The Experts below are selected from a list of 222 Experts worldwide ranked by ideXlab platform

Changquan Calvin Sun - One of the best experts on this subject based on the ideXlab platform.

Raghunath S Holambe - One of the best experts on this subject based on the ideXlab platform.

  • fast communication radon and discrete cosine transforms based feature extraction and dimensionality reduction approach for face recognition
    Signal Processing, 2008
    Co-Authors: Dattatray V Jadhav, Raghunath S Holambe
    Abstract:

    This paper presents a pattern recognition framework for face recognition based on the combination of Radon and discrete cosine transforms (DCT). The Property of Radon transform to enhance the low frequency components, which are useful for face recognition, has been exploited to derive the effective facial features. Data Compaction Property of DCT yields lower-dimensional feature vector. The proposed technique computes Radon projections in different orientations and captures the directional features of the face images. Further, DCT applied on Radon projections provides frequency features. The technique is invariant to in-plane rotation (tilt) and robust to zero mean white noise. The proposed algorithm is evaluated using FERET and ORL databases. The experimental results show the superiority of the proposed method compared to some of the existing algorithms.

  • fast communication radon and discrete cosine transforms based feature extraction and dimensionality reduction approach for face recognition
    Signal Processing, 2008
    Co-Authors: Dattatray V Jadhav, Raghunath S Holambe
    Abstract:

    This paper presents a pattern recognition framework for face recognition based on the combination of Radon and discrete cosine transforms (DCT). The Property of Radon transform to enhance the low frequency components, which are useful for face recognition, has been exploited to derive the effective facial features. Data Compaction Property of DCT yields lower-dimensional feature vector. The proposed technique computes Radon projections in different orientations and captures the directional features of the face images. Further, DCT applied on Radon projections provides frequency features. The technique is invariant to in-plane rotation (tilt) and robust to zero mean white noise. The proposed algorithm is evaluated using FERET and ORL databases. The experimental results show the superiority of the proposed method compared to some of the existing algorithms.

Aparajita Ojha - One of the best experts on this subject based on the ideXlab platform.

  • Significant region based robust watermarking scheme in lifting wavelet transform domain
    Expert Systems With Applications, 2015
    Co-Authors: Vivek Singh Verma, Aparajita Ojha
    Abstract:

    It discusses the experimental flaw in Lin et al. (2009) and Run et al. (2011).Different secret keys and region based strategy helps the system to be more secure.Remarkably efficient especially in case of JPEG compression attack.Also provides more robustness against various signal processing operations.Shows noteworthy comparisons with currently existing techniques. With the aim of designing a more robust digital watermarking scheme against various unintentional and intentional attacks, a significant region (SR) based image watermarking technique is proposed in the present paper using lifting wavelet transform (LWT). While the energy Compaction Property of LWT provides higher tolerance against image distortion as opposed to conventional wavelet transform, the proposed block selection procedure provides greater security over the existing watermarking approaches. Non-overlapping coefficient blocks from the lowpass subband are selected after applying three levels of LWT and using certain criterion based on minimum coefficient difference and a threshold value. To disguise the intruder completely, secret key based randomization of coefficients, blocks, and watermark bits is incorporated. Maximum coefficients difference of each selected block and the same threshold value are then used for deciding which block to choose for embedding the bit 0 or 1. Performance of the proposed method is analyzed and compared with some of the existing schemes that demonstrates that the proposed scheme not only outperforms other methods with respect to various attacks for most of the cases, but also maintains a satisfactory image quality.

  • Digital watermark extraction using support vector machine with principal component analysis based feature reduction
    Journal of Visual Communication and Image Representation, 2015
    Co-Authors: Vivek Singh Verma, Aparajita Ojha
    Abstract:

    Efficient especially in case of JPEG compression attack with low quality factor.Also provides more robustness against various signal processing operations.Randomization using different secret keys helps the system to be more secure.It uses simpler features set for training and testing the SVM.Shows noteworthy comparisons with currently existing techniques. This paper proposes a new approach for watermark extraction using support vector machine (SVM) with principal component analysis (PCA) based feature reduction. In this method, the original cover image is decomposed up to three level using lifting wavelet transform (LWT), and lowpass subband is selected for data hiding purpose. The lowpass subband is divided into small blocks, and a binary watermark is embedded into the original cover image by quantizing the two maximum coefficients of the block. In order to extract watermark bits with maximum correlation, SVM based binary classification approach is incorporated. The training and testing patterns are constructed by employing a reduced set of features along with block coefficients. Firstly, different features are obtained by evaluating the statistical parameters of each block coefficients, and then PCA is utilized to reduce this feature set. As far as security is concerned, randomization of coefficients, blocks, and watermark bits enhances the security of system. Furthermore, energy Compaction Property of LWT increases the robustness in comparison to conventional wavelet transform. A comparison of the proposed method with some of the recent techniques shows remarkable improvement in terms of robustness and security of the watermark.

Sibo Liu - One of the best experts on this subject based on the ideXlab platform.

Dattatray V Jadhav - One of the best experts on this subject based on the ideXlab platform.

  • fast communication radon and discrete cosine transforms based feature extraction and dimensionality reduction approach for face recognition
    Signal Processing, 2008
    Co-Authors: Dattatray V Jadhav, Raghunath S Holambe
    Abstract:

    This paper presents a pattern recognition framework for face recognition based on the combination of Radon and discrete cosine transforms (DCT). The Property of Radon transform to enhance the low frequency components, which are useful for face recognition, has been exploited to derive the effective facial features. Data Compaction Property of DCT yields lower-dimensional feature vector. The proposed technique computes Radon projections in different orientations and captures the directional features of the face images. Further, DCT applied on Radon projections provides frequency features. The technique is invariant to in-plane rotation (tilt) and robust to zero mean white noise. The proposed algorithm is evaluated using FERET and ORL databases. The experimental results show the superiority of the proposed method compared to some of the existing algorithms.

  • fast communication radon and discrete cosine transforms based feature extraction and dimensionality reduction approach for face recognition
    Signal Processing, 2008
    Co-Authors: Dattatray V Jadhav, Raghunath S Holambe
    Abstract:

    This paper presents a pattern recognition framework for face recognition based on the combination of Radon and discrete cosine transforms (DCT). The Property of Radon transform to enhance the low frequency components, which are useful for face recognition, has been exploited to derive the effective facial features. Data Compaction Property of DCT yields lower-dimensional feature vector. The proposed technique computes Radon projections in different orientations and captures the directional features of the face images. Further, DCT applied on Radon projections provides frequency features. The technique is invariant to in-plane rotation (tilt) and robust to zero mean white noise. The proposed algorithm is evaluated using FERET and ORL databases. The experimental results show the superiority of the proposed method compared to some of the existing algorithms.