3 research outputs found
Self-organising maps : statistical analysis, treatment and applications.
This thesis presents some substantial theoretical analyses and optimal treatments
of Kohonen's self-organising map (SOM) algorithm, and explores the practical
application potential of the algorithm for vector quantisation, pattern classification,
and image processing. It consists of two major parts. In the first part, the SOM
algorithm is investigated and analysed from a statistical viewpoint. The proof of its
universal convergence for any dimensionality is obtained using a novel and
extended form of the Central Limit Theorem. Its feature space is shown to be an
approximate multivariate Gaussian process, which will eventually converge and
form a mapping, which minimises the mean-square distortion between the feature
and input spaces. The diminishing effect of the initial states and implicit effects of
the learning rate and neighbourhood function on its convergence and ordering are
analysed and discussed. Distinct and meaningful definitions, and associated
measures, of its ordering are presented in relation to map's fault-tolerance. The
SOM algorithm is further enhanced by incorporating a proposed constraint, or
Bayesian modification, in order to achieve optimal vector quantisation or pattern
classification. The second part of this thesis addresses the task of unsupervised
texture-image segmentation by means of SOM networks and model-based
descriptions. A brief review of texture analysis in terms of definitions, perceptions,
and approaches is given. Markov random field model-based approaches are
discussed in detail. Arising from this a hierarchical self-organised segmentation
structure, which consists of a local MRF parameter estimator, a SOM network, and
a simple voting layer, is proposed and is shown, by theoretical analysis and
practical experiment, to achieve a maximum likelihood or maximum a posteriori
segmentation. A fast, simple, but efficient boundary relaxation algorithm is
proposed as a post-processor to further refine the resulting segmentation. The class
number validation problem in a fully unsupervised segmentation is approached by
a classical, simple, and on-line minimum mean-square-error method. Experimental
results indicate that this method is very efficient for texture segmentation
problems. The thesis concludes with some suggestions for further work on SOM
neural networks
S-TREE: Self-Organizing Trees for Data Clustering and Online Vector Quantization
This paper introduces S-TREE (Self-Organizing Tree), a family of models that use unsupervised learning to construct hierarchical representations of data and online tree-structured vector quantizers. The S-TREE1 model, which features a new tree-building algorithm, can be implemented with various cost functions. An alternative implementation, S-TREE2, which uses a new double-path search procedure, is also developed. S-TREE2 implements an online procedure that approximates an optimal (unstructured) clustering solution while imposing a tree-structure constraint. The performance of the S-TREE algorithms is illustrated with data clustering and vector quantization examples, including a Gauss-Markov source benchmark and an image compression application. S-TREE performance on these tasks is compared with the standard tree-structured vector quantizer (TSVQ) and the generalized Lloyd algorithm (GLA). The image reconstruction quality with S-TREE2 approaches that of GLA while taking less than 10% of computer time. S-TREE1 and S-TREE2 also compare favorably with the standard TSVQ in both the time needed to create the codebook and the quality of image reconstruction.Office of Naval Research (N00014-95-10409, N00014-95-0G57
Segmentaci贸n y detecci贸n de objetos en im谩genes y v铆deo mediante inteligencia computacional
Finalmente, se exponen las conclusiones obtenidas tras la realizaci贸n de esta tesis y unas posibles l铆neas futuras de investigaci贸n.
Fecha de lectura de Tesis: 17 diciembre 2018.La presente tesis trata sobre el procesamiento y an谩lisis de im谩genes y video mediante sistemas inform谩ticos.
Primeramente se hace una introducci贸n, especificando contexto, objetivos y metodolog铆a. Luego se muestran los antecedentes, los fundamentos de la videovigilancia, las dificultades existentes y diversos algoritmos del estado del arte, seguido de las principales caracter铆sticas del aprendizaje profundo, transporte inteligente y sistemas con c谩mara PTZ, finalizando con la evaluaci贸n de m茅todos y distintos conjuntos de datos.
Despu茅s se muestran tres partes. La primera comenta los estudios desarrollados que tratan sobre segmentaci贸n. Aqu铆 se explican diferentes modelos desarrollados cuyo objetivo es la detecci贸n de objetos, tanto usando hardware gen茅rico o especifico como en 谩mbitos espec铆ficos, o un estudio de c贸mo influye la reducci贸n del tama帽o de las im谩genes al rendimiento de los algoritmos.
La segunda parte describe los trabajos que utilizan una c谩mara PTZ. El primero trabajo hace un seguimiento del objeto m谩s an贸malo del escenario, siendo el propio sistema el que decide cu谩les son an贸malos y cu谩les no; el segundo muestra un sistema que indica a la c谩mara los movimientos a realizar en funci贸n de la salida producida por un modelo de fondo no panor谩mico y mejorada con un gas neuronal creciente.
La tercera parte trata sobre los estudios desarrollados con relaci贸n con el transporte inteligente, como es la clasificaci贸n de los veh铆culos que aparecen en secuencias de tr谩fico. El primer trabajo aplica t茅cnicas tradicionales como segmentaci贸n y extracci贸n de rasgos; el segundo utiliza segmentaci贸n y redes convolucionales, complementado con un estudio del redimensionado de im谩genes para proveerlas en el formato necesario a cada red; y el tercero emplea un modelo que detecta y clasifica objetos, estimando posteriormente la contaminaci贸n generada por los veh铆culos