In this paper, we present a novel algorithm for high capacity data embedding. The proposed algorithm gives high hiding ratios up to 1/13 ( 1 embedded bit out of 13 raw image pixels) subject to JPEG compression with qu...
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ISBN:
(纸本)0780362985
In this paper, we present a novel algorithm for high capacity data embedding. The proposed algorithm gives high hiding ratios up to 1/13 ( 1 embedded bit out of 13 raw image pixels) subject to JPEG compression with quality factor equals 75. With such high capacity, one can easily embed some important regions of an image inside the image itself with very small perceptual distortion. In the proposed algorithm, DCT coefficients of 8x8 blocks are fil st rearranged using Hilbert curves[5]. data is embedded using projections on a random orthogonal set. The algorithm recovers the embedded data without any reference to the original image, with very low BER (around 0.1%). Finally, the proposed algorithm shows robustness to JPEG compression.
In this study, the ROC curves for several alternative solutions are derived. One solution considers all possible spectral n-tuples within a small region. The other applies a spatial maximum operator to each spectral b...
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In this study, the ROC curves for several alternative solutions are derived. One solution considers all possible spectral n-tuples within a small region. The other applies a spatial maximum operator to each spectral band prior to the anomally detector. The ROC curves obtained using both solutions are compared to establish relative levels of performance.
We present a Bayesian algorithm for optimal multiframe detection and tracking of small extended targets in two-dimensional (2D) finite resolution images. The algorithm integrates detection and tracking into a single f...
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Regions of interest that contain smalltargets often cover a small number of pixels, e.g., 100 or fewer. For such regions vision-based super-resolution techniques are feasible that would be infeasible for regions that...
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Regions of interest that contain smalltargets often cover a small number of pixels, e.g., 100 or fewer. For such regions vision-based super-resolution techniques are feasible that would be infeasible for regions that cover a large number of pixels. One such technique centers basis functions (such as Gaussians) of the same width on all pixels and adjusts their amplitudes so that the sum of the basis functions integrated over each pixel is its gray value. This technique implements super-resolution in that the sum of basis functions determines the gray values of sub-pixels of any size. The resulting super-resolved visualizations, each characterized by a different basis function width, may enable the recognition of smalltargets that would otherwise remain unrecognized.
An extended Kalman filter was developed by Maybeck and Mercier to track unresolved or point targets whose spatial signature is the point spread function of the sensor. An extension of this filter which takes into acco...
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An extended Kalman filter was developed by Maybeck and Mercier to track unresolved or point targets whose spatial signature is the point spread function of the sensor. An extension of this filter which takes into account the target shape and its variations with aspect angle will be developed and should offer improvement in several areas: performance against structured clutter;Maybeck considered only a white noise background;the errors and computations associated with segmentation are eliminated.
Proper choice of prior distributions is a very important issue in Bayesian methodology. It is particularly important when the number of available data for processing is rather small. When little is known a priori, non...
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In a recent paper, van der Veen has developed a baseband-equivalent data model for antenna array reception of multiple sources subject to incoherent multipath with small delay spread. Capitalizing on the structure of ...
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Most real world signals consist of stronger low frequency components than high frequency ones. However, in many cases, high frequency components with small amplitude values represent the components of interest. Althou...
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We present a bilinear approach to mutiple-input multiple-output (MIMO) blind channel estimation where products of channel parameters are first estimated from the covariance of the received data, The channel parameters...
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We present a bilinear approach to mutiple-input multiple-output (MIMO) blind channel estimation where products of channel parameters are first estimated from the covariance of the received data, The channel parameters are then obtained as the dominant eigenvectors of the outer-product estimate. Necessary and sufficient identifiability conditions are presented for a single channel and extended to the multichannel case, It is found that this technique can identify the channel to within a subspace ambiguity, as long as the basis functions for the channel satisfy certain constraints, regardless of the left invertability of the channel matrix. One important requirement for identifiability is that the number of channel parameters is small compared with the channel length;advantageously, this is exactly the situation in which this algorithm has significantly lower complexity than competing (parametric, multiuser) blind algorithms. Simulations show that the technique is applicable in situations where typical identifiability conditions fail: common nulls, a single symbol-spaced channel, and more users than channels. These simulations are :: for the "almost flat" faded situation when the propagation delay spread is a fraction of the transmission pulse duration las might : be found in current TDMA systems), Comparisons are made,.,when possible, to a subspace method incorporating knowledge of the basis functions. The bilinear approach requires significantly less computation but performs better than the subspace method at low SNR, especially for multiple users.
The batch Maximum Likelihood Estimator, combined with Probabilistic data Association (ML-PDA), has been shown to be effective in acquiring low observable (LO) - low SNR - non-maneuvering targets in the presence of hea...
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The batch Maximum Likelihood Estimator, combined with Probabilistic data Association (ML-PDA), has been shown to be effective in acquiring low observable (LO) - low SNR - non-maneuvering targets in the presence of heavy clutter. The use of signal strength or amplitude information (AI) in the ML-PDA estimator further facilitates the acquisition of weak targets. In this paper we present an adaptive algorithm, which uses the ML-PDA estimator with AI in a sliding-window fashion, to detect high-speed targets in heavy clutter using electro-optical (EO) sensors. The initial time and the length of the sliding-window are adjusted adaptively according to the information content of the received measurements. A track validation scheme via hypothesis testing is developed to confirm the estimated track, that is, the presence of a target, in each window. The sliding-window ML-PDA approach, together with track validation, enables early detection by rejecting noninformative scans, target reacquisition in case of temporary target disappearance and the handling of targets with speeds evolving over time. The proposed algorithm is shown to detect the target, which is hidden in as many as 600 false alarms per scan, 10 frames earlier than the Multiple Hypothesis Tracking (MHT) algorithm.
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