In this contribution the robustness of a novel steganographic scheme based on the generalized Fibonacci sequence against Chi-square attacks is investigated. In essence, an image is first represented in a basis defined...
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ISBN:
(纸本)9780819474957
In this contribution the robustness of a novel steganographic scheme based on the generalized Fibonacci sequence against Chi-square attacks is investigated. In essence, an image is first represented in a basis defined by a generalized Fibonacci sequence. Then the secret data are inserted by substitution technique into selected bit planes preserving the first order distributions, and finally, the inverse Fibonacci decomposition is applied to obtain the stego-image. Secret data are scrambled before the embedding to improve the security of the whole system. In order to perform Chi-square attacks, the knowledge of both the parameters determining the binary Fibonacci representation of an image is assumed. Experimental results show that no visual impairments are introduced and the probability of detecting the presence of hidden data is small even if a modest capacity loss is present.
A new architecture is presented for incremental computation of the two-dimensional inverse discrete cosine transform (2D-IDCT) in the context of progressive image decoding. This architecture offers advantages over exi...
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ISBN:
(纸本)0819425842
A new architecture is presented for incremental computation of the two-dimensional inverse discrete cosine transform (2D-IDCT) in the context of progressive image decoding. This architecture offers advantages over existing DCT inversion techniques for systems used in conjunction with progressive image coding schemes or those that must operate in environments with severe and/or time-varying resource constraints (e.g. real-time and low-power systems). The use of a bit-serial distributed arithmetic approach comprised of parallel input-pruned 2D-IDCT processing elements enables low-quality image reconstructions to be quickly and efficiently obtained using only a subset of the DCT coefficient bit stream. Initial approximate reconstructions can be improved in subsequent stages of incremental refinement according to the availability of processing resources or DCT coefficient data. Analysis is presented of image degradation at successive stages, illustrating uniform improvement according to quantitative and perceptual criteria.
In this paper we propose to evaluate both robustness and security of digital image watermarking techniques by considering the perceptual quality of un-marked images in terms of Weightened PSNR. The proposed tool is ba...
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ISBN:
(纸本)9780819474957
In this paper we propose to evaluate both robustness and security of digital image watermarking techniques by considering the perceptual quality of un-marked images in terms of Weightened PSNR. The proposed tool is based on genetic algorithms and is suitable for researchers to evaluate robustness performances of developed watermarking methods. Given a combination of selected attacks, the proposed framework looks for a fine parameterization of them ensuring a perceptual quality of the un-marked image lower than a given threshold. Correspondingly, a novel metric for robustness assessment is introduced. On the other hand, this tool results to be useful also in those scenarios where an attacker tries to remove the watermark to overcome copyright issues. Security assessment is provided by a stochastic search of the minimum degradation that needs to be introduced in order to obtain an un-marked version of the image as close as possible to the given one. Experimental results show the effectiveness of the proposed approach.
In this paper, we focus on the effective representation of the image, which is called the paired representation and reduces the image to the set of independent 1-D signals and splits the 2-D DFT into a minimal number ...
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ISBN:
(纸本)9780819474957
In this paper, we focus on the effective representation of the image, which is called the paired representation and reduces the image to the set of independent 1-D signals and splits the 2-D DFT into a minimal number of 1-D DFTs. The paired transform is a frequency and time representation of the image. Splitting-signals carry the spectral information in disjoint subsets of frequencies, which allows for enhancing the image by processing splitting-signals separately and changing the resolution of periodic structures composing the image. We present a new effective formula for the inverse 2-D paired transform, which can be used for solving the algebraic system of equations with measurement data for image reconstruction without using the Fourier transform technique. The image is reconstructed directly from the splitting-signals which can be calculated from projection data. The same inverse formula can be used for image enhancement, such as the known method of a-rooting. A new concept of direction images is introduced, that define the decomposition of the image by directions.
Low quality iris images such as blurry, low resolution images with poor illumination create a challenge for iris recognition systems. Therefore, an efficient enhancement of iris images are needed in challenging enviro...
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ISBN:
(纸本)9780819481313
Low quality iris images such as blurry, low resolution images with poor illumination create a challenge for iris recognition systems. Therefore, an efficient enhancement of iris images are needed in challenging environments. We propose a new iris recognition algorithm for enhancement of normalized iris images. Our algorithm is based on the logarithmic imageprocessing (LIP) image enhancement which is used as one of the 3 stages in the enhancement process. Methods are tested on the MBGC database to compare enrolled video iris images from 124 subjects with 220 pixels resolutions to query video portal images from 110 subjects with 120 pixels resolution. Results from processing challenging MBGC iris data show significant improvement in the performance of iris recognition algorithms in terms of equal error rates compared to the original (unenhanced images) and the other fast image enhancement methods.
