Detalhes bibliográficos
| Ano de defesa: |
2009 |
| Autor(a) principal: |
Kuester Neto, Paulo
 |
| Orientador(a): |
Giorno, Fernando Antonio de Castro |
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Pontifícia Universidade Católica de São Paulo
|
| Programa de Pós-Graduação: |
Programa de Estudos Pós-Graduados em Tecnologias da Inteligência e Design Digital
|
| Departamento: |
Faculdade de Ciências Exatas e Tecnologia
|
| País: |
BR
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
https://tede2.pucsp.br/handle/handle/18230
|
Resumo: |
This project is part of the research line Collective Intelligence and Interactive Environments and aims to investigate modes of pattern recognition and classification in three-dimensional images using artificial neural networks. To achieve this, three-dimensional images will be submitted to a connection is system based on Artificial Neural Networks according to a back propagation algorithm used as the basis for training, in order to obtain patterns that are common among these images. This work aims to contribute to image analysis so that it can be applied to research, from forest mapping and virtual worlds construction to prognostics and/or diagnoses in health-related areas, in which, due to variances and imperfections in images that are said to be similar, it is not possible to use simple algorithms that recognize similarities between them. In light of the theoretical presuppositions discussed in chapter 2 and to the state-of-the-art approached in chapter 3, the characteristics, organization modes, learning algorithms and free parameters of this neural model that best adapt to the nature of the research are defined. The work must involve a simulation environment, the framework for neural models experimentation and results verification, chosen according to characteristics like reliability, viability and adequacy to hardware conditions and limitations. In addition, the environment must be capable of dealing with the research object, that is, the analysis and classification of three-dimensional forms and their recognition through adjustments to the parameters of the neural model. The research to be carried out was divided into two phases: the first one is network training, in which some images are arbitrarily chosen from an image base. These images share common characteristics that must be recognized to make adjustments to the Neural Network. In the second phase, after the stage of tests and training, the network must be capable of dealing with the rest of the selected image base. The system must also effectively deal with exceptions and variation in some characteristics, such as light, positioning and color. The challenge is making the neural network training be as generic as possible, so it can deal with these variations, offering a degree of reliability without substantial decrease in effectiveness |
