A new method for extracting lines from discrete binary images is proposed. The algorithm is capable of extracting individual lines and producing a structure-descriptive representation for every line extracted. The algorithm could be considered as an extension of Wakayama's Maximal Square Moving (MSM) algorithm[37] since pseudo maximal squares are substituted for maximal squares, but essentially, it is distinct from the MSM algorithm because squares are derived only in the most desirable direction while tracking a line. The resulting representation of a line is a set of points that are the centers of the pseudo maximal squares along the tracked line. This information is highly conducive to creating a high-level mathematical representation of the line being tracked. Examples are given for regions of a complex map. / Master of Science

This thesis concerns with the recognition of music instruments from isolated notes. Music instrument recognition is a relatively nascent problem fast gaining importance not only because of the academic value the problem provides, but also for the potential it has in being able to realize applications like music content analysis, music transcription etc. Line spectral frequencies are proposed as features for music instrument recognition and shown to perform better than Mel filtered cepstral coefficients and linear prediction cepstral coefficients. Assuming a linear model of sound production, features based on the prediction residual, which represents the excitation signal, is proposed. Four improvements are proposed for classification using Gaussian mixture model (GMM) based classifiers. One of them involves characterizing the regions of overlap between classes in the feature space to improve classification. Applications to music instrument recognition and speaker recognition are shown. An experiment is proposed for discovering the hierarchy in music instrument in a data-driven manner. The hierarchy thus discovered closely corresponds to the hierarchy defined by musicians and experts and therefore shows that the feature space has successfully captured the required features for music instrument characterization.

As the clothing industry moves away from traditional models of mass production there has been increased interest towards customised clothing. The technology to produce cost effective customised clothing is already in place however the prerequisite to customised clothing is accurate body dimensional data. In response, image capture systems have been developed which are capable of recording a three-dimensional image of the body, from which measurements and shape information may be extracted. The use of these systems for customised clothing has, to date, been limited due to issues of inaccuracy, cost and portability. To address the issue of inaccuracy a diagnostic procedure has been developed through the performance evaluation of an image capture system. By systematically evaluating physical and instrumental parameters the more relevant sources of potential error were identified and quantified and subsequently corrected to form a `closed loop' experimental procedure. A systematic test procedure is therefore presented which may be universally applied to image capture systems working on the same principle. The methodology was based upon the isolation and subsequent testing of variables that were thought to be potential sources of error. The process therefore included altering the physical parameters of the target object in relation to the image capture system and amending the configuration and calibration settings within the system. From the evaluation the most relevant sources of error were identified as the cosine effect, measurement point displacement, the dimensional differences between views and the influence of the operator in measurement. The test procedure proved to be effective in both evaluating the performance of the system under investigation and in enabling the quantification of errors. Both random and systematic errors were noted which may be quantified or corrected to enable improved accuracy in the measured results. Recommendations have been made for the improvement of the performance of the current image capture system these include the integration of a cosine effect correction algorithm and suggestions for the automation of the image alignment process. The limitations of the system such as its reliance on manual intervention for both the measurement and stitching processes, are discussed, as is its suitability for providing dimensional information for bespoke clothing production. Recommendations are also made for the creation of an automated test procedure for testing the performance of alternative image capture systems, which involves evaluating the accuracy of object replication both for multiple and single image capture units using calibration objects which combine a range of surfaces.

The focus of this dissertation is a methodology that enables computer systems to classify different up-front images of human faces as belonging to one of the individuals to which the system has been exposed previously. The images can present variance in size, location of the face, orientation, facial expressions, and overall illumination. The approach to the problem taken in this dissertation can be classified as analytic as the shapes of individual features of human faces are examined separately, as opposed to holistic approaches to face recognition. The outline of the features is used to construct signature functions. These functions are then magnitude-, period-, and phase-normalized to form a translation-, size-, and rotation-invariant representation of the features. Vectors of a limited number of the Fourier decomposition coefficients of these functions are taken to form the feature vectors representing the features in the corresponding vector space. With this approach no computation is necessary to enforce the translational, size, and rotational invariance at the stage of recognition thus reducing the problem of recognition to the k-dimensional clustering problem. A recognizer is specified that can reliably classify the vectors of the feature space into object classes. The recognizer made use of the following principle: a trial vector is classified into a class with the greatest number of closest vectors (in the sense of the Euclidean distance) among all vectors representing the same feature in the database of known individuals. A system based on this methodology is implemented and tried on a set of 50 pictures of 10 individuals (5 pictures per individual). The recognition rate is comparable to that of most recent results in the area of face recognition. The methodology presented in this dissertation is also applicable to any problem of pattern recognition where patterns can be represented as a collection of black shapes on the white background.

Thesis (M.Sc.(Electrical Engineering))--University of the Witwatersrand, Faculty of Engineering, 1992.

