Hand motion provides a natural way of interaction that allows humans to interact not only with the environment, but also with each other. The effectiveness and accuracy of hand-tracking is fundamental to the recognition of sign language. Any inconsistencies in hand-tracking result in a breakdown in sign language communication. Hands are articulated objects, which complicates the tracking thereof. In sign language communication the tracking of hands is often challenged by the occlusion of the other hand, other body parts and the environment in which they are being tracked. The thesis investigates whether a single framework can be developed to track the hands independently of an individual from a single 2D camera in constrained and unconstrained environments without the need for any special device. The framework consists of a three-phase strategy, namely, detection, tracking and learning phases. The detection phase validates whether the object being tracked is a hand, using extended local binary patterns and random forests. The tracking phase tracks the hands independently by extending a novel data-association technique. The learning phase exploits contextual features, using the scale-invariant features transform (SIFT) algorithm and the fast library for approximate nearest neighbours (FLANN) algorithm to assist tracking and the recovering of hands from any form of tracking failure. The framework was evaluated on South African sign language phrases that use a single hand, both hands without occlusion, and both hands with occlusion. These phrases were performed by 20 individuals in constrained and unconstrained environments. The experiments revealed that integrating all three phases to form a single framework is suitable for tracking hands in both constrained and unconstrained environments, where a high average accuracy of 82,08% and 79,83% was achieved respectively.

>Magister Scientiae - MSc / Hand shape recognition is a pivotal part of any system that attempts to implement Sign Language recognition. This thesis presents a novel system which recognises hand shapes from a single camera view in 2D. By mapping the recognised hand shape from 2D to 3D,it is possible to obtain 3D co-ordinates for each of the joints within the hand using the kinematics embedded in a 3D hand avatar and smooth the transformation in 3D space between any given hand shapes. The novelty in this system is that it does not require a hand pose to be recognised at every frame, but rather that hand shapes be detected at a given step size. This architecture allows for a more efficient system with better accuracy than other related systems. Moreover, a real-time hand tracking strategy was developed that works efficiently for any skin tone and a complex background.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Peruvian Sign Language Recognition (PSL) is approached as a classification problem. Previous work has employed 2D features from the position of hands to tackle this problem. In this paper, we propose a method to construct a dataset consisting of 3D spatial positions of static gestures from the PSL alphabet, using the HTC Vive device and a well-known technique to extract 21 keypoints from the hand to obtain a feature vector. A dataset of 35, 400 instances of gestures for PSL was constructed and a novel way to extract data was stated. To validate the appropriateness of this dataset, a comparison of four baselines classifiers in the Peruvian Sign Language Recognition (PSLR) task was stated, achieving 99.32% in the average in terms of F1 measure in the best case. / Revisión por pares

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There are two main hypotheses examined in the dissertation. The first is that the physiology of the hand provides motivation for the claim that sign language handshapes can be considered easy, hard or even impossible to articulate. The second hypothesis is that easy handshapes occur more often than expected, hard handshapes occur less often than expected and impossible handshapes don't occur at all within a single sign language. These hypotheses are examined in the following ways: first, I provide a detailed explanation of the physiology of the hand from which I conclude that not all fingers are equal in skill and not all configurations a hand may assume are equally easy. Second, based on the physiology, I propose a metric for determining which handshapes are "easy" and which are "difficult". Third, I examine whether the "easy" handshapes occur more often than expected, while the "hard" handshapes occur less often than expected in the signs of two languages, American Sign Language (ASL) and Taiwan Sign Language (TSL). I conclude that the hypothesis that the "easy" handshapes occur more often than expected and the "hard" handshapes occur less often than expected is supported in approximately half of the cases.