modification of adaptive control laws using adaptive gain feedback plant bypass has been suggested as a solution to the problems that arise when adaptive systems are used to control non minimum phase plants or plants with unmodelled dynamics it is shown that plant bypass is normally not feasible it is demonstrated that realizable adaptive systems using plant bypass are combined fixed parameter and adaptive feedback systems the fixed parameter feedback provides adaptive gain feedback agf an adaptive control law is modified using agf and the characteristics of this modified adaptive scheme are investigated

dct domain watermarking techniques for still images detector performance analysis and a new structure a spread spectrum like discrete cosine transform dct domain watermarking technique for copyright protection of still digital images is analyzed the dct is applied in blocks of 8 spl times 8 pixels as in the jpeg algorithm the watermark can encode information to track illegal misuses for flexibility purposes the original image is not necessary during the ownership verification process so it must be modeled by noise two tests are involved in the ownership verification stage watermark decoding in which the message carried by the watermark is extracted and watermark detection which decides whether a given image contains a watermark generated with a certain key we apply generalized gaussian distributions to statistically model the dct coefficients of the original image and show how the resulting detector structures lead to considerable improvements in performance with respect to the correlation receiver which has been widely considered in the literature and makes use of the gaussian noise assumption as a result of our work analytical expressions for performance measures such as the probability of errors in watermark decoding and the probabilities of false alarms and of detection in watermark detection are derived and contrasted with experimental results

a high payload frequency based reversible image hiding method to provide both a high payload capacity and a good stego image quality a high payload frequency based reversible image hiding hpfrih method was presented in this paper these performances can be achieved by transforming a cover image from the spatial domain into the frequency domain through haar digital wavelet transform hdwt method followed by an adaptive arithmetic coding method to encode the hdwt coefficients in a high frequency band the hpfrih method was used to conceal both the compressed data and the secret data in the high frequency band this simple method can completely reconstruct the cover image only from the stego image moreover experimental results showed that the hpfrih method offers both impressive hiding capacity and satisfied stego image quality

similarity based classification of sequences using hidden markov models hidden markov models hmm are a widely used tool for sequence modelling in the sequence classification case the standard approach consists of training one hmm for each class and then using a standard bayesian classification rule in this paper we introduce a novel classification scheme for sequences based on hmms which is obtained by extending the recently proposed similarity based classification paradigm to hmm based classification in this approach each object is described by the vector of its similarities with respect to a predetermined set of other objects where these similarities are supported by hmms a central problem is the high dimensionality of resulting space and to deal with it three alternatives are investigated synthetic and real experiments show that the similarity based approach outperforms standard hmm classification schemes

a blind dual color images watermarking based on singular value decomposition in this paper a blind watermarking scheme based on singular value decomposition svd is proposed by analyzing the orthogonal matrix u via svd it is found that there exists a strong similarity correlation between the second row first column element and the third row first column element hence this work will utilize this property for image watermarking firstly the 4x4 non overlapping pixels block of each component in color host image is processed by svd and then the color watermark is embedded by slightly modifying the value of the second row first column element and the third row first column one of u matrix and the modified relation can be utilized to extract watermark hence without resorting to the original watermark image and original host image the embedded watermark can be easily extracted the experiment results show that the proposed watermarking algorithm can ensure the invisibility and stronger robustness for the common image processing operations and geometric attacks and the performance of this proposed method outperforms that of other proposed methods considered in this work

locally optimal detection of image watermarks in the wavelet domain using bessel k form distribution a uniformly most powerful watermark detector which applies the bessel k form bkf probability density function to model the noise distribution was proposed by bian and liang in this paper we derive a locally optimum lo detector using the same noise model since the literature lacks thorough discussion on the performance of the bkf lo nonlinearities the performance of the proposed detector is discussed in detail first we prove that the test statistic of the proposed detector is asymptotically gaussian and evaluate the actual performance of the proposed detector using the receiver operating characteristic roc then the large sample performance of the proposed detector is evaluated using asymptotic relative efficiency are and maximum are the experimental results show that the proposed detector has a good performance with or without attacks in terms of its roc curves particularly when the watermark is weak therefore the proposed method is suitable for wavelet domain watermark detection particularly when the watermark is weak

2 d shape classification using hidden markov model the authors present a planar shape recognition approach based on the hidden markov model and autoregressive parameters this approach segments closed shapes to make classifications at a finer level the algorithm can tolerate a lot of shape contour perturbation and a moderate amount of occlusion an orientation scheme is described to make the overall classification insensitive to shape orientation excellent recognition results have been reported a distinct advantage of the approach is that the classifier does not have to be trained again when a new class of shapes is added

color image watermark extraction based on support vector machines abstract this work proposes a novel watermarking technique called svm based color image watermarking sciw based on support vector machines svms for the authentication of color images to protect the copyright of a color image a signature a watermark which is represented by a sequence of binary data is embedded in the color image the watermark extraction issue can be treated as a classification problem involving binary classes the sciw method constructs a set of training patterns with the use of binary labels by employing three image features which are the differences between a local image statistic and the luminance value of the center pixel in a sliding window with three distinct shapes this set of training patterns is gathered from a pair of images an original image and its corresponding watermarked image in the spatial domain a quasi optimal hyperplane a binary classifier can be realized by an svm the sciw method utilizes this set of training patterns to train the svm and then applies the trained svm to classify a set of testing patterns following the results produced by the classifier the trained svm the sciw method retrieves the hidden signature without the original image during watermark extraction experimental results have demonstrated that the sciw method is sufficiently robust against several color image manipulations and that it outperforms other proposed methods considered in this work

an improved adaptive control for robust adaptation an adaptive control law consisting of an integral and proportional adaptation is suggested it is shown that the addition of the proportional adaptation term to the control law improves the performance of the adaptive system although this is done in the context of a first order system it is believed that this may also be the case for higher order systems the scalar case is only discussed simulation results are presented to complement the theoretical developments

