176 research outputs found

    Three-dimensional reconstruction of irregular foodstuffs

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    Three-dimensional reconstruction of general solid food materials was performed using a reverse engineering method based on a surface cross-sectional design. Digital images of cross-sections of irregular multi-dimensional foodstuffs were acquired using a computer vision system, and image processing was performed to obtain the actual boundaries. These boundaries were then approximated by closed B-spline curves, which were assembled through a lofting technique to construct a geometrical representation of food materials. Considering the reconstructed objects, a procedure based on finite element method was developed to estimate the surface area and volume. The developed finite element method approach was validated against experimental volume values of apples and meat pieces, obtaining an estimation error less than 2%. Surface area prediction equations were proposed from estimated surface area values and weight and volumeFil: Goñi, Sandro Mauricio. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Purlis, Emmanuel. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos; ArgentinaFil: Salvadori, Viviana Olga. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería; Argentin

    NURBS-Based Geometry for Integrated Structural Analysis

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    This grant was initiated in April 1993 and completed in September 1996. The primary goal of the project was to exploit the emerging defacto CAD standard of Non- Uniform Rational B-spline (NURBS) based curve and surface geometry to integrate and streamline the process of turbomachinery structural analysis. We focused our efforts on critical geometric modeling challenges typically posed by the requirements of structural analysts. We developed a suite of software tools that facilitate pre- and post-processing of NURBS-based turbomachinery blade models for finite element structural analyses. We also developed tools to facilitate the modeling of blades in their manufactured (or cold) state based on nominal operating shape and conditions. All of the software developed in the course of this research is written in the C++ language using the Iris Inventor 3D graphical interface tool-kit from Silicon Graphics. In addition to enhanced modularity, improved maintainability, and efficient prototype development, this design facilitates the re-use of code developed for other NASA projects and provides a uniform and professional 'look and feel' for all applications developed by the Iowa State Team

    Evalutionary algorithms for ship hull skinning approximation

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    Traditionally, the design process of a hull involves simulation using clay models. This must be done cautiously, accurately and efficiently in order to sustain the performance of ship. Presently, the current technology of Computer Aided Design, Manufacturing, Engineering and Computational Fluid Dynamic has enabled a 3D design and simulation of a hull be done at a lower cost and within a shorter period of time. Besides that, automated design tools allow the transformation of offset data in designing the hull be done automatically. One of the most common methods in constructing a hull from the offset data is the skinning method. Generally, the skinning method comprised of skinning interpolation and skinning approximation. Skinning interpolation constructs the surface perfectly but improper selection of parameterization methods may cause bumps, wiggles, or uneven surfaces on the generated surface. On the other hand, using the skinning surface approximation would mean that the surface can only be constructed closer to data points. Thus, the error between the generated surface and the data points must be minimized to increase the accuracy. Therefore, this study aims to solve the error minimization problem in order to produce a smoother and fairer surface by proposing Non Uniform Rational B-Spline surface using various evolutionary optimization algorithms, namely, Gravitational Search Algorithm, Particle Swarm Optimization and Genetic Algorithm. The proposed methods involve four procedures: extraction of offset data from line drawing plan; generation of control points; optimization of a surface; and validations of hull surfaces. Validation is done by analyzing the surface curvature and errors between the generated surface and the given data points. The experiments were implemented on both ship hull and free form models. The findings from the experiments are compared with interpolated skinning surface and conventional skinning surface approximation. The results show that the optimized skinning surfaces using the proposed methods yield a smaller error, less control points generation and feasible surfaces while maintaining the shape of the hull

    Fitting Terrestrial Laser Scanner Point Clouds with T-Splines: Local Refinement Strategy for Rigid Body Motion

