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    Wyświetlanie 1-3 z 3
    Tytuł:
    Curve skeleton extraction via k-nearest-neighbors based contraction
    Autorzy:
    Zhou, Jianling
    Liu, Ji
    Zhang, Min
    Powiązania:
    https://bibliotekanauki.pl/articles/331332.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    curve skeleton
    points contraction
    point cloud
    k nearest neighbors
    szkieletyzacja
    chmura punktów
    metoda k najbliższych sąsiadów
    Opis:
    We propose a skeletonization algorithm that is based on an iterative points contraction. We make an observation that the local center that is obtained via optimizing the sum of the distance to k nearest neighbors possesses good properties of robustness to noise and incomplete data. Based on such an observation, we devise a skeletonization algorithm that mainly consists of two stages: points contraction and skeleton nodes connection. Extensive experiments show that our method can work on raw scans of real-world objects and exhibits better robustness than the previous results in terms of extracting topology-preserving curve skeletons.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2020, 30, 1; 123-132
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of prototype selection algorithms used in construction of neural networks learned by SVD
    Autorzy:
    Jankowski, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/330020.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    radial basis function network
    extreme learning machine
    kernel method
    prototype selection
    machine learning
    k nearest neighbours
    radialna funkcja bazowa
    metoda jądrowa
    uczenie maszynowe
    metoda k najbliższych sąsiadów
    Opis:
    Radial basis function networks (RBFNs) or extreme learning machines (ELMs) can be seen as linear combinations of kernel functions (hidden neurons). Kernels can be constructed in random processes like in ELMs, or the positions of kernels can be initialized by a random subset of training vectors, or kernels can be constructed in a (sub-)learning process (sometimes by k-means, for example). We found that kernels constructed using prototype selection algorithms provide very accurate and stable solutions. What is more, prototype selection algorithms automatically choose not only the placement of prototypes, but also their number. Thanks to this advantage, it is no longer necessary to estimate the number of kernels with time-consuming multiple train-test procedures. The best results of learning can be obtained by pseudo-inverse learning with a singular value decomposition (SVD) algorithm. The article presents a comparison of several prototype selection algorithms co-working with singular value decomposition-based learning. The presented comparison clearly shows that the combination of prototype selection and SVD learning of a neural network is significantly better than a random selection of kernels for the RBFN or the ELM, the support vector machine or the kNN. Moreover, the presented learning scheme requires no parameters except for the width of the Gaussian kernel.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2018, 28, 4; 719-733
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Machine learning-based analysis of English lateral allophones
    Autorzy:
    Piotrowska, Magdalena
    Korvel, Gražina
    Kostek, Bożena
    Ciszewski, Tomasz
    Czyżewski, Andrzej
    Powiązania:
    https://bibliotekanauki.pl/articles/908115.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    allophone
    audio features
    artificial neural network
    k-nearest neighbor
    self organizing map
    alofon
    cechy akustyczne
    sztuczna sieć neuronowa
    metoda najbliższych sąsiadów
    mapa samoorganizująca
    Opis:
    Automatic classification methods, such as artificial neural networks (ANNs), the k-nearest neighbor (kNN) and self-organizing maps (SOMs), are applied to allophone analysis based on recorded speech. A list of 650 words was created for that purpose, containing positionally and/or contextually conditioned allophones. For each word, a group of 16 native and non-native speakers were audio-video recorded, from which seven native speakers’ and phonology experts’ speech was selected for analyses. For the purpose of the present study, a sub-list of 103 words containing the English alveolar lateral phoneme /l/ was compiled. The list includes ‘dark’ (velarized) allophonic realizations (which occur before a consonant or at the end of the word before silence) and 52 ‘clear’ allophonic realizations (which occur before a vowel), as well as voicing variants. The recorded signals were segmented into allophones and parametrized using a set of descriptors, originating from the MPEG 7 standard, plus dedicated time-based parameters as well as modified MFCC features proposed by the authors. Classification methods such as ANNs, the kNN and the SOM were employed to automatically detect the two types of allophones. Various sets of features were tested to achieve the best performance of the automatic methods. In the final experiment, a selected set of features was used for automatic evaluation of the pronunciation of dark /l/ by non-native speakers.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 2; 393-405
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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