A educação a distância e a formação continuada de professores de matemática: contribuições de um contexto formativo para a base de conhecimento docente

Pós-graduação em Educação - FCT

A Simple Method for Enema Administration in One-Day-Old Broiler Chicks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A simple and green analytical method for determination of glyphosate in commercial formulations and water by diffuse reflectance spectroscopy

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

On a regular convex solver potential for an elastic-damage constitutive model: a theoretical analysis

This work is related with the proposition of a so-called regular or convex solver potential to be used in numerical simulations involving a certain class of constitutive elastic-damage models. All the mathematical aspects involved are based on convex analysis, which is employed aiming a consistent variational formulation of the potential and its conjugate one. It is shown that the constitutive relations for the class of damage models here considered can be derived from the solver potentials by means of sub-differentials sets. The optimality conditions of the resulting minimisation problem represent in particular a linear complementarity problem. Finally, a simple example is present in order to illustrate the possible integration errors that can be generated when finite step analysis is performed. (C) 2003 Elsevier Ltd. All rights reserved.

The pioneering use of ISSR (Inter Simple Sequence Repeat) in Neotropical anurans: Preliminary assessment of genetic diversity in populations of Physalaemus cuvieri (Amphibia, Leiuperidae)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Unsupervised manifold learning using Reciprocal kNN Graphs in image re-ranking and rank aggregation tasks

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut

Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.