The Experts below are selected from a list of 6384 Experts worldwide ranked by ideXlab platform

Christine Fernandez-maloigne - One of the best experts on this subject based on the ideXlab platform.

  • Choice of a pertinent color space for color texture characterization using parametric spectral analysis
    Pattern Recognition, 2011
    Co-Authors: Imtnan-ul-haque Qazi, Olivier Alata, Jean-christophe Burie, Ahmed Moussa, Christine Fernandez-maloigne
    Abstract:

    This article presents a comparison of different color spaces including RGB, IHLS and L*a*b* for color texture characterization. This comparison is based on the fusion of the independent spatial structure and color feature cues. In IHLS and L*a*b*, two channel complex color images are created from the luminance and the Chrominance values. For such images, two dimensional complex multichannel linear prediction models are used to perform parametric power spectrum estimation and the structure feature cues are computed from this estimated power spectrum. Quantitative comparison of auto spectra of luminance and combined Chrominance channels for different color spaces is done. This comparison is based on the degree of decorrelation between luminance and Chrominance information provided by different color space transformations. Three dimensional histograms are used as color feature cues. Then, to classify color textures, Kullback-Leibler divergence based symmetric distance measures are calculated for pure color, luminance structure and Chrominance structure feature cues. Individual as well as combined effect of information from all feature cues on classification results is then compared for different color spaces and different color texture data sets. The proposed color texture classification method performs better than the state of the art methods in certain cases. The L*a*b* color space gives us a better characterization of the Chrominance spatial structure as well as the overall spatial structure for all of the chosen data sets. Experimental results on pixel classification of color textures are also presented and discussed.

  • Colour Spectral Analysis for Spatial Structure Characterization of Textures in IHLS Colour Space
    Pattern Recognition, 2010
    Co-Authors: Imtnan-ul-haque Qazi, Olivier Alata, Jean-christophe Burie, Christine Fernandez-maloigne
    Abstract:

    In this article, linear prediction model based approach for colour texture characterization and classification in the Improved Hue Luminance and Saturation colour space is presented. Pure Chrominance structure information is used in addition with normally used luminance structure information for colour texture classification. Hue and saturation channels of a colour image in IHLS colour space are combined through a complex exponential to give a single channel which holds all the Chrominance information of the image. Two dimensional complex multichannel versions of Non-Symmetric Half Plane Autoregressive model, Quarter Plane Autoregressive model and Gauss Markov Random Field model are used to perform parametric power spectrum estimation of both luminance and the \textquotedblleft combined Chrominance\textquotedblright\ channels of the image. Accuracy and precision of these spectral estimates are proven quantitatively by performing tests on a large number of images. Spectral distance measures are calculated for the spectral information of luminance and Chrominance channels individually as well as combined through a combination coefficient. Using these distance measures, colour texture classification is done with $k$-nearest neighbour algorithm. A comparison of experimental results in IHLS with the ones in RGB indicate the significance of using IHLS for such analysis. They also show that colour texture characterization and percentage classification obtained by combined luminance and Chrominance structure information is better than the colour texture classification done using only the luminance structure information.

Imtnan-ul-haque Qazi - One of the best experts on this subject based on the ideXlab platform.

  • Choice of a pertinent color space for color texture characterization using parametric spectral analysis
    Pattern Recognition, 2011
    Co-Authors: Imtnan-ul-haque Qazi, Olivier Alata, Jean-christophe Burie, Ahmed Moussa, Christine Fernandez-maloigne
    Abstract:

    This article presents a comparison of different color spaces including RGB, IHLS and L*a*b* for color texture characterization. This comparison is based on the fusion of the independent spatial structure and color feature cues. In IHLS and L*a*b*, two channel complex color images are created from the luminance and the Chrominance values. For such images, two dimensional complex multichannel linear prediction models are used to perform parametric power spectrum estimation and the structure feature cues are computed from this estimated power spectrum. Quantitative comparison of auto spectra of luminance and combined Chrominance channels for different color spaces is done. This comparison is based on the degree of decorrelation between luminance and Chrominance information provided by different color space transformations. Three dimensional histograms are used as color feature cues. Then, to classify color textures, Kullback-Leibler divergence based symmetric distance measures are calculated for pure color, luminance structure and Chrominance structure feature cues. Individual as well as combined effect of information from all feature cues on classification results is then compared for different color spaces and different color texture data sets. The proposed color texture classification method performs better than the state of the art methods in certain cases. The L*a*b* color space gives us a better characterization of the Chrominance spatial structure as well as the overall spatial structure for all of the chosen data sets. Experimental results on pixel classification of color textures are also presented and discussed.

