Marcos Aurélio Batista

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The work presented in this article aims at shape feature extraction and description. In this paper, we propose a shape-based image retrieval technique using salience points to describe shapes. The saliences of a shape are defined as the higher curvature points along the shape contour. The technique presented here consists of: a salience point detector; a(More)
Inpainting and denoising are two important tasks in the field of image processing with broad applications in image and vision analysis. In this paper, we present a new approach for image restoration. Our method simultaneously fills in missing, corrupted, or undesirable information while it removes noise. The denoising is performed by the smoothing equation(More)
In this paper, we propose a shape-based image retrieval technique using salience points to describe shapes. This technique consists of a salience point detector robust to noise, a salience representation using angular relative position and curvature value, invariant to rotation, translation and scaling, and an elastic matching algorithm to analyze the(More)
In this work an image retrieval system adaptable to user’s interests by the use of relevance feedback via genetic algorithm is presented. The retrieval process is based on local similarity patterns. The goal of the genetic algorithm is to infer weights for regions and features that better translate the user’s requirements producing better quality rankings.(More)
Inpainting digital models have been since the late 1990s a powerful image reconstruction tool for missing data. After the original work of Bertalmio et al. (2000) several different approaches have been used to tackle the problem. Some are based on partial differential equations to model a transport process and a diffusion process, others are based on the(More)