1,195 research outputs found

    Graduating the age-specific fertility pattern using Support Vector Machines

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    A topic of interest in demographic literature is the graduation of the age-specific fertility pattern. A standard graduation technique extensively used by demographers is to fit parametric models that accurately reproduce it. Non-parametric statistical methodology might be alternatively used for this graduation purpose. Support Vector Machines (SVM) is a non-parametric methodology that could be utilized for fertility graduation purposes. This paper evaluates the SVM techniques as tools for graduating fertility rates In that we apply these techniques to empirical age specific fertility rates from a variety of populations, time period, and cohorts. Additionally, for comparison reasons we also fit known parametric models to the same empirical data sets.age patterns of fertility, graduation techniques, parametric models of fertility, support vector machines

    Adaptive Methods for Point Cloud and Mesh Processing

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    Point clouds and 3D meshes are widely used in numerous applications ranging from games to virtual reality to autonomous vehicles. This dissertation proposes several approaches for noise removal and calibration of noisy point cloud data and 3D mesh sharpening methods. Order statistic filters have been proven to be very successful in image processing and other domains as well. Different variations of order statistics filters originally proposed for image processing are extended to point cloud filtering in this dissertation. A brand-new adaptive vector median is proposed in this dissertation for removing noise and outliers from noisy point cloud data. The major contributions of this research lie in four aspects: 1) Four order statistic algorithms are extended, and one adaptive filtering method is proposed for the noisy point cloud with improved results such as preserving significant features. These methods are applied to standard models as well as synthetic models, and real scenes, 2) A hardware acceleration of the proposed method using Microsoft parallel pattern library for filtering point clouds is implemented using multicore processors, 3) A new method for aerial LIDAR data filtering is proposed. The objective is to develop a method to enable automatic extraction of ground points from aerial LIDAR data with minimal human intervention, and 4) A novel method for mesh color sharpening using the discrete Laplace-Beltrami operator is proposed. Median and order statistics-based filters are widely used in signal processing and image processing because they can easily remove outlier noise and preserve important features. This dissertation demonstrates a wide range of results with median filter, vector median filter, fuzzy vector median filter, adaptive mean, adaptive median, and adaptive vector median filter on point cloud data. The experiments show that large-scale noise is removed while preserving important features of the point cloud with reasonable computation time. Quantitative criteria (e.g., complexity, Hausdorff distance, and the root mean squared error (RMSE)), as well as qualitative criteria (e.g., the perceived visual quality of the processed point cloud), are employed to assess the performance of the filters in various cases corrupted by different noisy models. The adaptive vector median is further optimized for denoising or ground filtering aerial LIDAR data point cloud. The adaptive vector median is also accelerated on multi-core CPUs using Microsoft Parallel Patterns Library. In addition, this dissertation presents a new method for mesh color sharpening using the discrete Laplace-Beltrami operator, which is an approximation of second order derivatives on irregular 3D meshes. The one-ring neighborhood is utilized to compute the Laplace-Beltrami operator. The color for each vertex is updated by adding the Laplace-Beltrami operator of the vertex color weighted by a factor to its original value. Different discretizations of the Laplace-Beltrami operator have been proposed for geometrical processing of 3D meshes. This work utilizes several discretizations of the Laplace-Beltrami operator for sharpening 3D mesh colors and compares their performance. Experimental results demonstrated the effectiveness of the proposed algorithms

    Proceedings. 26. Workshop Computational Intelligence, Dortmund, 24. - 25. November 2016

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    Dieser Tagungsband enthĂ€lt die BeitrĂ€ge des 26. Workshops Computational Intelligence. Die Schwerpunkte sind Methoden, Anwendungen und Tools fĂŒr Fuzzy-Systeme, KĂŒnstliche Neuronale Netze, EvolutionĂ€re Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen und Benchmark-Problemen

