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A study of relative phase in complex wavelet domain / An Vo in Signal processing. Image communication, Vol. 25 N° 1 (Janvier 2010) 

Public ISBD [article]  in Signal processing. Image communication > Vol. 25 N° 1 (Janvier 2010) . - pp. 28-46 Titre : A study of relative phase in complex wavelet domain : property, statistics and applications in texture image retrieval and segmentation Type de document : texte imprimé Auteurs : An Vo, Auteur ; Soontorn Oraintara, Auteur Article en page(s) : pp. 28-46 Note générale : Electronique Langues : Anglais (eng) Mots-clés : Relative  phase  Statistics  of  relative  phase  Pdf  of  relative  phase  Statistical  image  modeling  Von  Mises  distribution  Wrapped  Cauchy  distribution  Pyramidal  dual-tree  directional  filter  bank  Dual-tree  complex  wavelet  transform  Uniform  discrete  curvelet  transform  Texture  image  retrieval  Texture  segmentation Index. décimale : 621.382 Dispositifs électroniques utilisant les effets des corps solides. Dispositifs semi-conducteurs Résumé : In this paper, we develop a new approach which exploits the probabilistic properties from the phase information of 2-D complex wavelet coefficients for image modeling. Instead of directly using phases of complex wavelet coefficients, we demonstrate why relative phases should be used. The definition, properties and statistics of relative phases of complex coefficients are studied in detail. We proposed von Mises and wrapped Cauchy for the probability density function (pdf) of relative phases in the complex wavelet domain. The maximum-likelihood method is used to estimate two parameters of von Mises and wrapped Cauchy. We demonstrate that the von Mises and wrapped Cauchy fit well with real data obtained from various real images including texture images as well as standard images. The von Mises and wrapped Cauchy models are compared, and the simulation results show that the wrapped Cauchy fits well with the peaky and heavy-tailed pdf of relative phases and the von Mises fits well with the pdf which is in Gaussian shape. For most of the test images, the wrapped Cauchy model is more accurate than the von Mises model, when images are decomposed by different complex wavelet transforms including dual-tree complex wavelet (DTCWT), pyramidal dual-tree directional filter bank (PDTDFB) and uniform discrete curvelet transform (UDCT). Moreover, the relative phase is applied to obtain new features for texture image retrieval and segmentation applications. Instead of using only real or magnitude coefficients, the new approach uses a feature in which phase information is incorporated, yielding a higher accuracy in texture image retrieval as well as in segmentation. The relative phase information which is complementary to the magnitude is a promising approach in image processing. DEWEY : 361.382 ISSN : 0923-5965 En ligne : http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235640%23 [...] [article] A study of relative phase in complex wavelet domain : property, statistics and applications in texture image retrieval and segmentation [texte imprimé] / An Vo, Auteur ; Soontorn Oraintara, Auteur . - pp. 28-46. Electronique Langues : Anglais (eng) in Signal processing. Image communication > Vol. 25 N° 1 (Janvier 2010) . - pp. 28-46 Mots-clés : Relative  phase  Statistics  of  relative  phase  Pdf  of  relative  phase  Statistical  image  modeling  Von  Mises  distribution  Wrapped  Cauchy  distribution  Pyramidal  dual-tree  directional  filter  bank  Dual-tree  complex  wavelet  transform  Uniform  discrete  curvelet  transform  Texture  image  retrieval  Texture  segmentation Index. décimale : 621.382 Dispositifs électroniques utilisant les effets des corps solides. Dispositifs semi-conducteurs Résumé : In this paper, we develop a new approach which exploits the probabilistic properties from the phase information of 2-D complex wavelet coefficients for image modeling. Instead of directly using phases of complex wavelet coefficients, we demonstrate why relative phases should be used. The definition, properties and statistics of relative phases of complex coefficients are studied in detail. We proposed von Mises and wrapped Cauchy for the probability density function (pdf) of relative phases in the complex wavelet domain. The maximum-likelihood method is used to estimate two parameters of von Mises and wrapped Cauchy. We demonstrate that the von Mises and wrapped Cauchy fit well with real data obtained from various real images including texture images as well as standard images. The von Mises and wrapped Cauchy models are compared, and the simulation results show that the wrapped Cauchy fits well with the peaky and heavy-tailed pdf of relative phases and the von Mises fits well with the pdf which is in Gaussian shape. For most of the test images, the wrapped Cauchy model is more accurate than the von Mises model, when images are decomposed by different complex wavelet transforms including dual-tree complex wavelet (DTCWT), pyramidal dual-tree directional filter bank (PDTDFB) and uniform discrete curvelet transform (UDCT). Moreover, the relative phase is applied to obtain new features for texture image retrieval and segmentation applications. Instead of using only real or magnitude coefficients, the new approach uses a feature in which phase information is incorporated, yielding a higher accuracy in texture image retrieval as well as in segmentation. The relative phase information which is complementary to the magnitude is a promising approach in image processing. DEWEY : 361.382 ISSN : 0923-5965 En ligne : http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235640%23 [...]

