Digital Audio Watermarking Scheme Based on Independent Component Analysis in DWT Domain

2011 ◽  
Vol 219-220 ◽  
pp. 1121-1125 ◽  
Author(s):  
Rui Chen ◽  
Yu Lin Lan ◽  
Reza Asharif Mohammad
Keyword(s):  
Independent Component Analysis ◽  
Low Frequency ◽  
Component Analysis ◽  
Extraction Process ◽  
Independent Component ◽  
Audio Watermarking ◽  
Discrete Wavelet ◽  
Digital Audio ◽  
Digital Audio Watermarking ◽  
Watermarking Scheme

This paper proposed a digital audio watermarking scheme based on independent component analysis (ICA) in DWT domain. The embedding process make full use of the multi-resolution characteristic of discrete wavelet transform (DWT), performing 3-level DWT. Selecting the low frequency coefficient appropriately as the embed location to make sure of the balance between the transparency and robustness. Then constructing the ICA model to embed the watermarking. The extraction process is similar with ICA’s goal, it’s used in extraction makes the scheme simple for implementation. The experiment results show that the proposed scheme has good robustness against common attacks, as well as transparency.

Digital Audio Watermarking Scheme Based on Independent Component Analysis in Stereo Sound

2012 ◽  
Vol 459 ◽  
pp. 469-473
Author(s):  
Rui Chen ◽  
Yu Lin Lan ◽  
Mohammad Reza Asharif
Keyword(s):  
Independent Component Analysis ◽  
Low Frequency ◽  
Component Analysis ◽  
Extraction Process ◽  
Independent Component ◽  
Audio Watermarking ◽  
Discrete Wavelet ◽  
Digital Audio ◽  
Digital Audio Watermarking ◽  
Watermarking Scheme

This paper proposed a digital audio watermarking scheme based on independent component analysis (ICA) in stereo sound. In order to make full use of the multi-channel characteristic of stereo sound, The watermarking embedded into the two channel, half respectively. Also using the multi-resolution characteristic of discrete wavelet transform, performing 3-level DWT in each channel. Selecting the low frequency coefficient appropriately as the embed location to make sure of the balance between the transparency and robustness. The extraction process is similar with ICA’ s goal, it’s used in extraction makes the scheme simple for implementation. The experiment results show that the proposed scheme has good robustness against common attacks, as well as transparency

Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

2020 ◽  
Vol 2020 (14) ◽  
pp. 357-1-357-6
Author(s):  
Luisa F. Polanía ◽  
Raja Bala ◽  
Ankur Purwar ◽  
Paul Matts ◽  
Martin Maltz
Keyword(s):  
Independent Component Analysis ◽  
Spectral Reflectance ◽  
Source Separation ◽  
Principal Component ◽  
Component Analysis ◽  
Previous Method ◽  
Independent Component ◽  
Light Transport ◽  
Camera Model ◽  
Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

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