Amplitude-only log Radon transform for geometric invariant shape descriptor
Identifieur interne : 000053 ( PascalFrancis/Corpus ); précédent : 000052; suivant : 000054Amplitude-only log Radon transform for geometric invariant shape descriptor
Auteurs : Makoto Hasegawa ; Salvatore TabboneSource :
- Pattern recognition [ 0031-3203 ] ; 2014.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
A shape descriptor combining the Radon transform, the amplitude extraction, and the log-mapping is proposed in this paper. It is invariant to shape rotation, scaling, and translation. Invariance to translation is achieved by amplitude extraction on the radial coordinate. Rotation and scaling are log-mapped into two-dimensional translations and recovered with the phase-only correlation function. In addition, all transformation parameters (rotating angle, scaling factor, and position shift) can be determined also. The efficiency of the proposed descriptor compared to existing methods is shown experimentally on different kind of datasets.
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Pour connaître la documentation sur le format Inist Standard.
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Format Inist (serveur)
NO : | PASCAL 13-0366117 INIST |
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ET : | Amplitude-only log Radon transform for geometric invariant shape descriptor |
AU : | HASEGAWA (Makoto); TABBONE (Salvatore) |
AF : | School of Engineering Tokyo Denki University, 5 Senju Asahi-cho/Adachi-ku, Tokyo 120-8551/Japon (1 aut.); LORIA, UMR 7503, Université de Lorraine/54506 Vandoeuvre-lès-Nancy/France (2 aut.) |
DT : | Publication en série; Niveau analytique |
SO : | Pattern recognition; ISSN 0031-3203; Coden PTNRA8; Royaume-Uni; Da. 2014; Vol. 47; No. 2; Pp. 643-658; Bibl. 21 ref. |
LA : | Anglais |
EA : | A shape descriptor combining the Radon transform, the amplitude extraction, and the log-mapping is proposed in this paper. It is invariant to shape rotation, scaling, and translation. Invariance to translation is achieved by amplitude extraction on the radial coordinate. Rotation and scaling are log-mapped into two-dimensional translations and recovered with the phase-only correlation function. In addition, all transformation parameters (rotating angle, scaling factor, and position shift) can be determined also. The efficiency of the proposed descriptor compared to existing methods is shown experimentally on different kind of datasets. |
CC : | 001D04A05A |
FD : | Transformation Radon; Forme géométrique; Reconnaissance forme; Mappage; Invariance; Modèle 2 dimensions; Fonction phase; Fonction corrélation; Evaluation performance; Transformation Fourier; Transformation Mellin |
ED : | Radon transformation; Geometrical shape; Pattern recognition; Mapping; Invariance; Two dimensional model; Phase function; Correlation function; Performance evaluation; Fourier transformation; Mellin transformation |
SD : | Transformación Radon; Forma geométrica; Reconocimiento patrón; Carta de datos; Invarianza; Modelo 2 dimensiones; Función fase; Función correlación; Evaluación prestación; Transformación Fourier; Transformación Mellin |
LO : | INIST-15220.354000501094390140 |
ID : | 13-0366117 |
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Pascal:13-0366117Le document en format XML
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<ET>Amplitude-only log Radon transform for geometric invariant shape descriptor</ET>
<AU>HASEGAWA (Makoto); TABBONE (Salvatore)</AU>
<AF>School of Engineering Tokyo Denki University, 5 Senju Asahi-cho/Adachi-ku, Tokyo 120-8551/Japon (1 aut.); LORIA, UMR 7503, Université de Lorraine/54506 Vandoeuvre-lès-Nancy/France (2 aut.)</AF>
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<SO>Pattern recognition; ISSN 0031-3203; Coden PTNRA8; Royaume-Uni; Da. 2014; Vol. 47; No. 2; Pp. 643-658; Bibl. 21 ref.</SO>
<LA>Anglais</LA>
<EA>A shape descriptor combining the Radon transform, the amplitude extraction, and the log-mapping is proposed in this paper. It is invariant to shape rotation, scaling, and translation. Invariance to translation is achieved by amplitude extraction on the radial coordinate. Rotation and scaling are log-mapped into two-dimensional translations and recovered with the phase-only correlation function. In addition, all transformation parameters (rotating angle, scaling factor, and position shift) can be determined also. The efficiency of the proposed descriptor compared to existing methods is shown experimentally on different kind of datasets.</EA>
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