restudy on svd based watermarking scheme this paper presents two notes for singular value decomposition svd based watermarking scheme the presented two notes can increase invisibility and robustness when embedding watermark into u component or v component of svd

joint coding and embedding techniques for multimedia images hiding data in digital images is a technique for secret data communication it involves embedding important data into a cover image in digital form with minimal perceptible degradation in this paper we first propose an improved data hiding method for side match vector quantization smvq compressed images which utilizes the codeword pairing procedure to modulate the index code of each image block in order to take it a step further and obtain a larger embedding capacity we remodel the first proposed method into a new one with the power of variable rate data embedding through the use of a multiple base notational system according to the experimental results the two proposed methods are indeed capable of providing a larger embedding capacity without causing noticeable distortions of stego images in comparison with previous methods moreover our methods also maintain a low compression bit rate without auxiliary data

robust optimum detection of transform domain multiplicative watermarks digital watermarking is an emerging technique to protect data security and intellectual property right identification or verification of watermarking patterns can be achieved by detecting watermarks in received signals however one of the biggest challenges in watermarking detection is that the strengths of the watermark signals will change after being distorted by an attacker in a watermarking channel meanwhile the embedding strengths may be adapted to the original signals which are unknown at the receiver end further the original signals are often highly non gaussian although some work has been done on optimum detection of watermarks the uncertainty of watermark signal strengths and real statistical behavior of multimedia contents have not been taken into account simultaneously much more study is needed to enhance the performance of watermarking systems since multiplicative watermarks are robust and well suited for copyright protection this paper presents our investigation on robust optimum detection of multiplicative watermarks for sub band transformed domains such as the discrete cosine transform dct discrete wavelet transform dwt and pyramid transform a class of generalized correlators is constructed based on the generalized gaussian distributions thresholding methods to achieve a given false alarm rate and the performance analyses are provided the square root detector is designed and demonstrated to have near optimal performance for a large set of natural images and can be employed as a universally optimal detector or decoder for images and video the locally most powerful detection method is then extended to dft domain multiplicative watermarking with the magnitudes of coefficients modeled by the weibull distributions another class of detectors is built based on this statistical modeling the robust optimum detection of multiplicative watermarks can be applied to copyright notification enforcement and broadcast monitoring we have applied the robust optimum watermarking detection to combined audio and video watermarking

a new decoder for the optimum recovery of nonadditive watermarks watermark detection i e the detection of an invisible signal hidden within an image for copyright protection or data authentication has classically been tackled by means of correlation based techniques nevertheless when watermark embedding does not obey an additive rule or when the features the watermark is superimposed on do not follow a gaussian pdf correlation based decoding is not the optimum choice a new decoding algorithm is presented here which is optimum for nonadditive watermarks embedded in the magnitude of a set of full frame dft coefficients of the host image by relying on statistical decision theory the structure of the optimum is derived according to the neyman pearson criterion thus permitting to minimize the missed detection probability subject to a given false detection rate the validity of the optimum decoder has been tested thoroughly to assess the improvement it permits to achieve from a robustness perspective the results we obtained confirm the superiority of the novel algorithm with respect to classical correlation based decoding

a new data hiding scheme for binary image authentication with small image distortion a new data hiding scheme for binary image authentication that has a small distortion of the cover image is proposed in this paper using the data embedding algorithm that is based on hamming codes the proposed scheme embeds authentication information into the cover image with flipping only a small number of pixels a special type of the pixels are selected and flipped by a new algorithm to minimize visual distortion this new algorithm is based on elssm edge line segment similarity measure randomly shuffling the bit order of the authentication information to be embedded the information can only be extracted by the designated receiver who has the symmetric key we employ two measurement metrics miss detection rates for the degree of security and psnr peak signal to noise ratio and elssm for the degree of the image distortion to demonstrate the feasibility of the proposed scheme using these metrics we analyze the proposed scheme and the previous schemes the analysis reveals that the proposed scheme requires less image distortion than the previous schemes whilst achieving the same level of the miss detection rate experimental results demonstrate that the proposed scheme is more resilient against recent steganalysis attacks than the previous schemes

optimal signature design for spread spectrum steganography for any given host image or group of host images and any block transform domain of interest we find the signature vector that when used for spread spectrum ss message embedding maximizes the signal to interference plus noise ratio sinr at the output of the corresponding maximum sinr linear filter we establish that under a colored gaussian assumption on the transform domain host data the same derived signature minimizes host distortion for any target message recovery error rate and maximizes the shannon capacity of the covert steganographic link then we derive jointly optimal signature and linear processor designs for ss embedding in linearly modified transform domain host data and demonstrate orders of magnitude improvement over current ss steganographic practices optimized multisignature multimessage embedding in the same host data is studied as well

perceptually adaptive spread transform image watermarking scheme using hadamard transform the present paper proposes a digital image watermarking scheme using the characteristics of the human visual system hvs spread transform technique and statistical information measure spread transform st scheme is implemented using the transform coefficients of both the host and the watermark signal watermark embedding strength is adaptively adjusted using frequency sensitivity luminance contrast and entropy masking of hvs model the choice of hadamard transform as watermark embedding domain offers several advantages such as low loss in image information higher image fidelity greater reliability of watermark detection and higher data hiding capacity at high degree of compression performance of the proposed method is compared with a number of recently reported watermarking schemes based on spread spectrum ss and quantization index modulation qim

polygonal approximation of digital planar curves through vertex betweenness contour polygonal approximation is usually defined as a set of selected points which describes a polygon and best represents the original contour this paper presents a novel graph based approach to compute a polygonal approximation of a shape contour in a graph such points correspond to a high transitivity region of the graph we use the vertex betweenness to measure the importance of each vertice in a graph according to the number of shortest paths where each vertice occurs by selecting the vertices with higher vertex betweenness a polygon which retains the main characteristics of the contour is achieved by using benchmark curves a comparative experiment with other commonly used algorithms is presented results indicate that the proposed approach produced efficient and effective polygonal approximations for digital planar curves

