The Experts below are selected from a list of 489 Experts worldwide ranked by ideXlab platform

King Ngi Ngan - One of the best experts on this subject based on the ideXlab platform.

  • Transactions Papers Face Segmentation Using Skin-Color Map in Videophone Applications
    2014
    Co-Authors: Douglas Chai, King Ngi Ngan, Student Member, Senior Member
    Abstract:

    Abstract—This paper addresses our proposed method to au-tomatically segment out a person’s face from a given image that consists of a head-and-shoulders view of the person and a complex background scene. The method involves a fast, reliable, and effective algorithm that exploits the spatial distribution characteristics of human skin color. A universal skin-color map is derived and used on the Chrominance Component of the input image to detect pixels with skin-color appearance. Then, based on the spatial distribution of the detected skin-color pixels and their corresponding luminance values, the algorithm employs a set of novel regularization processes to reinforce regions of skin-color pixels that are more likely to belong to the facial regions and eliminate those that are not. The performance of the face-segmentation algorithm is illustrated by some simulation results carried out on various head-and-shoulders test images. The use of face segmentation for video coding in applications such as videotelephony is then presented. We explain how the face-segmentation results can be used to improve the perceptual quality of a videophone sequence encoded by the H.261-compliant coder. Index Terms — Color image processing, face location, facial image analysis, H.261, image segmentation, quantization, video coding, videophone communication. I

  • face segmentation using skin color map in videophone applications
    IEEE Transactions on Circuits and Systems for Video Technology, 1999
    Co-Authors: Douglas Chai, King Ngi Ngan
    Abstract:

    This paper addresses our proposed method to automatically segment out a person's face from a given image that consists of a head-and-shoulders view of the person and a complex background scene. The method involves a fast, reliable, and effective algorithm that exploits the spatial distribution characteristics of human skin color. A universal skin-color map is derived and used on the Chrominance Component of the input image to detect pixels with skin-color appearance. Then, based on the spatial distribution of the detected skin-color pixels and their corresponding luminance values, the algorithm employs a set of novel regularization processes to reinforce regions of skin-color pixels that are more likely to belong to the facial regions and eliminate those that are not. The performance of the face-segmentation algorithm is illustrated by some simulation results carried out on various head-and-shoulders test images. The use of face segmentation for video coding in applications such as videotelephony is then presented. We explain how the face-segmentation results can be used to improve the perceptual quality of a videophone sequence encoded by the H.261-compliant coder.

Douglas Chai - One of the best experts on this subject based on the ideXlab platform.

  • Transactions Papers Face Segmentation Using Skin-Color Map in Videophone Applications
    2014
    Co-Authors: Douglas Chai, King Ngi Ngan, Student Member, Senior Member
    Abstract:

    Abstract—This paper addresses our proposed method to au-tomatically segment out a person’s face from a given image that consists of a head-and-shoulders view of the person and a complex background scene. The method involves a fast, reliable, and effective algorithm that exploits the spatial distribution characteristics of human skin color. A universal skin-color map is derived and used on the Chrominance Component of the input image to detect pixels with skin-color appearance. Then, based on the spatial distribution of the detected skin-color pixels and their corresponding luminance values, the algorithm employs a set of novel regularization processes to reinforce regions of skin-color pixels that are more likely to belong to the facial regions and eliminate those that are not. The performance of the face-segmentation algorithm is illustrated by some simulation results carried out on various head-and-shoulders test images. The use of face segmentation for video coding in applications such as videotelephony is then presented. We explain how the face-segmentation results can be used to improve the perceptual quality of a videophone sequence encoded by the H.261-compliant coder. Index Terms — Color image processing, face location, facial image analysis, H.261, image segmentation, quantization, video coding, videophone communication. I

  • face segmentation using skin color map in videophone applications
    IEEE Transactions on Circuits and Systems for Video Technology, 1999
    Co-Authors: Douglas Chai, King Ngi Ngan
    Abstract:

    This paper addresses our proposed method to automatically segment out a person's face from a given image that consists of a head-and-shoulders view of the person and a complex background scene. The method involves a fast, reliable, and effective algorithm that exploits the spatial distribution characteristics of human skin color. A universal skin-color map is derived and used on the Chrominance Component of the input image to detect pixels with skin-color appearance. Then, based on the spatial distribution of the detected skin-color pixels and their corresponding luminance values, the algorithm employs a set of novel regularization processes to reinforce regions of skin-color pixels that are more likely to belong to the facial regions and eliminate those that are not. The performance of the face-segmentation algorithm is illustrated by some simulation results carried out on various head-and-shoulders test images. The use of face segmentation for video coding in applications such as videotelephony is then presented. We explain how the face-segmentation results can be used to improve the perceptual quality of a videophone sequence encoded by the H.261-compliant coder.

Amiri Delaram - One of the best experts on this subject based on the ideXlab platform.

  • Bilateral and adaptive loop filter implementations in 3D-high efficiency video coding standard
    'Purdue University (bepress)', 2015
    Co-Authors: Amiri Delaram
    Abstract:

    In this thesis, we describe a different implementation for in loop filtering method for 3D-HEVC. First we propose the use of adaptive loop filtering (ALF) technique for 3D-HEVC standard in-loop filtering. This filter uses Wiener-based method to minimize the Mean Squared Error between filtered pixel and original pixels. The performance of adaptive loop filter in picture based level is evaluated. Results show up to of 0.2 dB PSNR improvement in Luminance Component for the texture and 2.1 dB for the depth. In addition, we obtain up to 0.1 dB improvement in Chrominance Component for the texture view after applying this filter in picture based filtering. Moreover, a design of an in-loop filtering with Fast Bilateral Filter for 3D-HEVC standard is proposed. Bilateral filter is a filter that smoothes an image while preserving strong edges and it can remove the artifacts in an image. Performance of the bilateral filter in picture based level for 3D-HEVC is evaluated. Test model HTM- 6.2 is used to demonstrate the results. Results show up to of 20 percent of reduction in processing time of 3D-HEVC with less than affecting PSNR of the encoded 3D video using Fast Bilateral Filter

