http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Iris Recognition Using Ridgelets
Birgale, Lenina,Kokare, Manesh Korea Information Processing Society 2012 Journal of information processing systems Vol.8 No.3
Image feature extraction is one of the basic works for biometric analysis. This paper presents the novel concept of application of ridgelets for iris recognition systems. Ridgelet transforms are the combination of Radon transforms and Wavelet transforms. They are suitable for extracting the abundantly present textural data that is in an iris. The technique proposed here uses the ridgelets to form an iris signature and to represent the iris. This paper contributes towards creating an improved iris recognition system. There is a reduction in the feature vector size, which is 1X4 in size. The False Acceptance Rate (FAR) and False Rejection Rate (FRR) were also reduced and the accuracy increased. The proposed method also avoids the iris normalization process that is traditionally used in iris recognition systems. Experimental results indicate that the proposed method achieves an accuracy of 99.82%, 0.1309% FAR, and 0.0434% FRR.
Ridgelet transform on square integrable Boehmians
Rajakumar Roopkumar 대한수학회 2009 대한수학회보 Vol.46 No.5
The ridgelet transform is extended to the space of square integrable Boehmians. It is proved that the extended ridgelet transform R is consistent with the classical ridgelet transform R, linear, one-to-one, onto and both R, R^(-1) are continuous with respect to δ-convergence as well as Δ-convergence.
RIDGELET TRANSFORM ON SQUARE INTEGRABLE BOEHMIANS
Roopkumar, Rajakumar Korean Mathematical Society 2009 대한수학회보 Vol.46 No.5
The ridgelet transform is extended to the space of square integrable Boehmians. It is proved that the extended ridgelet transform $\mathfrak{R}$ is consistent with the classical ridgelet transform R, linear, one-to-one, onto and both $\mathfrak{R}$, $\mathfrak{R}^{-1}$.1 are continuous with respect to $\delta$-convergence as well as $\Delta$-convergence.