Region-based active contours are a variational framework for image segmentation. It involves estimating the probability distributions of observed features within each image region. Subsequently, these so-called region...
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ISBN:
(纸本)9780819474957
Region-based active contours are a variational framework for image segmentation. It involves estimating the probability distributions of observed features within each image region. Subsequently, these so-called region descriptors are used to generate forces to move the contour toward real image boundaries. In this paper region descriptors are computed from samples within windows centered on contour pixels and they are named local region descriptors (LRDs). With these descriptors we introduce an equation for contour motion with two terms: growing and competing. This equation yields a novel type of AC that can adjust the behavior of contour pieces to image patches and to the presence of other contours. The quality of the proposed motion model is demonstrated on complex images.
In this paper, we investigate the use of the Stockwell Transform for image compression. The proposed technique uses the Discrete Orthogonal Stockwell Transform (DOST), an orthogonal version of the Discrete Stockwell T...
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ISBN:
(纸本)9780819474957
In this paper, we investigate the use of the Stockwell Transform for image compression. The proposed technique uses the Discrete Orthogonal Stockwell Transform (DOST), an orthogonal version of the Discrete Stockwell Transform (DST). These mathematical transforms provide a multiresolution spatial-frequency representation of a signal or image. First, we give a brief introduction for the Stockwell transform and the DOST. Then we outline a simplistic compression method based on setting the smallest coefficients to zero. In an experiment, we use this compression strategy on three different transforms: the Fast Fourier transform, the Daubechies wavelet transform and the DOST. The results show that the DOST outperforms the two other methods.
image segmentation is an important and difficult computer vision problem. Hyper-spectral images pose even more difficulty due to their high-dimensionality. Spectral clustering (SC) is a recently popular clustering/seg...
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ISBN:
(纸本)9780819474957
image segmentation is an important and difficult computer vision problem. Hyper-spectral images pose even more difficulty due to their high-dimensionality. Spectral clustering (SC) is a recently popular clustering/segmentation algorithm. In general, SC lifts the data to a high dimensional space, also known as the kernel trick, then derive eigenvectors in this new space, and finally using these new dimensions partition the data into clusters. We demonstrate that SC works efficiently when combined with covariance descriptors that can be used to assess pixelwise similarities rather than in the high-dimensional Euclidean space. We present the formulations and some preliminary results of the proposed hybrid image segmentation method for hyper-spectral images.
Multivariate analysis seeks to describe the relationship between an arbitrary number of variables. To explore high-dimensional data sets, projections are often used for data visualisation to aid discovering structure ...
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ISBN:
(纸本)9780819474957
Multivariate analysis seeks to describe the relationship between an arbitrary number of variables. To explore high-dimensional data sets, projections are often used for data visualisation to aid discovering structure or patterns that lead to the formation of statistical hypothesis. The basic concept necessitates a systematic search for lower-dimensional representations of the data that might show interesting structure(s). Motivated by the recent research on the image Grand Tour (IGT), which can be adapted to view guided projections by using objective indexes that are capable of revealing latent structures of the data, this paper presents a signal processing perspective on constructing such indexes under the unifying exploratory frameworks of Independent Component Analysis (ICA) and Projection Pursuit ( PP). Our investigation begins with an overview of dimension reduction techniques by means of orthogonal transforms, including the classical procedure of Principal Component Analysis (PCA), and extends to an application of the more powerful techniques of ICA in the context of our recent work on non-destructive testing technology by element specific x-ray imaging.
In this study, the radial basis functions based SG algorithm (SGRBF) is applied for evolution of level sets in image segmentation. The implementation of level set method in imageprocessing often involves solving part...
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ISBN:
(纸本)9780819474957
In this study, the radial basis functions based SG algorithm (SGRBF) is applied for evolution of level sets in image segmentation. The implementation of level set method in imageprocessing often involves solving partial differential equations (PDEs). Finite differences implicit scheme is a prevalent method to solve PDE for extending the evolution of level sets. Instead of using finite differences method, SGRBF is used in our study for evolving level sets. The SGRBF is a mathematical framework developed for function approximation using Gaussian RBFs. In SGRBF, the number and centers of the basis functions are determined in a systematic and mathematically sound way using a purely algebraic approach. The numerical results show that, except for a continuous representation of both the implicit function and its level sets, the algorithm we introduce here can reduce the computation cost by selecting the most contributive centers for radial basis functions.
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