8 resultados para Multi Domain Information Model

em SAPIENTIA - Universidade do Algarve - Portugal


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A three-dimensional primitive equation model and its application to a tidal estuary is described. The model solves the primitive equations for incompressible fluids with Boussinesq and hydrostatic approximations. The discretization is based on the finite volume method and allows a general vertical coordinate. The computational code is implemented in such a way that different vertical coordinates can be used in different parts of the domain. The model was designed to be able to simulate the flow both in the open ocean and in coastal and estuarine zones and can be coupled in a simple way to ecological models. The model was implemented successfully in several estuarine and coastal areas. Results are show for the Sado estuary in Portugal to illustrate model accuracy and potential. Quantitative validation is based on field data (water levels and velocities) while qualitative verification is based on the analysis of secondary flows.

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Keypoints (junctions) provide important information for focus-of-attention (FoA) and object categorization/recognition. In this paper we analyze the multi-scale keypoint representation, obtained by applying a linear and quasi-continuous scaling to an optimized model of cortical end-stopped cells, in order to study its importance and possibilities for developing a visual, cortical architecture.We show that keypoints, especially those which are stable over larger scale intervals, can provide a hierarchically structured saliency map for FoA and object recognition. In addition, the application of non-classical receptive field inhibition to keypoint detection allows to distinguish contour keypoints from texture (surface) keypoints.

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Tese de dout., Engenharia Electrónica e de Computadores, Faculdade de Ciência e Tecnologia, Universidade do Algarve, 2007

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The primary visual cortex employs simple, complex and end-stopped cells to create a scale space of 1D singularities (lines and edges) and of 2D singularities (line and edge junctions and crossings called keypoints). In this paper we show first results of a biological model which attributes information of the local image structure to keypoints at all scales, ie junction type (L, T, +) and main line/edge orientations. Keypoint annotation in combination with coarse to fine scale processing facilitates various processes, such as image matching (stereo and optical flow), object segregation and object tracking.

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A biological disparity energy model can estimate local depth information by using a population of V1 complex cells. Instead of applying an analytical model which explicitly involves cell parameters like spatial frequency, orientation, binocular phase and position difference, we developed a model which only involves the cells’ responses, such that disparity can be extracted from a population code, using only a set of previously trained cells with random-dot stereograms of uniform disparity. Despite good results in smooth regions, the model needs complementary processing, notably at depth transitions. We therefore introduce a new model to extract disparity at keypoints such as edge junctions, line endings and points with large curvature. Responses of end-stopped cells serve to detect keypoints, and those of simple cells are used to detect orientations of their underlying line and edge structures. Annotated keypoints are then used in the leftright matching process, with a hierarchical, multi-scale tree structure and a saliency map to segregate disparity. By combining both models we can (re)define depth transitions and regions where the disparity energy model is less accurate.

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This paper discusses the role of enterprise architecture representation, in the context of ERP (Enterprise Resource Planning) information systems, as an instrument for an organization to reflect on itself and develop its business strategies and respective alignment with Information Systems. The paper proposes a representation model of enterprise architecture, as a tool for recommending good practices, and it emerges from a case study undertaken in the context of and investigation on advantages and limitations of ERP systems in the hospitality industry. The proposed approach is also inspired on other academic or market propositions suitable for the objectives of the investigation. It consists on a set of items representing the steps that must be taken by top managers and IS managers.

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We present an improved, biologically inspired and multiscale keypoint operator. Models of single- and double-stopped hypercomplex cells in area V1 of the mammalian visual cortex are used to detect stable points of high complexity at multiple scales. Keypoints represent line and edge crossings, junctions and terminations at fine scales, and blobs at coarse scales. They are detected by applying first and second derivatives to responses of complex cells in combination with two inhibition schemes to suppress responses along lines and edges. A number of optimisations make our new algorithm much faster than previous biologically inspired models, achieving real-time performance on modern GPUs and competitive speeds on CPUs. In this paper we show that the keypoints exhibit state-of-the-art repeatability in standardised benchmarks, often yielding best-in-class performance. This makes them interesting both in biological models and as a useful detector in practice. We also show that keypoints can be used as a data selection step, significantly reducing the complexity in state-of-the-art object categorisation. (C) 2014 Elsevier B.V. All rights reserved.

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Tese de doutoramento, Ciências do Mar ( Processos de Ecossistemas Marinhos), Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2012