robust redesign of adaptive control effects of unmodeled high frequency dynamics on stability and performance of adaptive control schemes are analyzed in the regulation problem global stability properties are no longer guaranteed but a region of attraction exists for exact adaptive regulation the dependence of the region of attraction on unmodeled parasitics is examined first then the general case of model reference adaptive control is considered in which parasitics can destroy stability and boundedness properties a modified adaptive law is proposed guaranteeing the existence of a region of attraction from which all signals converge to a residual set which contains the equilibrium for exact tracking the size of this set depends on design parameters the frequency range of parasitics and the reference input signal characteristics

transcale control for a class of discrete stochastic systems based on wavelet packet decomposition abstract in this paper a wavelet packet decomposition wpd based real time transcale linear quadratic gaussian lqg tracking control algorithm is given for a class of discrete stochastic systems which is developed with the state space models of wavelet packet coefficients at the coarsest scale decomposition layer established by haar wpd the wpd based transcale control algorithm provides a compromise between performance index and computational efficiency compared with the conventional lqg tracking control algorithm and it is an improvement of the wavelet transform based algorithm previously proposed simulation results are presented to demonstrate the effectiveness of the proposed algorithm

video watermarking with empirical pca based decoding a new method for video watermarking is presented in this paper in the proposed method data are embedded in the ll subband of wavelet coefficients and decoding is performed based on the comparison among the elements of the first principal component resulting from empirical principal component analysis pca the locations for data embedding are selected such that they offer the most robust pca based decoding data are inserted in the ll subband in an adaptive manner based on the energy of high frequency subbands and visual saliency extensive testing was performed under various types of attacks such as spatial attacks uniform and gaussian noise and median filtering compression attacks mpeg 2 h 263 and h 264 and temporal attacks frame repetition frame averaging frame swapping and frame rate conversion the results show that the proposed method offers improved performance compared with several methods from the literature especially under additive noise and compression attacks

fractional krawtchouk transform with an application to image watermarking this paper proposes a novel fractional transform denoted as the fractional krawtchouk transform frkt a generalization of the krawtchouk transform the derivation of the frkt uses the eigenvalue decomposition method we determine the eigenvalues and the corresponding multiplicity of the krawtchouk transform matrix moreover the orthonormal eigenvectors of the transform matrix are derived for validation purpose only and as a first illustration of the interest of frkt a watermarking example was chosen experimental results show that better watermark robustness and imperceptibility are achieved by adjusting the fractional orders in the frkt

review of shape representation and description techniques more and more images have been generated in digital form around the world there is a growing interest in 1nding images in large collections or from remote databases in order to 1nd an image the image has to be described or represented by certain features shape is an important visual feature of an image searching for images using shape features has attracted much attention there are many shape representation anddescription techniques in the literature in this paper we classify and review these important techniques we examine implementation procedures for each technique and discuss its advantages and disadvantages some recent research results are also included and discussed in this paper finally we identify some promising techniques for image retrieval according to standard principles

additive watermark detection in the wavelet domain using 2d garch model watermark detection can be generally achieved by statistical decision methods so selection of an accurate statistical model is one of the major issues in watermark detection usually watermark detectors assume the wavelet coefficients to be independent and identically distributed therefore they cannot take into account important characteristics of the wavelet coefficients such as dependency and heteroscedasticity this paper presents a novel detector for wavelet domain additive image watermarking we use two dimensional generalized autoregressive conditional heteroscedasticity 2d garch model for the wavelet coefficients this model can capture the dependency heteroscedasticity and heavy tailed marginal distribution of these coefficients based on 2d garch model we design a new watermark detector and derive its receiver operating characteristics experimental results demonstrate the efficiency of proposed detector and its higher performance compared to alternative watermarking methods in the literature

shape retrieval based on dynamic programming we propose a shape matching algorithm for deformed shapes based on dynamic programming our algorithm is capable of grouping together segments at finer scales in order to come up with appropriate correspondences with segments at coarser scales we illustrate the effectiveness of our algorithm in retrieval of shapes by content on two different two dimensional 2 d datasets one of static hand gesture shapes and another of marine life shapes we also demonstrate the superiority of our approach over traditional approaches to shape matching and retrieval such as fourier descriptors and geometric and sequential moments our evaluation is based on human relevance judgments following a well established methodology from the information retrieval field

wavelet based copyright protection scheme for digital images based on local features a lossless robust copyright protection scheme for digital images based on cryptography and watermarking is proposed in the proposed wavelet based scheme the logo image is not embedded into the protected image the secret key is generated during the embedding process using local features extracted from the perceptually prominent components of the host image after the digital wavelet transform digital signature and time stamping technologies are then used for further protection with the secret key the logo image extraction process can be completed without the host image experimental results show that the proposed scheme is effective and robust against common image processing and geometric distortions

rotation invariant watermark embedding based on scale adapted characteristic regions rotation invariance is one of the most challenging issues in robust image watermarking this paper presents two rotation invariant watermark embedding schemes in the non subsampled contourlet transform nsct domain based on the scale adapted local regions watermark synchronization is achieved using the scale space feature point based local characteristic regions the first method embeds a binary watermark sequence by partitioning the local regions in a rotation invariant pattern the second method embeds a binary watermark image in a content based manner and the watermark signal is adaptive to the orientation of the image both methods achieve rotation invariant embedding by only using the rotation normalizing angle thus no interpolation operation is performed extensive simulation results and comparisons show that the proposed schemes can efficiently resist both signal processing attacks and geometric attacks

