Erratum: An improved UWB receiver employing generalized normal-Laplacian distribution model [IEICE Electronics Express Vol.8 (2011), No. 18 pp.1505-1510]

To improve the invisibility and robustness of the multiplicative watermarking algorithm, an adaptive image watermarking algorithm is proposed based on the visual saliency model and Laplacian distribution in the wavelet domain. The algorithm designs an adaptive multiplicative watermark strength factor by utilizing the energy aggregation of the high-frequency wavelet sub-band, texture masking and visual saliency characteristics. Then, the image blocks with high-energy are selected as the watermark embedding space to implement the imperceptibility of the watermark. In terms of watermark detection, the Laplacian distribution model is used to model the wavelet coefficients, and a blind watermark detection approach is exploited based on the maximum likelihood scheme. Finally, this paper performs the simulation analysis and comparison of the performance of the proposed algorithm. Experimental results show that the proposed algorithm is robust against additive white Gaussian noise, JPEG compression, median filtering, scaling, rotation attack and other attacks.

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Advanced background subtraction approach using Laplacian distribution model

2010 IEEE International Conference on Multimedia and Expo ◽

10.1109/icme.2010.5582598 ◽

2010 ◽

Cited By ~ 9

Author(s):

Fan-Chieh Cheng ◽

Shih-Chia Huang ◽

Shanq-Jang Ruan

Keyword(s):

Background Subtraction ◽

Distribution Model ◽

Laplacian Distribution

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Reliability of Generalized Normal Laplacian Distribution Model in TH-BPSK UWB Systems

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e94.a.1772 ◽

2011 ◽

Vol E94-A (8) ◽

pp. 1772-1775 ◽

Cited By ~ 3

Author(s):

Sangchoon KIM

Keyword(s):

Distribution Model ◽

Laplacian Distribution

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Detecting Activation in fMRI Data: An Approach Based on Sparse Representation of BOLD Signal

Mathematical Problems in Engineering ◽

10.1155/2018/1730149 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15

Author(s):

Blanca Guillen ◽

Jose L. Paredes ◽

Rubén Medina

Keyword(s):

Synthetic Data ◽

Real Data ◽

Spatial Domain ◽

Fmri Data ◽

Distribution Model ◽

Bold Signal ◽

Absolute Deviation ◽

Spatial Domains ◽

Laplacian Distribution ◽

The Time Domain

This paper proposes a simple yet effective approach for detecting activated voxels in fMRI data by exploiting the inherent sparsity property of the BOLD signal in temporal and spatial domains. In the time domain, the approach combines the General Linear Model (GLM) with a Least Absolute Deviation (LAD) based regression method regularized by the pseudonorm l0 to promote sparsity in the parameter vector of the model. In the spatial domain, detection of activated regions is based on thresholding the spatial map of estimated parameters associated with a particular stimulus. The threshold is calculated by exploiting the sparseness of the BOLD signal in the spatial domain assuming a Laplacian distribution model. The proposed approach is validated using synthetic and real fMRI data. For synthetic data, results show that the proposed approach is able to detect most activated voxels without any false activation. For real data, the method is evaluated through comparison with the SPM software. Results indicate that this approach can effectively find activated regions that are similar to those found by SPM, but using a much simpler approach. This study may lead to the development of robust spatial approaches to further simplifying the complexity of classical schemes.

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