  • Colour Spectral Analysis for Spatial Structure Characterization of Textures in IHLS Colour Space
    Pattern Recognition, 2010
    Co-Authors: Imtnan-ul-haque Qazi, Olivier Alata, Jean-christophe Burie, Christine Fernandez-maloigne
    Abstract:

    In this article, linear prediction model based approach for colour texture characterization and classification in the Improved Hue Luminance and Saturation colour space is presented. Pure Chrominance structure information is used in addition with normally used luminance structure information for colour texture classification. Hue and saturation channels of a colour image in IHLS colour space are combined through a complex exponential to give a single channel which holds all the Chrominance information of the image. Two dimensional complex multichannel versions of Non-Symmetric Half Plane Autoregressive model, Quarter Plane Autoregressive model and Gauss Markov Random Field model are used to perform parametric power spectrum estimation of both luminance and the \textquotedblleft combined Chrominance\textquotedblright\ channels of the image. Accuracy and precision of these spectral estimates are proven quantitatively by performing tests on a large number of images. Spectral distance measures are calculated for the spectral information of luminance and Chrominance channels individually as well as combined through a combination coefficient. Using these distance measures, colour texture classification is done with $k$-nearest neighbour algorithm. A comparison of experimental results in IHLS with the ones in RGB indicate the significance of using IHLS for such analysis. They also show that colour texture characterization and percentage classification obtained by combined luminance and Chrominance structure information is better than the colour texture classification done using only the luminance structure information.

Khan A Wahid - One of the best experts on this subject based on the ideXlab platform.

  • image enhancement and space variant color reproduction method for endoscopic images using adaptive sigmoid function
    International Conference of the IEEE Engineering in Medicine and Biology Society, 2014
    Co-Authors: Mohammad S Imtiaz, Khan A Wahid
    Abstract:

    This paper presents an image enhancement and space-variant color reproduction method based on adaptive sigmoid function for endoscopic image. At first, using YCBCR conversion matrix, the color image is separated into luminance and Chrominance components. The adaptive sigmoid function with two controlling parameters is applied on the uniformly distributed luminance pixels. The space-variant color reproduction generates new Chrominance components by transferring and modifying old Chrominance based on texture information. Finally, new luminance and Chrominance components are converted into RGB color image. The proposed method highlights some of the tissue and vascular characteristics as well as pit patterns in lesion and polyp. The performance of the proposed scheme is compared with other related methods in terms of image quality, focus value, efficiency of color reproduction and statistic of visual representation.

  • EMBC - Image enhancement and space-variant color reproduction method for endoscopic images using adaptive sigmoid function.
    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2014
    Co-Authors: Mohammad S Imtiaz, Khan A Wahid
    Abstract:

    This paper presents an image enhancement and space-variant color reproduction method based on adaptive sigmoid function for endoscopic image. At first, using YCBCR conversion matrix, the color image is separated into luminance and Chrominance components. The adaptive sigmoid function with two controlling parameters is applied on the uniformly distributed luminance pixels. The space-variant color reproduction generates new Chrominance components by transferring and modifying old Chrominance based on texture information. Finally, new luminance and Chrominance components are converted into RGB color image. The proposed method highlights some of the tissue and vascular characteristics as well as pit patterns in lesion and polyp. The performance of the proposed scheme is compared with other related methods in terms of image quality, focus value, efficiency of color reproduction and statistic of visual representation.

Rabab K Ward - One of the best experts on this subject based on the ideXlab platform.