an artificial bee colony algorithm for the maximally diverse grouping problem in this paper an artificial bee colony algorithm is proposed to solve the maximally diverse grouping problem this complex optimisation problem consists of forming maximally diverse groups with restricted sizes from a given set of elements the artificial bee colony algorithm is a new swarm intelligence technique based on the intelligent foraging behaviour of honeybees the behaviour of this algorithm is determined by two search strategies an initialisation scheme employed to construct initial solutions and a method for generating neighbouring solutions more specifically the proposed approach employs a greedy constructive method to accomplish the initialisation task and also employs different neighbourhood operators inspired by the iterated greedy algorithm in addition it incorporates an improvement procedure to enhance the intensification capability through an analysis of the experimental results the highly effective performance of the proposed algorithm is shown in comparison to the current state of the art algorithms which address the problem

a study of multiplicative watermark detection in the contourlet domain using alpha stable distributions in the past decade several schemes for digital image watermarking have been proposed to protect the copyright of an image document or to provide proof of ownership in some identifiable fashion this paper proposes a novel multiplicative watermarking scheme in the contourlet domain the effectiveness of a watermark detector depends highly on the modeling of the transform domain coefficients in view of this we first investigate the modeling of the contourlet coefficients by the alpha stable distributions it is shown that the univariate alpha stable distribution fits the empirical data more accurately than the formerly used distributions such as the generalized gaussian and laplacian do we also show that the bivariate alpha stable distribution can capture the across scale dependencies of the contourlet coefficients motivated by the modeling results a blind watermark detector in the contourlet domain is designed by using the univariate and bivariate alpha stable distributions it is shown that the detectors based on both of these distributions provide higher detection rates than that based on the generalized gaussian distribution does however a watermark detector designed based on the alpha stable distribution with a value of its parameter other than 1 or 2 is computationally expensive because of the lack of a closed form expression for the distribution in this case therefore a watermark detector is designed based on the bivariate cauchy member of the alpha stable family for which 1 the resulting design yields a significantly reduced complexity detector and provides a performance that is much superior to that of the gg detector and very close to that of the detector corresponding to the best fit alpha stable distribution the robustness of the proposed bivariate cauchy detector against various kinds of attacks such as noise filtering and compression is studied and shown to be superior to that of the generalized gaussian detector

lightweight detection of additive watermarking in the dwt domain this article aims at lightweight blind detection of additive spread spectrum watermarks in the dwt domain we focus on two host signal noise models and two types of hypothesis tests for watermark detection as a crucial point of our work we take a closer look at the computational requirements of watermark detectors this involves the computation of the detection response parameter estimation and threshold selection we show that by switching to approximate host signal parameter estimates or even fixed parameter settings we achieve a remarkable improvement in runtime performance without sacrificing detection performance our experimental results on a large number of images confirm the assumption that there is not necessarily a tradeoff between computation time and detection performance

an image watermarking scheme in wavelet domain with optimized compensation of singular value decomposition via artificial bee colony digital image watermarking is the process of authenticating a digital image by embedding a watermark into it and thereby protecting the image from copyright infringement this paper proposes a novel robust image watermarking scheme developed in the wavelet domain based on the singular value decomposition svd and artificial bee colony abc algorithm the host image is transformed into an invariant wavelet domain by applying redistributed invariant wavelet transform subsequently the low frequency sub band of wavelet transformed image is segmented into non overlapping blocks the most suitable embedding blocks are selected using the human visual system for the watermark embedding the watermark bits are embedded into the target blocks by modifying the first column coefficients of the left singular vector matrix of svd decomposition with the help of a threshold and the visible distortion caused by the embedding is compensated by modifying the coefficients of the right singular vector matrix employing compensation parameters furthermore abc is employed to obtain the optimized threshold and compensation parameters experimental results compared with the related existing schemes demonstrated that the proposed scheme not only possesses the strong robustness against image manipulation attacks but also is comparable to other schemes in term of visual quality

improved spread spectrum a new modulation technique for robust watermarking this paper introduces a new watermarking modulation technique which we call improved spread spectrum iss when compared with traditional spread spectrum ss the signal does not act as a noise source leading to significant gains in some examples performance improvements over ss are 20 db in signal to noise ratio snr or ten or more orders of magnitude in the error probability the proposed method achieves roughly the same noise robustness gain as quantization index modulation qim but without the amplitude scale sensitivity of qim our proposed iss is as robust in practice as traditional ss

image clustering using local discriminant models and global integration in this paper we propose a new image clustering algorithm referred to as clustering using local discriminant models and global integration ldmgi to deal with the data points sampled from a nonlinear manifold for each data point we construct a local clique comprising this data point and its neighboring data points inspired by the fisher criterion we use a local discriminant model for each local clique to evaluate the clustering performance of samples within the local clique to obtain the clustering result we further propose a unified objective function to globally integrate the local models of all the local cliques with the unified objective function spectral relaxation and spectral rotation are used to obtain the binary cluster indicator matrix for all the samples we show that ldmgi shares a similar objective function with the spectral clustering sc algorithms e g normalized cut ncut in contrast to ncut in which the laplacian matrix is directly calculated based upon a gaussian function a new laplacian matrix is learnt in ldmgi by exploiting both manifold structure and local discriminant information we also prove that k means and discriminative k means diskmeans are both special cases of ldmgi extensive experiments on several benchmark image datasets demonstrate the effectiveness of ldmgi we observe in the experiments that ldmgi is more robust to algorithmic parameter when compared with ncut thus ldmgi is more appealing for the real image clustering applications in which the ground truth is generally not available for tuning algorithmic parameters

on hidden markov models and cyclic strings for shape recognition abstract shape descriptions and the corresponding matching techniques must be robust to noise and invariant to transformations for their use in recognition tasks most transformations are relatively easy to handle when contours are represented by strings however starting point invariance is difficult to achieve one interesting possibility is the use of cyclic strings which are strings that have no starting and final points we propose new methodologies to use hidden markov models to classify contours represented by cyclic strings experimental results show that our proposals outperform other methods in the literature

