Li, Sai Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 91-96). / Abstracts in English and Chinese. / Abstract --- p.2 / List of Figures --- p.6 / Acknowledgement --- p.10 / Chapter Chapter 1 --- Introduction --- p.11 / Chapter 1.1 --- Problem definition --- p.11 / Chapter 1.2. --- Proposed algorithm --- p.12 / Chapter 1.3. --- Main features --- p.14 / Chapter Chapter 2 --- Literature Review --- p.16 / Chapter 2.1 --- Local Feature Matching techniques --- p.16 / Chapter 2.2. --- Global Iterative alignment techniques --- p.19 / Chapter 2.3 --- Other Approaches --- p.20 / Chapter Chapter 3 --- Correspondence Matching --- p.21 / Chapter 3.1 --- Fundamental Techniques --- p.24 / Chapter 3.1.1 --- Geodesic Distance Approximation --- p.24 / Chapter 3.1.1.1 --- Dijkstra ´ةs algorithm --- p.25 / Chapter 3.1.1.2 --- Wavefront Propagation --- p.26 / Chapter 3.1.2 --- Farthest Point Sampling --- p.27 / Chapter 3.1.3 --- Curvature Estimation --- p.29 / Chapter 3.1.4 --- Radial Basis Function (RBF) --- p.32 / Chapter 3.1.5 --- Multi-dimensional Scaling (MDS) --- p.35 / Chapter 3.1.5.1 --- Classical MDS --- p.35 / Chapter 3.1.5.2 --- Fast MDS --- p.38 / Chapter 3.2 --- Matching Processes --- p.40 / Chapter 3.2.1 --- Posture Alignment --- p.42 / Chapter 3.2.1.1 --- Sign Flip Correction --- p.43 / Chapter 3.2.1.2 --- Input model Alignment --- p.49 / Chapter 3.2.2 --- Surface Fitting --- p.52 / Chapter 3.2.2.1 --- Optimizing Surface Fitness --- p.54 / Chapter 3.2.2.2 --- Optimizing Surface Smoothness --- p.56 / Chapter 3.2.3 --- Feature Matching Refinement --- p.59 / Chapter 3.2.3.1 --- Feature descriptor --- p.61 / Chapter 3.2.3.3 --- Feature Descriptor matching --- p.63 / Chapter Chapter 4 --- Experimental Result --- p.66 / Chapter 4.1 --- Result of the Fundamental Techniques --- p.66 / Chapter 4.1.1 --- Geodesic Distance Approximation --- p.67 / Chapter 4.1.2 --- Farthest Point Sampling (FPS) --- p.67 / Chapter 4.1.3 --- Radial Basis Function (RBF) --- p.69 / Chapter 4.1.4 --- Curvature Estimation --- p.70 / Chapter 4.1.5 --- Multi-Dimensional Scaling (MDS) --- p.71 / Chapter 4.2 --- Result of the Core Matching Processes --- p.73 / Chapter 4.2.1 --- Posture Alignment Step --- p.73 / Chapter 4.2.2 --- Surface Fitting Step --- p.78 / Chapter 4.2.3 --- Feature Matching Refinement --- p.82 / Chapter 4.2.4 --- Application of the proposed algorithm --- p.84 / Chapter 4.2.4.1 --- Design Automation in Garment Industry --- p.84 / Chapter 4.3 --- Analysis --- p.86 / Chapter 4.3.1 --- Performance --- p.86 / Chapter 4.3.2 --- Accuracy --- p.87 / Chapter 4.3.3 --- Approach Comparison --- p.88 / Chapter Chapter 5 --- Conclusion --- p.89 / Chapter 5.1 --- Strength and contributions --- p.89 / Chapter 5.2 --- Limitation and future works --- p.90 / References --- p.91

Image processing systems used in Vision Assisted Dimensional Inspection usually output a set of boundary pixels representing the part edges. This boundary information must be divided into several subsets representing the various edges of the actual object, so that comparisons with the nominal part can be made. The purpose of this project is to devise a method to divide the set of pixels obtained from the image processing system into subsets of pixels. Each of these subsets represent an edge in the contour of the actual object. This method must also detect transition points between the adjacent features. This project addresses only planar contours which are composed of straight and circular edges. Two new algorithms have been developed, the first algorithm detects the transition points involving straight edges and the second algorithm finds the transition points when circular features are involved. In addition, the measured features are also matched with their nominal counterparts. The performance of these algorithms are demonstrated by simulated as well as images from the vision system.