Research on Multiple Object Tracking (MOT) has typically involved 2D displays where stimuli move in a single depth plane. However, under natural conditions, objects move in 3D which adds complexity to tracking. According to the spatial interference model, tracked objects have an inhibitory surround that when crossed causes tracking errors. How do these inhibitory fields translate to 3D space? Does multiple object tracking operate on a 2D planar projection, or is it in fact 3D? To investigate this, we used a fully immersive virtual-reality environment where participants were required to track 1 to 4 moving objects. We compared performance to a condition where participants viewed the same stimuli on a computer screen with monocular depth cues. Results suggest that participants were more accurate in the VR condition than the computer screen condition. This demonstrates interference is negligent when the objects are spatially distant, yet proximate within the 2D projection. / Includes bibliography. / Thesis (M.A.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Molecular dynamics is a computer simulation technique for expressing the ultimate details of individual particle motions and can be used in many fields, such as chemical physics, materials science, and the modeling of biomolecules. In this thesis, we study visualization and pattern mining in molecular dynamics simulation. The molecular data set has a large number of atoms in each frame and range of frames. The features of the data set include atom ID; frame number; position in x, y, and z plane; charge; and mass. The three main challenges of this thesis are to display a larger number of atoms and range of frames, to visualize this large data set in 3-dimension, and to cluster the abnormally shifting atoms that move with the same pace and direction in different frames. Focusing on these three challenges, there are three contributions of this thesis. First, we design an abnormal pattern mining and visualization framework for molecular dynamics simulation. The proposed framework can visualize the clusters of abnormal shifting atom groups in a three-dimensional space, and show their temporal relationships. Second, we propose a pattern mining method to detect abnormal atom groups which share similar movement and have large variance compared to the majority atoms. We propose a general molecular dynamics simulation tool, which can visualize a large number of atoms, including their movement and temporal relationships, to help domain experts study molecular dynamics simulation results. The main functions for this visualization and pattern mining tool include atom number, cluster visualization, search across different frames, multiple frame range search, frame range switch, and line demonstration for atom motions in different frames. Therefore, this visualization and pattern mining tool can be used in the field of chemical physics, materials science, and the modeling of biomolecules for the molecular dynamic simulation outcomes. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Submitted by Maria Cristina (library@lncc.br) on 2015-09-25T15:37:17Z No. of bitstreams: 1 Tiene-Dissettacao.pdf: 10301534 bytes, checksum: 1bbdd0329f315edc2564176156c20ee3 (MD5) / Approved for entry into archive by Maria Cristina (library@lncc.br) on 2015-09-25T15:37:32Z (GMT) No. of bitstreams: 1 Tiene-Dissettacao.pdf: 10301534 bytes, checksum: 1bbdd0329f315edc2564176156c20ee3 (MD5) / Made available in DSpace on 2015-09-25T15:37:48Z (GMT). No. of bitstreams: 1 Tiene-Dissettacao.pdf: 10301534 bytes, checksum: 1bbdd0329f315edc2564176156c20ee3 (MD5) Previous issue date: 2015-03-26 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / The main contributions of this dissertations are divided in two parts. The main goal of the first one is to investigate the efficiency of ranking techniques for tensor components in gender and facial expression (happiness versus neutral) experiments for classification and reconstruction problems in multilinear subspace learning. We will verify the consequences of ranking techniques in subspace learning through Fisher criterion, by estimating the covariance structure of the database and by using weights generated through separating hyperplanes, such as support vector machine (SVM) and linear discriminant analysis (LDA). The latter is named tensor discriminant principal component analysis (TDPCA). This analysis will be performed in the context of multilinear principal components analysis (MPCA) and concurrent subspace analysis (CSA), which are known techniques for multilinear subspace learning. The former follows the principal components analysis methodology, that centers the samples before the subspace learning computation while the latter performs the learning procedure using raw data. In the second part, we will analyze the influence of weights computed using SVM, LDA and Fisher criterion as a prior information to generate new tensor components in the context of MPCA. This new supervised subspace learning technique, named weighted MPCA (WMPCA), is combined with the ranking methods to re-orient weighted tensor components generating the WTDPCA subspaces. We apply the WMPCA and WTDPCA frameworks, for face image analysis. The results show the efficiency of WMPCA and WTDPCA subspaces to distinguish sample groups in classification tasks as well as some drawbacks between global and local patterns for reconstruction. We also address theoretical issues related to the connection between MPCA and CSA, as well as foundations in multilinear subspace learning problems; that is, the corresponding covariance structure and discriminant analysis. / As principais contribuições desta dissertação estão divididas em duas partes. O principal objetivo da primeira parte é investigar a eficiência das técnicas de ordenação para componentes tensoriais em experimentos de genero e expressão facial (felicidade contra neutra) para problemas de classificação e reconstrução em espaços multilineares. Nós verificaremos as consequências das técnicas de ordenação tensoriais via estrutura espectral dos dados, bem como usando pesos gerados através de hiperplanos de sepação, tais como SVM (support vector machine) e LDA (linear discriminant analysis), além de, do critério de Fisher. Esta análise foi realizada no contexto do MPCA (multilinear principal components analysis) e CSA (concurrent subspace analysis), as quais, são técnicas conhecidas na área de aprendizagem de subespaços multilineares. A primeira segue a metodologia do (PCA) (principal components analysis),que centraliza as amostras antes de computar o subespaço enquanto que a última realiza o procedimento de aprendizagem usando os dados brutos. Na segunda parte, nós analisamos a influência dos pesos computados usando SVM, LDA e critério de Fisher como uma informação a priori para gerar novas componentes tensoriais no contexto do MPCA. Esta nova técnica de aprendizagem supervisionada, que chamamos de WMPCA (weighted MPCA), é combinada com o método de ordenação para re-ordenar componentes tensoriais ponderadas computadas pelo WMPCA. Nós aplicamos a técnica combinada, denominada WTDPCA (weighted tensor discriminant principal components analysis), bem como o WMPCA, para análise de imagens de faces. Os resultados mostram a eficiência dos subespaços gerados para distinguir grupos de amostras em tarefas de classificação bem como questões inerentes a padrões globais e locais na reconstrução. Nós também abordamos aspectos teóricos relacionadas a conexão entre MPCA e CSA, bem como fundamentos relacionados a estrutura espectral e análise discriminante em problemas de aprendizagem multilineares.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Vita. / Bibliography: leaves 365-385. / by Theodore Thomas Kuklinski. / Ph.D.

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Stanley Richard Rotman. / B.S.