Separação em duas ou mais classes utilizando o classificador polinomial

• Ano de defesa: 2013
• Autor(a) principal: Martins, Alessandro Santana
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; BR; Programa de Pós-graduação em Engenharia Elétrica; Engenharias; UFU
• Palavras-chave em Português: Classicador polinomial; Reconhecimento de padrões; Processamento digital de imagens; Vetor de características; Imagens médicas; Processamento de imagens; Polynomial classier; Pattern recognition; Digital image processing; Feature vector; Medical images; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/14319; https://doi.org/10.14393/ufu.te.2013.23

The improvement of computer systems has benefited the development of many research areas in medicine. Analysis and interpretation of medical images represent an important part in computer vision and pattern recognition. It\'s essential for many researchers and medical centers to develop a diagnosis system aided by computer for diseases such as breast cancer, in order to assist doctors in hospitals. The polynomial classifier developed in this work is a supervised classification method and can sort two or more classes. This classifier expands the feature vector projected into the space Rd into a higher dimensional space where the classification is possible. The polynomial classifier proves to be an important method of classification mainly on treatment with non-linearly separable classes. In this thesis, the classifier was used to recognize patterns from the database IRIS Fisher 1936, which is composed by the flowers setosa, versicolor and virginica. Several tests were performed on this basis and by using three or four features the polynomial classifier managed to sort all these flowers. An application was also performed with the polynomial classifier for the identification of partial pixels in regions of interest in mammographic images. Compared with SVM classifiers and decision tree, the results were better using the polynomial classifier.