In pattern recognition applications, it is usually important that the same identification be given for a pattern, independent of a variety of positions, rotations and /or distortions of the pattern within the recognition device's field of view. This research relates to development of a preprocessor for a neural network character recognition system, where the role of the preprocessor is to assist in minimizing the difficulties related to variations of position and rotations of a character within the field of view. The preprocessor explored here was suggested in 1970' (Lendaris & Stanly, 1970), and is implemented here with more recent advances in neural network and discrete computation technologies. The preprocessor consists of calculating the two-dimensional Fourier transform of the image (current hardware technology allows this to occur in less than 100 ms for a 256x256 pixels image , on a PC based machine with accelerator card), and then taking certain measurements on the transformed image. These measurements are given to the neural network, which processes the data to provide the character identification. Introduction of the preprocessor is shown to yield a great reduction in sensitivity to image translation and/or rotation.

Abstract not available

Thesis (M.S.)--Rochester Institute of Technology, 2006. / Typescript. Includes bibliographical references (leaves 136-138).

<p>This thesis presents a system for performing whole gesture recognition for South African Sign Language. The system uses feature vectors combined with Hidden Markov models. In order to constuct a feature vector, dynamic segmentation must occur to extract the signer&rsquo / s hand movements. Techniques and methods for normalising variations that occur when recording a signer performing a gesture, are investigated. The system has a classification rate of 69%</p>

<p>The SASL project at the University of the Western Cape aims at developing a fully automated translation system between English and South African Sign Language (SASL). Three important aspects of this system require SASL documentation and knowledge. These are: recognition of SASL from a video sequence, linguistic translation between SASL and English and the rendering of SASL. Unfortunately, SASL documentation is a scarce resource and no official or complete documentation exists. This research focuses on creating an online collaborative video annotation knowledge management system for SASL where various members of the community can upload SASL videos to and annotate them in any of the sign language notation systems, SignWriting, HamNoSys and/or Stokoe. As such, knowledge about SASL structure is pooled into a central and freely accessible knowledge base that can be used as required. The usability and performance of the system were evaluated. The usability of the system was graded by users on a rating scale from one to five for a specific set of tasks. The system was found to have an overall usability of 3.1, slightly better than average. The performance evaluation included load and stress tests which measured the system response time for a number of users for a specific set of tasks. It was found that the system is stable and can scale up to cater for an increasing user base by improving the underlying hardware.</p>

<p>Recognising and estimating gestures is a fundamental aspect towards translating from a sign language to a spoken language. It is a challenging problem and at the same time, a growing phenomenon in Computer Vision. This thesis presents two approaches, an example-based and a learning-based approach, for performing integrated detection, segmentation and 3D estimation of the human upper body from a single camera view. It investigates whether an upper body pose can be estimated from a database of exemplars with labelled poses. It also investigates whether an upper body pose can be estimated using skin feature extraction, Support Vector Machines (SVM) and a 3D human body model. The example-based and learning-based approaches obtained success rates of 64% and 88%, respectively. An analysis of the two approaches have shown that, although the learning-based system generally performs better than the example-based system, both approaches are suitable to recognise and estimate upper body poses in a South African sign language recognition and translation system.</p>

Thesis (Ph. D.)--University of California, Riverside, 2009. / Includes abstract. Title from first page of PDF file (viewed March 8, 2010). Includes bibliographical references. Issued in print and online. Available via ProQuest Digital Dissertations.

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