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    Wyświetlanie 1-3 z 3
    Tytuł:
    Digital watermarking scheme based on curvelet transform and multiple chaotic maps
    Autorzy:
    Xiao, Yi
    Xu, Ya-Chen
    Zhou, Nan-Run
    Lin, Zhen-Rong
    Powiązania:
    https://bibliotekanauki.pl/articles/27310097.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    digital watermarking
    curvelet transform
    multiple chaotic map
    Opis:
    The rapid development of digital products brings security issues. Digital watermarking technology is an important means to handle these problems. To enhance the imperceptibility of watermark and locate the possible tampering as well, a digital watermarking scheme based on curvelet transform is presented by combining with multiple chaotic maps. The host image is decomposed into three parts, i.e., coarse layer, detail layer and fine layer, with curvelet transform, and a robust watermark is embedded into the coarse layer for copyright protection of digital products. In addition, an authentication watermark is embedded into the fine layer to detect and locate the illegal changes. Simulation results show that the proposed digital watermarking scheme possesses acceptable robustness and security.
    Źródło:
    Optica Applicata; 2023, 53, 2; 291--305
    0078-5466
    1899-7015
    Pojawia się w:
    Optica Applicata
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Catenary image enhancement method based on curvelet transform with adaptive enhancement function
    Autorzy:
    Wu, Changdong
    Powiązania:
    https://bibliotekanauki.pl/articles/327578.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    catenary image
    enhancement
    curvelet transform
    coefficient structure
    adaptive function
    sieć trakcyjna
    awaria
    diagnostyka
    transformata curvelet
    Opis:
    In the process of catenary failure diagnosis system based on image processing technique, some catenary images present low contrast, which need to be enhanced. Curvelet transform has the high directional sensitivity and anisotropy, which is suitable for image enhancement because of its optimal sparse representation of image with rich details and edges. First, the catenary image is decomposed by Curvelet transform to get its high and low frequency coefficients, then adjust the high frequency coefficients using the enhancement function. Afterwards, combine the high frequency coefficients and low frequency coefficients by the inverse Curvelet transform, and thus to get the enhanced catenary image. In this paper, Curvelet transform is compared with the traditional enhancement methods. The experimental results show that the proposed method can effectively enhance the low contrast catenary images, the catenary insulator, arm, hanger, pillar and locator part become visible, the details become more obvious. Moreover, as for the online application of catenary failure diagnosis system, efficiency is another important consideration. The experimental results also show that the cost time of catenary image enhancement is within a few tens of seconds, which meets the requirements of catenary failure diagnosis system.
    Źródło:
    Diagnostyka; 2019, 20, 2; 3-10
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid statistical approach for texture images classification based on scale invariant features and mixture gamma distribution
    Autorzy:
    Benlakhdar, Said
    Rziza, Mohammed
    Thami, Rachid Oulad Haj
    Powiązania:
    https://bibliotekanauki.pl/articles/29520269.pdf
    Data publikacji:
    2020
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    statistical image modeling
    SIFT
    mixture gamma distribution
    uniform discrete curvelet transform
    classification
    Opis:
    Image classification refers to an important process in computer vision. The purpose of this paper is to propose a novel approach named GGD-GMM and based on statistical modeling in wavelet domain to describe textured images and rely on number of principles which give its internal coherence and originality. Firstly, we propose a robust algorithm based on the combination of the wavelet transform and Scale Invariant Feature Transform. Secondly, we implement the aforementioned algorithm and fit the result using the finite mixture gamma distribution (GMM). The results, obtained for two benchmark datasets, show that the proposed algorithm has a good relevance as it provides higher classification accuracy compared to some other well known models see (Kohavi, 1995). Moreover, it shows other advantages relied to Noise-resistant and rotation invariant.
    Źródło:
    Computer Methods in Materials Science; 2020, 20, 3; 95-106
    2720-4081
    2720-3948
    Pojawia się w:
    Computer Methods in Materials Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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