• Title, Summary, Keyword: invariant Laplacian

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Scale Invariant Target Detection using the Laplacian Scale-Space with Adaptive Threshold (라플라스 스케일스페이스 이론과 적응 문턱치를 이용한 크기 불변 표적 탐지 기법)

  • Kim, Sung-Ho;Yang, Yu-Kyung
    • Journal of the Korea Institute of Military Science and Technology
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    • v.11 no.1
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    • pp.66-74
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    • 2008
  • This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection. Scale invariant feature using the Laplacian scale-space can detect different sizes of targets robustly compared to the conventional spatial filtering methods with fixed kernel size. Additionally, scale-reflected adaptive thresholding can reduce many false alarms. Experimental results with real IR images show the robustness of the proposed target detection in real world.

Fingerprint Recognition System for On-line User Authentication (온라인 사용자 인증을 위한 지문인식 시스템)

  • Han, Sang-Hoon;Lee, Ho;Seo, Jeong-Man
    • Journal of the Korea Society of Computer and Information
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    • v.11 no.1
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    • pp.283-292
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    • 2006
  • Interest about a latest security connection technology rises, and try to overcome security vulnerability Certification about on-line user methods through fingerprint that is biometries information apply. In this study, designs and implements fingerprint recognition system that is invariant to rotation by fingerprint recognition system for certification about on-line user. Proposed method focused in matching process through pre-process of fingerprint image, feature point extraction. Improved process time and correct recognition rate in fingerprint recognition system that is invariant to rotation presented in existing study. Also, improved noise, distortion problems that happen in preprocess of existing study applying directional Laplacian filter.

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A Novel Iris recognition method robust to noises and translation (잡음과 위치이동에 강인한 새로운 홍채인식 기법)

  • Won, Jung-Woo;Kim, Jae-Min;Cho, Sung-Won;Choi, Kyung-Sam;Choi, Jin-Su
    • Proceedings of the KIEE Conference
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    • pp.392-395
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    • 2003
  • This paper describes a new iris segmentation and recognition method, which is robust to noises. Combining statistical classification and elastic boundary fitting, the iris is first segmented. Then, the localized iris image is smoothed by a convolution with a Gaussian function, down-sampled by a factor of filtered with a Laplacian operator, and quantized using the Lloyd-Max method. Since the quantized output is sensitive to a small shift of the full-resolution iris image, the outputs of the Laplacian operator are computed for all space shifts. The quantized output with maximum entropy is selected as the final feature representation. An appropriate formulation of similarity measure is defined for the classification of the quantized output. Experimentally we showed that the proposed method produces superb performance in iris segmentation and recognition.

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CHARACTERIZING FUNCTIONS FIXED BY A WEIGHTED BEREZIN TRANSFORM IN THE BIDISC

  • Lee, Jaesung
    • Korean Journal of Mathematics
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    • v.27 no.2
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    • pp.437-444
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    • 2019
  • For c > -1, let ${\nu}_c$ denote a weighted radial measure on ${\mathbb{C}}$ normalized so that ${\nu}_c(D)=1$. For $c_1,c_2>-1$ and $f{\in}L^1(D^2,\;{\nu}_{c_1}{\times}{\nu}_{c_2})$, we define the weighted Berezin transform $B_{c_1,c_2}f$ on $D^2$ by $$(B_{c_1,c_2})f(z,w)={\displaystyle{\smashmargin2{\int\nolimits_D}{\int\nolimits_D}}}f({\varphi}_z(x),\;{\varphi}_w(y))\;d{\nu}_{c_1}(x)d{\upsilon}_{c_2}(y)$$. This paper is about the space $M^p_{c_1,c_2}$ of function $f{\in}L^p(D^2,\;{\nu}_{c_1}{\times}{\nu}_{c_2})$ ) satisfying $B_{c_1,c_2}f=f$ for $1{\leq}p<{\infty}$. We find the identity operator on $M^p_{c_1,c_2}$ by using invariant Laplacians and we characterize some special type of functions in $M^p_{c_1,c_2}$.