capacity estimates for data hiding in compressed images we present an information theoretic approach to obtain an estimate of the number of bits that can be hidden in still images or the capacity of the data hiding channel we show how the addition of the message signal or signature in a suitable transform domain rather than the spatial domain can significantly increase the channel capacity most of the state of the art schemes developed thus far for data hiding have embedded bits in some transform domain as it has always been implicitly understood that a decomposition would help though most methods reported in the literature use dct or wavelet decomposition for data embedding the choice of the transform is not obvious we compare the achievable data hiding capacities for different decompositions like dct dft hadamard and subband transforms and show that the magnitude dft decomposition performs best among the ones compared

wavelet thresholding via mdl for natural images we study the application of rissanen s 1989 principle of minimum description length mdl to the problem of wavelet denoising and compression for natural images after making a connection between thresholding and model selection we derive an mdl criterion based on a laplacian model for noiseless wavelet coefficients we find that this approach leads to an adaptive thresholding rule while achieving mean squared error performance comparable with other popular thresholding schemes the mdl procedure tends to keep far fewer coefficients from this property we demonstrate that our method is an excellent tool for simultaneous denoising and compression we make this claim precise by analyzing mdl thresholding in two optimality frameworks one in which we measure rate and distortion based on quantized coefficients and one in which we do not quantize but instead record rate simply as the number of nonzero coefficients

multi aspect target discrimination using hidden markov models and neural networks this paper presents a new multi aspect pattern classification method using hidden markov models hmms models are defined for each class with the probability found by each model determining class membership each hmm model is enhanced by the use of a multilayer perception mlp network to generate emission probabilities this hybrid system uses the mlp to find the probability of a state for an unknown pattern and the hmm to model the process underlying the state transitions a new batch gradient descent based method is introduced for optimal estimation of the transition and emission probabilities a prediction method in conjunction with hmm model is also presented that attempts to improve the computation of transition probabilities by using the previous states to predict the next state this method exploits the correlation information between consecutive aspects these algorithms are then implemented and benchmarked on a multi aspect underwater target classification problem using a realistic sonar data set collected in different bottom conditions

the upper and lower bounds of the information hiding capacity of digital images the information hiding capacity of a digital image is the maximum information that can be hidden in that image while the lower limit of information hiding is the minimum detectable information capacity this paper proposes a new method of information hiding capacity bounds analysis that is based on the neural network theories of attractors and attraction basins with this method the processes for determining upper and lower limits of information hiding are unified within a single theoretical framework results of research show that the theory of attraction basins of neural networks can be used to determine the upper limit of information hiding and the theory of attractors of neural networks can be used to determine the lower limit of information hiding

a new adaptive law for robust adaptation without persistent excitation a new adaptive law motivated by the work of loannou and kokotovic 1983 is proposed for the robust adaptive control of plants with unknown parameters in this adaptive law the output error plays a dual role in the adjustment of the control parameter vector the advantages of using the adaptive law over others proposed in the literature are discussed in the ideal case the adaptive system has bounded solutions in addition the origin of the error equations is exponentially stable when the reference input is persistently exciting and has a sufficiently large amplitude the adaptive system is also shown to be robust under bounded external disturbances finally it is shown that by suitably modifying the adaptive law the overall system can be made robust in the presence of a class of unmodeled dynamics of the plant simulation results are presented throughout the paper to complement the theoretical developments

blind optimum detector for robust image watermarking in nonsubsampled shearlet domain in this paper we propose a blind robust image watermarking approach in the nonsubsampled shearlet domain using bessel k form bkf modeling nonsubsampled shearlet transform nsst is an effective multi scale and multi direction analysis method it not only can exactly compute the shearlet coefficients based on a multiresolution analysis but also can provide nearly optimal approximation for a piecewise smooth function in order to achieve more robustness and imperceptibility watermark information is embedded into the most significant nsst directional subband with the highest energy by simply modifying the nsst coefficient amplitude according to the watermark data by modeling the nsst coefficients with bkf probability density function the distribution of watermarked noisy coefficients is analytically calculated the tradeoff between the imperceptibility and robustness of watermark data is solved in a novel fashion at the receiver based on the maximum likelihood ml decision rule a blind statistical watermark detector is proposed experimental results on test images demonstrate that the proposed approach can provide better imperceptibility and robustness against various attacks such as additive white gaussian noise salt pepper noise median filtering jpeg compression rotation and scaling in comparison with the recently proposed techniques

an optimal image watermarking approach based on a multi objective genetic algorithm in accordance with the multi objective nature of image watermarking an optimal image watermarking approach using a multi objective genetic algorithm is presented in this paper both watermarking parameters and embedding positions are often important factors affecting the performance of watermarking systems the proposed multi objective watermarking method can automatically optimize system parameters and a variable length mechanism is specially designed to search the most suitable positions for embedding watermarks the method can also remove the difficult issue of determining optimal watermarking parameters from previous watermarking algorithms the proposed multi objective watermarking method directly deals with the problem of optimizing watermarking under non dominated meaning thus it can effectively avoid the difficulty of determining the optimally weighted factor in existing single objective watermarking schemes in addition a pareto optimal set generated by multi objective optimization can provide flexibility in selecting the most suitable watermarking parameters according to practical requirements

sparsifying the fisher linear discriminant by rotation many high dimensional classification techniques have been proposed in the literature based on sparse linear discriminant analysis lda to efficiently use them sparsity of linear classifiers is a prerequisite however this might not be readily available in many applications and rotations of data are required to create the needed sparsity in this paper we propose a family of rotations to create the required sparsity the basic idea is to use the principal components of the sample covariance matrix of the pooled samples and its variants to rotate the data first and to then apply an existing high dimensional classifier this rotate and solve procedure can be combined with any existing classifiers and is robust against the sparsity level of the true model we show that these rotations do create the sparsity needed for high dimensional classifications and provide theoretical understanding why such a rotation works empirically the effectiveness of the proposed method is demonstrated by a number of simulated and real data examples and the improvements of our method over some popular high dimensional classification rules are clearly shown