    Geometric data understanding : deriving case specific features

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    There exists a tradition using precise geometric modeling, where uncertainties in data can be considered noise. Another tradition relies on statistical nature of vast quantity of data, where geometric regularity is intrinsic to data and statistical models usually grasp this level only indirectly. This work focuses on point cloud data of natural resources and the silhouette recognition from video input as two real world examples of problems having geometric content which is intangible at the raw data presentation. This content could be discovered and modeled to some degree by such machine learning (ML) approaches like deep learning, but either a direct coverage of geometry in samples or addition of special geometry invariant layer is necessary. Geometric content is central when there is a need for direct observations of spatial variables, or one needs to gain a mapping to a geometrically consistent data representation, where e.g. outliers or noise can be easily discerned. In this thesis we consider transformation of original input data to a geometric feature space in two example problems. The first example is curvature of surfaces, which has met renewed interest since the introduction of ubiquitous point cloud data and the maturation of the discrete differential geometry. Curvature spectra can characterize a spatial sample rather well, and provide useful features for ML purposes. The second example involves projective methods used to video stereo-signal analysis in swimming analytics. The aim is to find meaningful local geometric representations for feature generation, which also facilitate additional analysis based on geometric understanding of the model. The features are associated directly to some geometric quantity, and this makes it easier to express the geometric constraints in a natural way, as shown in the thesis. Also, the visualization and further feature generation is much easier. Third, the approach provides sound baseline methods to more traditional ML approaches, e.g. neural network methods. Fourth, most of the ML methods can utilize the geometric features presented in this work as additional features.Geometriassa kÀytetÀÀn perinteisesti tarkkoja malleja, jolloin datassa esiintyvÀt epÀtarkkuudet edustavat melua. Toisessa perinteessÀ nojataan suuren datamÀÀrÀn tilastolliseen luonteeseen, jolloin geometrinen sÀÀnnönmukaisuus on datan sisÀsyntyinen ominaisuus, joka hahmotetaan tilastollisilla malleilla ainoastaan epÀsuorasti. TÀmÀ työ keskittyy kahteen esimerkkiin: luonnonvaroja kuvaaviin pistepilviin ja videohahmontunnistukseen. NÀmÀ ovat todellisia ongelmia, joissa geometrinen sisÀltö on tavoittamattomissa raakadatan tasolla. TÀmÀ sisÀltö voitaisiin jossain mÀÀrin löytÀÀ ja mallintaa koneoppimisen keinoin, esim. syvÀoppimisen avulla, mutta joko geometria pitÀÀ kattaa suoraan nÀytteistÀmÀllÀ tai tarvitaan neuronien lisÀkerros geometrisia invariansseja varten. Geometrinen sisÀltö on keskeinen, kun tarvitaan suoraa avaruudellisten suureiden havainnointia, tai kun tarvitaan kuvaus geometrisesti yhtenÀiseen dataesitykseen, jossa poikkeavat nÀytteet tai melu voidaan helposti erottaa. TÀssÀ työssÀ tarkastellaan datan muuntamista geometriseen piirreavaruuteen kahden esimerkkiohjelman suhteen. EnsimmÀinen esimerkki on pintakaarevuus, joka on uudelleen virinneen kiinnostuksen kohde kaikkialle saatavissa olevan datan ja diskreetin geometrian kypsymisen takia. Kaarevuusspektrit voivat luonnehtia avaruudellista kohdetta melko hyvin ja tarjota koneoppimisessa hyödyllisiÀ piirteitÀ. Toinen esimerkki koskee projektiivisia menetelmiÀ kÀytettÀessÀ stereovideosignaalia uinnin analytiikkaan. Tavoite on löytÀÀ merkityksellisiÀ paikallisen geometrian esityksiÀ, jotka samalla mahdollistavat muun geometrian ymmÀrrykseen perustuvan analyysin. Piirteet liittyvÀt suoraan johonkin geometriseen suureeseen, ja tÀmÀ helpottaa luonnollisella tavalla geometristen rajoitteiden kÀsittelyÀ, kuten vÀitöstyössÀ osoitetaan. Myös visualisointi ja lisÀpiirteiden luonti muuttuu helpommaksi. Kolmanneksi, lÀhestymistapa suo selkeÀn vertailumenetelmÀn perinteisemmille koneoppimisen lÀhestymistavoille, esim. hermoverkkomenetelmille. NeljÀnneksi, useimmat koneoppimismenetelmÀt voivat hyödyntÀÀ tÀssÀ työssÀ esitettyjÀ geometrisia piirteitÀ lisÀÀmÀllÀ ne muiden piirteiden joukkoon

    A Physics-Constrained Data-Driven Approach Based on Locally Convex Reconstruction for Noisy Database

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    Physics-constrained data-driven computing is an emerging hybrid approach that integrates universal physical laws with data-driven models of experimental data for scientific computing. A new data-driven simulation approach coupled with a locally convex reconstruction, termed the local convexity data-driven (LCDD) computing, is proposed to enhance accuracy and robustness against noise and outliers in data sets in the data-driven computing. In this approach, for a given state obtained by the physical simulation, the corresponding optimum experimental solution is sought by projecting the state onto the associated local convex manifold reconstructed based on the nearest experimental data. This learning process of local data structure is less sensitive to noisy data and consequently yields better accuracy. A penalty relaxation is also introduced to recast the local learning solver in the context of non-negative least squares that can be solved effectively. The reproducing kernel approximation with stabilized nodal integration is employed for the solution of the physical manifold to allow reduced stress-strain data at the discrete points for enhanced effectiveness in the LCDD learning solver. Due to the inherent manifold learning properties, LCDD performs well for high-dimensional data sets that are relatively sparse in real-world engineering applications. Numerical tests demonstrated that LCDD enhances nearly one order of accuracy compared to the standard distance-minimization data-driven scheme when dealing with noisy database, and a linear exactness is achieved when local stress-strain relation is linear

    Interferometric Synthetic Aperture RADAR and Radargrammetry towards the Categorization of Building Changes

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    The purpose of this work is the investigation of SAR techniques relying on multi image acquisition for fully automatic and rapid change detection analysis at building level. In particular, the benefits and limitations of a complementary use of two specific SAR techniques, InSAR and radargrammetry, in an emergency context are examined in term of quickness, globality and accuracy. The analysis is performed using spaceborne SAR data
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