Statistical analysis applied to data classification and image filtering

• Ano de defesa: 2016
• Autor(a) principal: ALMEIDA, Marcos Antonio Martins de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Pernambuco; UFPE; Brasil; Programa de Pos Graduacao em Engenharia Eletrica
• Palavras-chave em Português: Electrical Eingineering; Data processing; Data classification; Image filtering
• Link de acesso: https://repositorio.ufpe.br/handle/123456789/25506

Statistical analysis is a tool of wide applicability in several areas of scientific knowledge. This thesis makes use of statistical analysis in two different applications: data classification and image processing targeted at document image binarization. In the first case, this thesis presents an analysis of several aspects of the consistency of the classification of the senior researchers in computer science of the Brazilian research council, CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico. The second application of statistical analysis developed in this thesis addresses filtering-out the back to front interference which appears whenever a document is written or typed on both sides of translucent paper. In this topic, an assessment of the most important algorithms found in the literature is made, taking into account a large quantity of parameters such as the strength of the back to front interference, the diffusion of the ink in the paper, and the texture and hue of the paper due to aging. A new binarization algorithm is proposed, which is capable of removing the back-to-front noise in a wide range of documents. Additionally, this thesis proposes a new concept of “intelligent” binarization for complex documents, which besides text encompass several graphical elements such as figures, photos, diagrams, etc.