reversible hiding in dct based compressed images this paper presents a lossless and reversible steganography scheme for hiding secret data in each block of quantized discrete cosine transformation dct coefficients in jpeg images in this scheme the two successive zero coefficients of the medium frequency components in each block are used to hide the secret data furthermore the scheme modifies the quantization table to maintain the quality of the stego image experimental results also confirm that the proposed scheme can provide expected acceptable image quality of stego images and successfully achieve reversibility

nonparametric joint shape and feature priors for image segmentation in many image segmentation problems involving limited and low quality data employing statistical prior information about the shapes of the objects to be segmented can significantly improve the segmentation result however defining probability densities in the space of shapes is an open and challenging problem especially if the object to be segmented comes from a shape density involving multiple modes classes existing techniques in the literature estimate the underlying shape distribution by extending parzen density estimator to the space of shapes in these methods the evolving curve may converge to a shape from a wrong mode of the posterior density when the observed intensities provide very little information about the object boundaries in such scenarios employing both shape and class dependent discriminative feature priors can aid the segmentation process such features may involve e g intensity based textural or geometric information about the objects to be segmented in this paper we propose a segmentation algorithm that uses nonparametric joint shape and feature priors constructed by parzen density estimation we incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation the resulting optimization problem is solved using active contours we present experimental results on a variety of synthetic and real data sets from several fields involving multimodal shape densities experimental results demonstrate the potential of the proposed method

predicting monthly streamflow using data driven models coupled with data preprocessing techniques 1 in this paper the accuracy performance of monthly streamflow forecasts is discussed when using data driven modeling techniques on the streamflow series a crisp distributed support vectors regression cdsvr model was proposed for monthly streamflow prediction in comparison with four other models autoregressive moving average arma k nearest neighbors knn artificial neural networks anns and crisp distributed artificial neural networks cdann with respect to distributed models of cdsvr and cdann the fuzzy c means fcm clustering technique first split the flow data into three subsets low medium and high levels according to the magnitudes of the data and then three single svrs or anns were fitted to three subsets this paper gives a detailed analysis on reconstruction of dynamics that was used to identify the configuration of all models except for arma to improve the model performance the data preprocessing techniques of singular spectrum analysis ssa and or moving average ma were coupled with all five models some discussions were presented 1 on the number of neighbors in knn 2 on the configuration of ann and 3 on the investigation of effects of ma and ssa two streamflow series from different locations in china xiangjiaba and danjiangkou were applied for the analysis of forecasting forecasts were conducted at four different horizons 1 3 6 and 1 2 month ahead forecasts the results showed that models fed by preprocessed data performed better than models fed by original data and cdsvr outperformed other models except for at a 6 month ahead horizon for danjiangkou for the perspective of streamflow series the ssa exhibited better effects on danjingkou data because its raw discharge series was more complex than the discharge of xiangjiaba the ma considerably improved the performance of ann cdann and cdsvr by adjusting the correlation relationship between input components and output of models it was also found that the performance of cdsvr deteriorated with the increase of the forecast horizon

image cryptographic algorithm based on the haar wavelet transform lossless encryption methods are more applicable than lossy encryption methods when marginal distortion is not tolerable in this research the authors propose a novel lossless symmetric key encryption decryption technique in the proposed algorithm the image is transformed into the frequency domain using the haar wavelet transform then the image sub bands are encrypted in a such way that guarantees a secure reliable and an unbreakable form the encryption involves scattering the distinguishable frequency data in the image using a reversible weighting factor amongst the rest of the frequencies the algorithm is designed to shuffle and reverse the sign of each frequency in the transformed image before the image frequencies are transformed back to the pixel domain the results show a total deviation in pixel values between the original and encrypted image the decryption algorithm reverses the encryption process and restores the image to its original form the proposed algorithm is evaluated using standard security and statistical methods results show that the proposed work is resistant to most known attacks and more secure than other algorithms in the cryptography domain

writing on dirty paper corresp a channel with output y x s z is examined the state s sim n 0 qi and the noise z sim n 0 ni are multivariate gaussian random variables i is the identity matrix the input x in r n satisfies the power constraint l n sum i 1 n x i 2 leq p if s is unknown to both transmitter and receiver then the capacity is frac 1 2 ln 1 p n q nats per channel use however if the state s is known to the encoder the capacity is shown to be c ast frac 1 2 ln 1 p n independent of q this is also the capacity of a standard gaussian channel with signal to noise power ratio p n therefore the state s does not affect the capacity of the channel even though s is unknown to the receiver it is shown that the optimal transmitter adapts its signal to the state s rather than attempting to cancel it

a logarithmic quantization index modulation for perceptually better data hiding in this paper a novel arrangement for quantizer levels in the quantization index modulation qim method is proposed due to perceptual advantages of logarithmic quantization and in order to solve the problems of a previous logarithmic quantization based method we used the compression function of law standard for quantization in this regard the host signal is first transformed into the logarithmic domain using the law compression function then the transformed data is quantized uniformly and the result is transformed back to the original domain using the inverse function the scalar method is then extended to vector quantization for this the magnitude of each host vector is quantized on the surface of hyperspheres which follow logarithmic radii optimum parameter for both scalar and vector cases is calculated according to the host signal distribution moreover inclusion of a secret key in the proposed method similar to the dither modulation in qim is introduced performance of the proposed method in both cases is analyzed and the analytical derivations are verified through extensive simulations on artificial signals the method is also simulated on real images and its performance is compared with previous scalar and vector quantization based methods results show that this method features stronger a watermark in comparison with conventional qim and as a result has better performance while it does not suffer from the drawbacks of a previously proposed logarithmic quantization algorithm

perceptual image hashing based on virtual watermark detection this paper proposes a new robust and secure perceptual image hashing technique based on virtual watermark detection the idea is justified by the fact that the watermark detector responds similarly to perceptually close images using a non embedded watermark the hash values are extracted in binary form with a perfect control over the probability distribution of the hash bits moreover a key is used to generate pseudo random noise whose real values contribute to the randomness of the feature vector with a significantly increased uncertainty of the adversary measured by mutual information in comparison with linear correlation experimentally the proposed technique has been shown to outperform related state of the art techniques recently proposed in the literature in terms of robustness with respect to image processing manipulations and geometric attacks

