Environment for the analysis and comparison of texture descriptors

• Ano de defesa: 2018
• Autor(a) principal: Farfan, Alex Josue Florez
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Ambiente web; Análise de textura; Classificação de textura; Computer vision; Descritor de textura; Texture analysis; Texture classification; Texture descriptor; Visão por computador; Web Environment
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-02012019-103709/

Texture analysis is an active area of research and plays an important role in computer vision applications. Texture, along with color and shape, contains important features of an image. Texture analysis allows to characterize regions inside an image by using descriptors. These descriptors are applied in the study of texture classification, in which the goal is to identify features that characterize a particular texture and assign a label to an image based on these features. Because of the importance of texture analysis in computer vision, researchers are continually devising and developing new descriptors, with the aim to improve the discriminative power of texture features of an image. A difficult task in texture analysis is to compare these descriptors and verify which are the most suitable for each type of image. The lack of a good review and comparison of descriptors cause that some applications do not use the most appropriate descriptor for a specific type of texture. Therefore, in this dissertation it was developed a research and collaboration platform for the analysis and comparison of texture descriptors and texture datasets. The platform aims to support the researchers in the area of texture analysis, specifically in texture classification. The platform was useful to perform an extensive comparison of texture descriptors and various texture datasets. Using the platform, in some datasets the results produced were better than those previously found in the literature. The results indicate that the classification accuracy varies according to the descriptor and classifier employed. By varying the parameters of texture descriptors it was possible to get different, yet better, classification accuracies.