  • Bilateral and adaptive loop filter implementations in 3D-high efficiency video coding standard
    2015
    Co-Authors: Amiri Delaram
    Abstract:

    Indiana University-Purdue University Indianapolis (IUPUI)In this thesis, we describe a different implementation for in loop filtering method for 3D-HEVC. First we propose the use of adaptive loop filtering (ALF) technique for 3D-HEVC standard in-loop filtering. This filter uses Wiener–based method to minimize the Mean Squared Error between filtered pixel and original pixels. The performance of adaptive loop filter in picture based level is evaluated. Results show up to of 0.2 dB PSNR improvement in Luminance Component for the texture and 2.1 dB for the depth. In addition, we obtain up to 0.1 dB improvement in Chrominance Component for the texture view after applying this filter in picture based filtering. Moreover, a design of an in-loop filtering with Fast Bilateral Filter for 3D-HEVC standard is proposed. Bilateral filter is a filter that smoothes an image while preserving strong edges and it can remove the artifacts in an image. Performance of the bilateral filter in picture based level for 3D-HEVC is evaluated. Test model HTM- 6.2 is used to demonstrate the results. Results show up to of 20 percent of reduction in processing time of 3D-HEVC with less than affecting PSNR of the encoded 3D video using Fast Bilateral Filter

George Bebis - One of the best experts on this subject based on the ideXlab platform.

  • Passive detection of image forgery using DCT and local binary pattern
    Signal Image and Video Processing, 2017
    Co-Authors: Ahmad A. Alahmadi, Hatim Aboalsamh, George Bebis, Muhammad Hussain, Ghulam Muhammad, Hassan Mathkour
    Abstract:

    With the development of easy-to-use and sophisticated image editing software, the alteration of the contents of digital images has become very easy to do and hard to detect. A digital image is a very rich source of information and can capture any event perfectly, but because of this reason, its authenticity is questionable. In this paper, a novel passive image forgery detection method is proposed based on local binary pattern (LBP) and discrete cosine transform (DCT) to detect copy–move and splicing forgeries. First, from the Chrominance Component of the input image, discriminative localized features are extracted by applying 2D DCT in LBP space. Then, support vector machine is used for detection. Experiments carried out on three image forgery benchmark datasets demonstrate the superiority of the method over recent methods in terms of detection accuracy.

  • Splicing image forgery detection based on DCT and Local Binary Pattern
    2013 IEEE Global Conference on Signal and Information Processing GlobalSIP 2013 - Proceedings, 2013
    Co-Authors: Ahmad A. Alahmadi, Hatim Aboalsamh, Muhammad Hussain, Ghulam Muhammad, George Bebis
    Abstract:

    The authenticity of a digital image suffers from severe threats due to the rise of powerful digital image editing tools that easily alter the image contents without leaving any visible traces of such changes. In this paper, a novel passive splicing image forgery detection scheme based on Local Binary Pattern (LBP) and Discrete Cosine Transform (DCT) is proposed. First, the Chrominance Component of the input image is divided into overlapping blocks. Then, for each block, LBP is calculated and transformed into frequency domain using 2D DCT. Finally, standard deviations are calculated of respective frequency coefficients of all blocks and they are used as features. For classification, a support vector machine (SVM) is used. Experimental results on benchmark splicing image forgery databases show that the detection accuracy of the proposed method is up to 97%, which is the best accuracy so far.

Ahmad A. Alahmadi - One of the best experts on this subject based on the ideXlab platform.

  • Passive detection of image forgery using DCT and local binary pattern
    Signal Image and Video Processing, 2017
    Co-Authors: Ahmad A. Alahmadi, Hatim Aboalsamh, George Bebis, Muhammad Hussain, Ghulam Muhammad, Hassan Mathkour
    Abstract:

    With the development of easy-to-use and sophisticated image editing software, the alteration of the contents of digital images has become very easy to do and hard to detect. A digital image is a very rich source of information and can capture any event perfectly, but because of this reason, its authenticity is questionable. In this paper, a novel passive image forgery detection method is proposed based on local binary pattern (LBP) and discrete cosine transform (DCT) to detect copy–move and splicing forgeries. First, from the Chrominance Component of the input image, discriminative localized features are extracted by applying 2D DCT in LBP space. Then, support vector machine is used for detection. Experiments carried out on three image forgery benchmark datasets demonstrate the superiority of the method over recent methods in terms of detection accuracy.

  • Splicing image forgery detection based on DCT and Local Binary Pattern
    2013 IEEE Global Conference on Signal and Information Processing GlobalSIP 2013 - Proceedings, 2013
    Co-Authors: Ahmad A. Alahmadi, Hatim Aboalsamh, Muhammad Hussain, Ghulam Muhammad, George Bebis
    Abstract:

    The authenticity of a digital image suffers from severe threats due to the rise of powerful digital image editing tools that easily alter the image contents without leaving any visible traces of such changes. In this paper, a novel passive splicing image forgery detection scheme based on Local Binary Pattern (LBP) and Discrete Cosine Transform (DCT) is proposed. First, the Chrominance Component of the input image is divided into overlapping blocks. Then, for each block, LBP is calculated and transformed into frequency domain using 2D DCT. Finally, standard deviations are calculated of respective frequency coefficients of all blocks and they are used as features. For classification, a support vector machine (SVM) is used. Experimental results on benchmark splicing image forgery databases show that the detection accuracy of the proposed method is up to 97%, which is the best accuracy so far.