  • efficient Chrominance compensation for mpeg2 to h 264 transcoding
    International Conference on Acoustics Speech and Signal Processing, 2007
    Co-Authors: Qiang Tang, Panos Nasiopoulos, Rabab K Ward
    Abstract:

    Although open-loop transcoding is known as the most computational efficient transcoding structure, it is also known to introduce many distortions in the transcoded video. This paper addresses the Chrominance distortions resulting from the open-loop MPEG2 to H.264 transcoding structure and proposes algorithms to compensate for the Chrominance distortions. The open-loop structure is replaced by a closed-loop transcoding structure, which provides high-quality video by removing the Chrominance distortions, resulting in an average of 6 dB picture quality improvement.

  • ICASSP (1) - Efficient Chrominance Compensation for MPEG2 to H.264 Transcoding
    2007 IEEE International Conference on Acoustics Speech and Signal Processing - ICASSP '07, 2007
    Co-Authors: Qiang Tang, Panos Nasiopoulos, Rabab K Ward
    Abstract:

    Although open-loop transcoding is known as the most computational efficient transcoding structure, it is also known to introduce many distortions in the transcoded video. This paper addresses the Chrominance distortions resulting from the open-loop MPEG2 to H.264 transcoding structure and proposes algorithms to compensate for the Chrominance distortions. The open-loop structure is replaced by a closed-loop transcoding structure, which provides high-quality video by removing the Chrominance distortions, resulting in an average of 6 dB picture quality improvement.

Jean-christophe Burie - One of the best experts on this subject based on the ideXlab platform.

  • Choice of a pertinent color space for color texture characterization using parametric spectral analysis
    Pattern Recognition, 2011
    Co-Authors: Imtnan-ul-haque Qazi, Olivier Alata, Jean-christophe Burie, Ahmed Moussa, Christine Fernandez-maloigne
    Abstract:

    This article presents a comparison of different color spaces including RGB, IHLS and L*a*b* for color texture characterization. This comparison is based on the fusion of the independent spatial structure and color feature cues. In IHLS and L*a*b*, two channel complex color images are created from the luminance and the Chrominance values. For such images, two dimensional complex multichannel linear prediction models are used to perform parametric power spectrum estimation and the structure feature cues are computed from this estimated power spectrum. Quantitative comparison of auto spectra of luminance and combined Chrominance channels for different color spaces is done. This comparison is based on the degree of decorrelation between luminance and Chrominance information provided by different color space transformations. Three dimensional histograms are used as color feature cues. Then, to classify color textures, Kullback-Leibler divergence based symmetric distance measures are calculated for pure color, luminance structure and Chrominance structure feature cues. Individual as well as combined effect of information from all feature cues on classification results is then compared for different color spaces and different color texture data sets. The proposed color texture classification method performs better than the state of the art methods in certain cases. The L*a*b* color space gives us a better characterization of the Chrominance spatial structure as well as the overall spatial structure for all of the chosen data sets. Experimental results on pixel classification of color textures are also presented and discussed.

  • Colour Spectral Analysis for Spatial Structure Characterization of Textures in IHLS Colour Space
    Pattern Recognition, 2010
    Co-Authors: Imtnan-ul-haque Qazi, Olivier Alata, Jean-christophe Burie, Christine Fernandez-maloigne
    Abstract:

    In this article, linear prediction model based approach for colour texture characterization and classification in the Improved Hue Luminance and Saturation colour space is presented. Pure Chrominance structure information is used in addition with normally used luminance structure information for colour texture classification. Hue and saturation channels of a colour image in IHLS colour space are combined through a complex exponential to give a single channel which holds all the Chrominance information of the image. Two dimensional complex multichannel versions of Non-Symmetric Half Plane Autoregressive model, Quarter Plane Autoregressive model and Gauss Markov Random Field model are used to perform parametric power spectrum estimation of both luminance and the \textquotedblleft combined Chrominance\textquotedblright\ channels of the image. Accuracy and precision of these spectral estimates are proven quantitatively by performing tests on a large number of images. Spectral distance measures are calculated for the spectral information of luminance and Chrominance channels individually as well as combined through a combination coefficient. Using these distance measures, colour texture classification is done with $k$-nearest neighbour algorithm. A comparison of experimental results in IHLS with the ones in RGB indicate the significance of using IHLS for such analysis. They also show that colour texture characterization and percentage classification obtained by combined luminance and Chrominance structure information is better than the colour texture classification done using only the luminance structure information.