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    T-splines have recently been introduced to represent objects of arbitrary shapes using a smaller number of control points than the conventional non-uniform rational B-splines (NURBS) or B-spline representatizons in computer-aided design, computer graphics and reverse engineering. They are flexible in representing complex surface shapes and economic in terms of parameters as they enable local refinement. This property is a great advantage when dense, scattered and noisy point clouds are approximated using least squares fitting, such as those from a terrestrial laser scanner (TLS). Unfortunately, when it comes to assessing the goodness of fit of the surface approximation with a real dataset, only a noisy point cloud can be approximated: (i) a low root mean squared error (RMSE) can be linked with an overfitting, i.e., a fitting of the noise, and should be correspondingly avoided, and (ii) a high RMSE is synonymous with a lack of details. To address the challenge of judging the approximation, the reference surface should be entirely known: this can be solved by printing a mathematically defined T-splines reference surface in three dimensions (3D) and modeling the artefacts induced by the 3D printing. Once scanned under different configurations, it is possible to assess the goodness of fit of the approximation for a noisy and potentially gappy point cloud and compare it with the traditional but less flexible NURBS. The advantages of T-splines local refinement open the door for further applications within a geodetic context such as rigorous statistical testing of deformation. Two different scans from a slightly deformed object were approximated; we found that more than 40% of the computational time could be saved without affecting the goodness of fit of the surface approximation by using the same mesh for the two epochs

    3-D surface modelling of the human body and 3-D surface anthropometry

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    This thesis investigates three-dimensional (3-D) surface modelling of the human body and 3-D surface anthropometry. These are two separate, but closely related, areas. 3-D surface modelling is an essential technology for representing and describing the surface shape of an object on a computer. 3-D surface modelling of the human body has wide applications in engineering design, work space simulation, the clothing industry, medicine, biomechanics and animation. These applications require increasingly realistic surface models of the human body. 3-D surface anthropometry is a new interdisciplinary subject. It is defined in this thesis as the art, science, and technology of acquiring, modelling and interrogating 3-D surface data of the human body. [Continues.

    Free-form surface reconstruction based on NURBS to serial cross-sections

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    Stereo facial image matching to aid in Fetal Alcohol Syndrome screening

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    Includes abstract. Includes bibliographical references

    Simultaneous multicurve approximation with NURBS

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    info:eu-repo/semantics/publishe

    Multiple 2D self organising map network for surface reconstruction of 3D unstructured data

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    Surface reconstruction is a challenging task in reverse engineering because it must represent the surface which is similar to the original object based on the data obtained. The data obtained are mostly in unstructured type whereby there is not enough information and incorrect surface will be obtained. Therefore, the data should be reorganised by finding the correct topology with minimum surface error. Previous studies showed that Self Organising Map (SOM) model, the conventional surface approximation approach with Non Uniform Rational B-Splines (NURBS) surfaces, and optimisation methods such as Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimisation (PSO) methods are widely implemented in solving the surface reconstruction. However, the model, approach and optimisation methods are still suffer from the unstructured data and accuracy problems. Therefore, the aims of this research are to propose Cube SOM (CSOM) model with multiple 2D SOM network in organising the unstructured surface data, and to propose optimised surface approximation approach in generating the NURBS surfaces. GA, DE and PSO methods are implemented to minimise the surface error by adjusting the NURBS control points. In order to test and validate the proposed model and approach, four primitive objects data and one medical image data are used. As to evaluate the performance of the proposed model and approach, three performance measurements have been used: Average Quantisation Error (AQE) and Number Of Vertices (NOV) for the CSOM model while surface error for the proposed optimised surface approximation approach. The accuracy of AQE for CSOM model has been improved to 64% and 66% when compared to 2D and 3D SOM respectively. The NOV for CSOM model has been reduced from 8000 to 2168 as compared to 3D SOM. The accuracy of surface error for the optimised surface approximation approach has been improved to 7% compared to the conventional approach. The proposed CSOM model and optimised surface approximation approach have successfully reconstructed surface of all five data with better performance based on three performance measurements used in the evaluation
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