Reconhecimento de formas utilizando modelos de compressão de dados e espaços de escalas de curvatura
As the processing power of computers increases, the quantity and complexity of stored data have growing in the same way, requiring more sophisticated mechanisms to accomplish retrieval with efficacy and efficiency over these information. In image processing, it has become common the retrieval based...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2009 |
| País: | Brasil |
| Institución: | Universidade Federal da Paraíba (UFPB) |
| Repositorio: | Biblioteca Digital de Teses e Dissertações da UFPB |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufpb.br:tede/6137 |
| Acceso en línea: | https://repositorio.ufpb.br/jspui/handle/tede/6137 |
| Access Level: | acceso abierto |
| Palabra clave: | Processamento digital de imagens Recuperação baseada em conteúdo Compressão de dados CSS FullCSS PPM Digital image processing Content-based image retrieval Data compression CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
| Sumario: | As the processing power of computers increases, the quantity and complexity of stored data have growing in the same way, requiring more sophisticated mechanisms to accomplish retrieval with efficacy and efficiency over these information. In image processing, it has become common the retrieval based on its own content, namely Content-Based Image Retrieval (CBIR), which eliminates the need to place additional annotations as textual descriptions and keywords registered by an observer. The purpose of this work is the development of an image retrieval mechanism based on shape recognition. The mechanism consists in (1) compute the Full Curvature Scale Space (FullCSS) image descriptors; and (2) apply over them a lossless compression method objecting to (3) classify these descriptors and retrieve the corresponding images. The FullCSS descriptors register the curvature variations on the image contour indicating the degree and the signal of these variations, which allow identifying where the curvature is concave or convex. The adopted compression method uses the Prediction by Partial Matching (PPM) compression model, which has been successfully used in other works to classify texture images. The results obtained show that this novel approach is able to reach competitive levels of efficacy and efficiency when compared to other works recently developed in this same area. |
|---|