A study of relative phase in complex wavelet domain: Property, statistics and applications in texture image retrieval and segmentation / An Vo in Signal processing. Image communication, Vol. 25 N° 1 (Janvier 2010) 

Public ISBD [article]  in Signal processing. Image communication > Vol. 25 N° 1 (Janvier 2010) . - pp. 28–46 Titre : A study of relative phase in complex wavelet domain: Property, statistics and applications in texture image retrieval and segmentation Type de document : texte imprimé Auteurs : An Vo, Auteur ; Soontorn Oraintara, Auteur Année de publication : 2012 Article en page(s) : pp. 28–46 Note générale : Electronique Langues : Anglais (eng) Mots-clés : Relative  phase  Statistics  of  relative  phase  Pdf  of  relative  phase  Statistical  image  modeling  Von  Mises  distribution  Wrapped  Cauchy  distribution  Pyramidal  dual-tree  directional  filter  bank  Dual-tree  complex  wavelet  transform  Uniform  discrete  curvelet  transform  Texture  image  retrieval  Texture  segmentation Résumé : In this paper, we develop a new approach which exploits the probabilistic properties from the phase information of 2-D complex wavelet coefficients for image modeling. Instead of directly using phases of complex wavelet coefficients, we demonstrate why relative phases should be used. The definition, properties and statistics of relative phases of complex coefficients are studied in detail. We proposed von Mises and wrapped Cauchy for the probability density function (pdf) of relative phases in the complex wavelet domain. The maximum-likelihood method is used to estimate two parameters of von Mises and wrapped Cauchy. We demonstrate that the von Mises and wrapped Cauchy fit well with real data obtained from various real images including texture images as well as standard images. The von Mises and wrapped Cauchy models are compared, and the simulation results show that the wrapped Cauchy fits well with the peaky and heavy-tailed pdf of relative phases and the von Mises fits well with the pdf which is in Gaussian shape. For most of the test images, the wrapped Cauchy model is more accurate than the von Mises model, when images are decomposed by different complex wavelet transforms including dual-tree complex wavelet (DTCWT), pyramidal dual-tree directional filter bank (PDTDFB) and uniform discrete curvelet transform (UDCT). Moreover, the relative phase is applied to obtain new features for texture image retrieval and segmentation applications. Instead of using only real or magnitude coefficients, the new approach uses a feature in which phase information is incorporated, yielding a higher accuracy in texture image retrieval as well as in segmentation. The relative phase information which is complementary to the magnitude is a promising approach in image processing. ISSN : 0923-5965 En ligne : http://www.sciencedirect.com/science/article/pii/S0923596509001192# [article] A study of relative phase in complex wavelet domain: Property, statistics and applications in texture image retrieval and segmentation [texte imprimé] / An Vo, Auteur ; Soontorn Oraintara, Auteur . - 2012 . - pp. 28–46. Electronique Langues : Anglais (eng) in Signal processing. Image communication > Vol. 25 N° 1 (Janvier 2010) . - pp. 28–46 Mots-clés : Relative  phase  Statistics  of  relative  phase  Pdf  of  relative  phase  Statistical  image  modeling  Von  Mises  distribution  Wrapped  Cauchy  distribution  Pyramidal  dual-tree  directional  filter  bank  Dual-tree  complex  wavelet  transform  Uniform  discrete  curvelet  transform  Texture  image  retrieval  Texture  segmentation Résumé : In this paper, we develop a new approach which exploits the probabilistic properties from the phase information of 2-D complex wavelet coefficients for image modeling. Instead of directly using phases of complex wavelet coefficients, we demonstrate why relative phases should be used. The definition, properties and statistics of relative phases of complex coefficients are studied in detail. We proposed von Mises and wrapped Cauchy for the probability density function (pdf) of relative phases in the complex wavelet domain. The maximum-likelihood method is used to estimate two parameters of von Mises and wrapped Cauchy. We demonstrate that the von Mises and wrapped Cauchy fit well with real data obtained from various real images including texture images as well as standard images. The von Mises and wrapped Cauchy models are compared, and the simulation results show that the wrapped Cauchy fits well with the peaky and heavy-tailed pdf of relative phases and the von Mises fits well with the pdf which is in Gaussian shape. For most of the test images, the wrapped Cauchy model is more accurate than the von Mises model, when images are decomposed by different complex wavelet transforms including dual-tree complex wavelet (DTCWT), pyramidal dual-tree directional filter bank (PDTDFB) and uniform discrete curvelet transform (UDCT). Moreover, the relative phase is applied to obtain new features for texture image retrieval and segmentation applications. Instead of using only real or magnitude coefficients, the new approach uses a feature in which phase information is incorporated, yielding a higher accuracy in texture image retrieval as well as in segmentation. The relative phase information which is complementary to the magnitude is a promising approach in image processing. ISSN : 0923-5965 En ligne : http://www.sciencedirect.com/science/article/pii/S0923596509001192#