intelligent reversible watermarking and authentication hiding depth map information for 3d cameras in this paper we propose an application for 3d cameras by reversibly hiding the depth map in the corresponding 2d images the proposed technique is prospective in cameras capable of capturing simultaneously the 2d image and resultant depth map of an object 3d cameras equipped with self embedding capability can serve two additional purposes protection of the captured image and secure transmission of its depth map the reversible watermarking in addition to other features guarantees the lossless recovery of an original image and separation of its depth map when required for this purpose a reversible watermarking scheme based on genetic algorithm ga has been proposed which computes suitable threshold for each block of coefficients in the wavelet domain thus a tradeoff is made between watermark imperceptibility and capacity using ga the threshold map is embedded in addition to the actual payload and thus the proposed approach does not require histogram pre processing in order to avoid overflow underflow the same threshold map has been used for authentication purpose by correlating it with the low frequency coefficients of the 2d transformed image further to exploit the inherent redundancy in the depth map which is the actual payload in this case lossless compression has been employed before its embedding similarly besides secret key based permutation a cryptographic layer is overlaid on the watermarking layer for security purposes experiments conducted on images and depth maps obtained using a 3d camera and an optical microscopic system validate the proposed concept

watermarking and authentication of quantum images based on restricted geometric transformations a secure keyless and blind watermarking and authentication strategy for images on quantum computers waqi is proposed based on restricted geometric transformations in contrast with conventional digital watermarking techniques where geometric transformations on the contents of an image are considered undesirable the proposed waqi scheme utilises the restricted variants of the quantum versions of these transformations as the main resources of the watermark embedding and authentication circuits this is accomplished by a careful analysis of the classical content of the image watermark pair based on which a bespoke watermark map that translates into the gate sequences of the quantum watermark embedding and authentication circuits is realised simulation based experimental results involving the classical i e conventional or non quantum simulation of the input images watermark signals and quantum circuits yielded a 25 improvement in terms of overall watermark embedding capacity and between 7 and 50 7 in terms of the visible quality of the watermarked images in comparison with select digital watermarking methods for various pairs thus demonstrating both the feasibility and capabilities of the proposed waqi scheme when the necessary quantum hardware are realised physically this scheme provides the framework for representing two or more quantum data as a single quantum circuit and opens the door for other applications aimed at quantum data protection

adaptive multiwavelet based watermarking through jpw masking in this paper a multibit multiplicative spread spectrum watermarking using the discrete multiwavelet including unbalanced and balanced multiwavelet transform is presented performance improvement with respect to existing algorithm is obtained by means of a new just perceptual weighting jpw model the new model incorporates various masking effects of human visual perception by taking into account the eye s sensitivity to noise changes depending on spatial frequency luminance and texture of all the image subbands in contrast to conventional jnd threshold model jpw describing minimum perceptual sensitivity weighting to noise changes is fitter for nonadditive watermarking specifically watermarking strength is adaptively adjusted to obtain minimum perceptual distortion by employing the jpw model correspondingly an adaptive optimum decoding is derived using a statistic model based on generalized gaussian distribution ggd for multiwavelet coefficients of the cover image furthermore the impact of multiwavelet characteristics on proposed watermarking scheme is also analyzed finally the experimental results show that proposed jpw model can improve the quality of the watermarked image and give more robustness of the watermark as compared with a variety of state of the art algorithms

a new reliable optimized image watermarking scheme based on the integer wavelet transform and singular value decomposition for copyright protection although image watermarking schemes based on singular value decomposition svd demonstrate high robustness and imperceptibility they are exposed to the false positive problem fpp this drawback mostly occurs when embedding steps depend on singular values while singular vectors are used as secret keys in this study a new reliable svd based image watermarking scheme that uses integer wavelet transform iwt is proposed to overcome fpp and fulfil all watermarking requirements unlike in other schemes the s and v matrices of the watermark are used as secret keys whereas the s singular vector of the watermark is embedded into the singular values of the host image the additional secret key is obtained from the watermarked image during the embedding process to increase security and avoid fpp completely to improve the robustness as well as achieve balance between robustness and imperceptibility multi objective ant colony optimization moaco is utilized to find the optimal scaling factors namely multiple zooming factors results of the robustness imperceptibility and reliability tests demonstrate that the proposed iwt svd moaco scheme outperforms several previous schemes and avoids fpp completely

a new approach for optimization in image watermarking by using genetic algorithms in this paper the authors propose the spread spectrum image watermarking algorithm using the discrete multiwavelet transform performance improvement with respect to existing algorithms is obtained by genetic algorithms optimization in the proposed optimization process the authors search for parameters that consist of threshold values and the embedding strength to improve the visual quality of watermarked images and the robustness of the watermark these parameters are varied to find the most suitable for images with different characteristics the experimental results show that the proposed algorithm yields a watermark that is invisible to human eyes and robust to various image manipulations the authors also compare their experimental results with the results of previous work using various test images

flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes curve matching is one instance of the fundamental correspondence problem our flexible algorithm is designed to match curves under substantial deformations and arbitrary large scaling and rigid transformations a syntactic representation is constructed for both curves and an edit transformation which maps one curve to the other is found using dynamic programming we present extensive experiments where we apply the algorithm to silhouette matching in these experiments we examine partial occlusion viewpoint variation articulation and class matching where silhouettes of similar objects are matched based on the qualitative syntactic matching we define a dissimilarity measure and we compute it for every pair of images in a database of 121 images we use this experiment to objectively evaluate our algorithm first we compare our results to those reported by others second we use the dissimilarity values in order to organize the image database into shape categories the veridical hierarchical organization stands as evidence to the quality of our matching and similarity estimation

