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    Wyświetlanie 1-2 z 2
    Tytuł:
    Determination of the Optimal Threshold Value and Number of Keypoints in Scale Invariant Feature Transform-based Copy-Move Forgery Detection
    Autorzy:
    Isnanto, R. Rizal
    Zahra, Ajub Ajulian
    Santoso, Imam
    Lubis, Muhammad Salman
    Powiązania:
    https://bibliotekanauki.pl/articles/227299.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    forgery
    Gaussian noise
    feature extraction
    pattern matching
    Euclidean distance
    Opis:
    The copy-move forgery detection (CMFD) begins with the preprocessing until the image is ready to process. Then, the image features are extracted using a feature-transform-based extraction called the scale-invariant feature transform (SIFT). The last step is features matching using Generalized 2 Nearest-Neighbor (G2NN) method with threshold values variation. The problem is what is the optimal threshold value and number of keypoints so that copy-move detection has the highest accuracy. The optimal threshold value and number of keypoints had determined so that the detection n has the highest accuracy. The research was carried out on images without noise and with Gaussian noise.
    Źródło:
    International Journal of Electronics and Telecommunications; 2020, 66, 3; 561-569
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Critical exponent analysis applied to surface EMG signals for multifunction myoelectric control
    Autorzy:
    Phinyomark, A.
    Phothisonothai, M.
    Phukpattaranont, P.
    Limsakul, C.
    Powiązania:
    https://bibliotekanauki.pl/articles/220544.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    biomedical signal processing
    electromyography signal
    feature extraction
    fractal analysis
    human machine interface
    pattern classification
    Opis:
    Based on recent advances in non-linear analysis, the surface electromyography (sEMG) signal has been studied from the viewpoints of self-affinity and complexity. In this study, we examine usage of critical exponent analysis (CE) method, a fractal dimension (FD) estimator, to study properties of the sEMG signal and to deploy these properties to characterize different movements for gesture recognition. SEMG signals were recorded from thirty subjects with seven hand movements and eight muscle channels. Mean values and coefficient of variations of the CE from all experiments show that there are larger variations between hand movement types but there is small variation within the same type. It also shows that the CE feature related to the self-affine property for the sEMG signal extracted from different activities is in the range of 1.855∼2.754. These results have also been evaluated by analysis-of-variance (p-value). Results show that the CE feature is more suitable to use as a learning parameter for a classifier compared with other representative features including root mean square, median frequency and Higuchi's method. Most p-values of the CE feature were less than 0.0001. Thus the FD that is computed by the CE method can be applied to be used as a feature for a wide variety of sEMG applications.
    Źródło:
    Metrology and Measurement Systems; 2011, 18, 4; 645-658
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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