hidden markov model based weighted likelihood discriminant for 2 d shape classification the goal of this paper is to present a weighted likelihood discriminant for minimum error shape classification different from traditional maximum likelihood ml methods in which classification is based on probabilities from independent individual class models as is the case for general hidden markov model hmm methods proposed method utilizes information from all classes to minimize classification error the proposed approach uses a hmm for shape curvature as its 2 d shape descriptor we introduce a weighted likelihood discriminant function and present a minimum classification error strategy based on generalized probabilistic descent method we show comparative results obtained with our approach and classic ml classification with various hmm topologies alongside fourier descriptor and zernike moments based support vector machine classification for a variety of shapes

shape similarity measure based on correspondence of visual parts a cognitively motivated similarity measure is presented and its properties are analyzed with respect to retrieval of similar objects in image databases of silhouettes of 2d objects to reduce influence of digitization noise as well as segmentation errors the shapes are simplified by a novel process of digital curve evolution to compute our similarity measure we first establish the best possible correspondence of visual parts without explicitly computing the visual parts then the similarity between corresponding parts is computed and aggregated we applied our similarity measure to shape matching of object contours in various image databases and compared it to well known approaches in the literature the experimental results justify that our shape matching procedure gives an intuitive shape correspondence and is stable with respect to noise distortions

image compression via joint statistical characterization in the wavelet domain we develop a probability model for natural images based on empirical observation of their statistics in the wavelet transform domain pairs of wavelet coefficients corresponding to basis functions at adjacent spatial locations orientations and scales are found to be non gaussian in both their marginal and joint statistical properties specifically their marginals are heavy tailed and although they are typically decorrelated their magnitudes are highly correlated we propose a markov model that explains these dependencies using a linear predictor for magnitude coupled with both multiplicative and additive uncertainties and show that it accounts for the statistics of a wide variety of images including photographic images graphical images and medical images in order to directly demonstrate the power of the model we construct an image coder called epwic embedded predictive wavelet image coder in which subband coefficients are encoded one bitplane at a time using a nonadaptive arithmetic encoder that utilizes conditional probabilities calculated from the model bitplanes are ordered using a greedy algorithm that considers the mse reduction per encoded bit the decoder uses the statistical model to predict coefficient values based on the bits it has received despite the simplicity of the model the rate distortion performance of the coder is roughly comparable to the best image coders in the literature

adaptive embedding techniques for vq compressed images an embedding algorithm which can adaptively embed a binary message into a vq compressed image is proposed in this paper the proposed algorithm is divided into three phases in the codeword grouping phase a new group of codewords is initiated by the two most similar codewords which do not belong to any group for each codeword which does not belong to any group if the codeword is similar to all of the codewords in the group it will be added to the group in the embedding phase each codeword in a group will be assigned to embed a certain sub message whose length is determined by the number of codewords in the group the more codewords a group has the higher the embedding capacity of a codeword in the group will be in the extracting phase given a codeword and the number of codewords in the group to which the codeword belongs the embedded message can be extracted from the codeword by simply determining the order of the codeword in the group experimental results show that the proposed algorithm performs better than previous algorithms with regard to embedding capacity and image quality for the test images when the embedding capacity is less than 5bits per codeword index the difference of the psnr values between the stego image and its vq compressed cover image will be no more than 5db on average

deformation based curved shape representation in this paper we introduce a deformation based representation space for curved shapes in mathbb r n given an ordered set of points sampled from a curved shape the proposed method represents the set as an element of a finite dimensional matrix lie group variation due to scale and location are filtered in a preprocessing stage while shapes that vary only in rotation are identified by an equivalence relationship the use of a finite dimensional matrix lie group leads to a similarity metric with an explicit geodesic solution subsequently we discuss some of the properties of the metric and its relationship with a deformation by least action furthermore invariance to reparametrization or estimation of point correspondence between shapes is formulated as an estimation of sampling function thereafter two possible approaches are presented to solve the point correspondence estimation problem finally we propose an adaptation of k means clustering for shape analysis in the proposed representation space experimental results show that the proposed representation is robust to uninformative cues e g local shape perturbation and displacement in comparison to state of the art methods it achieves a high precision on the swedish and the flavia leaf datasets and a comparable result on mpeg 7 kimia99 and kimia216 datasets

on distortion compensated dither modulation data hiding with repetition coding an exhaustive analysis of the distortion compensated dither modulation dc dm data hiding method with repetition coding is presented two decoding strategies maximum likelihood lattice decoding and euclidean distance decoding are discussed and some simplifications presented an exact performance analysis in terms of the bit error rate ber is given such an exact analysis is currently not available in the literature two methods for computing the exact ber and several approximations and bounds most of them in closed form are provided these approximations are employed to propose two novel improvements on the standard dc dm method with repetition the use of a weighted euclidean distance with optimizable weights and a vector form of the distortion compensation parameter both account for significant performance improvements dc dm is compared with quantization methods in the projected domain showing worse performance against additive noise attacks but higher robustness to cropping attacks a performance analysis of dc dm under coarse quantization that can be specialized to jpeg compression is also supplied all our results are validated with numerical simulations with both synthetic data and real images

contourlet based image watermarking using optimum detector in a noisy environment in this paper an improved multiplicative image watermarking system is presented since human visual system is less sensitive to the image edges watermarking is applied in the contourlet domain which represents image edges sparsely in the presented scheme watermark data is embedded in directional subband with the highest energy by modeling the contourlet coefficients with general gaussian distribution ggd the distribution of watermarked noisy coefficients is analytically calculated the tradeoff between the transparency and robustness of the watermark data is solved in a novel fashion at the receiver based on the maximum likelihood ml decision rule an optimal detector by the aid of channel side information is proposed in the next step a blind extension of the suggested algorithm is presented using the patchwork idea experimental results confirm the superiority of the proposed method against common attacks such as additive white gaussian noise awgn jpeg compression and rotation attacks in comparison with the recently proposed techniques