Block-wise Method Research Articles

A secret image sharing method based on block-wise cheating detection and recovery on shadow images

In (k,n) threshold schemes, it is very important to determine which shadow images are cheated on and which shadow images will be used in the recovery of the secret image. Also, some of the cheated shadow images may have not cheated regions. Therefore, authentication of the shadow image’s blocks is necessary. In this paper, a secret image sharing method using block-wise cheating detection and recovery is proposed. With the proposed block-wise method, even if all shadow images have been cheated, recovery of the secret images is performed with the block-based authentication and recovery mechanism. In the proposed recovery process, it is sufficient for k of each paired block group in the n shadow image to be not cheated. Experimental results illustrated that the proposed method successfully recovered the secret image even if n shadow images were cheated, and the randomness of the proposed method is satisfactory.

Investigating the Influences of Task Demand and Reward on Cardiac Pre-Ejection Period Reactivity During a Speech-in-Noise Task.

Objectives:Effort investment during listening varies as a function of task demand and motivation. Several studies have manipulated both these factors to elicit and measure changes in effort associated with listening. The cardiac pre-ejection period (PEP) is a relatively novel measure in the field of cognitive hearing science. This measure, which reflects sympathetic nervous system activity on the heart, has previously been implemented during a tone discrimination task but not during a speech-in-noise task. Therefore, the primary goal of this study was to explore the influences of signal to noise ratio (SNR) and monetary reward level on PEP reactivity during a speech-in-noise task.Design:Thirty-two participants with normal hearing (mean age = 22.22 years, SD = 3.03) were recruited at VU University Medical Center. Participants completed a Dutch speech-in-noise test with a single-interfering-talker masking noise. Six fixed SNRs, selected to span the entire psychometric performance curve, were presented in a block-wise fashion. Participants could earn a low (€0.20) or high (€5.00) reward by obtaining a score of ≥70% of words correct in each block. The authors analyzed PEP reactivity: the change in PEP measured during the task, relative to the baseline during rest. Two separate methods of PEP analysis were used, one including data from the whole task block and the other including data obtained during presentation of the target sentences only. After each block, participants rated their effort investment, performance, tendency to give up, and the perceived difficulty of the task. They also completed the need for recovery questionnaire and the reading span test, which are indices of additional factors (fatigue and working memory capacity, respectively) that are known to influence listening effort.Results:Average sentence perception scores ranged from 2.73 to 91.62%, revealing a significant effect of SNR. In addition, an improvement in performance was elicited by the high, compared to the low reward level. A linear relationship between SNR and PEP reactivity was demonstrated: at the lower SNRs PEP reactivity was the most negative, indicating greater effort investment compared to the higher SNRs. The target stimuli method of PEP analysis was more sensitive to this effect than the block-wise method. Contrary to expectations, no significant impact of reward on PEP reactivity was found in the present dataset. Also, there was no physiological evidence that participants were disengaged, even when performance was poor. A significant correlation between need for recovery scores and average PEP reactivity was demonstrated, indicating that a lower need for recovery was associated with less effort investment.Conclusions:This study successfully implemented the measurement of PEP during a standard speech-in-noise test and included two distinct methods of PEP analysis. The results revealed for the first time that PEP reactivity varies linearly with task demand during a speech-in-noise task, although the effect size was small. No effect of reward on PEP was demonstrated. Finally, participants with a higher need for recovery score invested more effort, as shown by average PEP reactivity, than those with a lower need for recovery score.

Phase-to-depth calibration in fringe projection profilometry based on blockwise surface fitting

A new classification method is proposed in this paper which categorizes the phase-to-depth calibration methods for fringe projection profilometry into pixel-dependent (P-D) ones and pixel-independent (P-I) ones. In P-D methods, depth is expressed as a function of the unwrapped phase, and parameters of the function are different from pixel to pixel. In P-I calibration methods, depth is expressed as a function of both phase and pixel coordinate, and there is only one set of parameters which applies to all pixels. P-D method is resistant to distortion of the projector or the camera but is time-consuming because a number of phase maps need to be recorded; P-I method requires only two or three phase maps but is sensitive to distortion. To address this problem, we further propose a blockwise calibration method. It requires the same number of phase maps as the P-I method and meanwhile can be as resistant to distortion as the P-D method. Simulation results show that when phase maps are contaminated by random additive noise, error produced by the proposed blockwise method is even less than that of the P-D method which uses 18 phase maps. In the experiment the proposed method and the P-D method were utilized to calibrate a fiber-optic fringe projection profilometer and then heights of a 4 mm and a 7 mm gauge block were measured. Difference between results of the two methods was less than the accuracy of the translation stage (0.01 mm), which verified the effectiveness of the proposed method.

Visual Navigation for Recovering an AUV by Another AUV in Shallow Water.

Autonomous underwater vehicles (AUVs) play very important roles in underwater missions. However, the reliability of the automated recovery of AUVs has still not been well addressed. We propose a vision-based framework for automatically recovering an AUV by another AUV in shallow water. The proposed framework contains a detection phase for the robust detection of underwater landmarks mounted on the docking station in shallow water and a pose-estimation phase for estimating the pose between AUVs and underwater landmarks. We propose a Laplacian-of-Gaussian-based coarse-to-fine blockwise (LCB) method for the detection of underwater landmarks to overcome ambient light and nonuniform spreading, which are the two main problems in shallow water. We propose a novel method for pose estimation in practical cases where landmarks are broken or covered by biofouling. In the experiments, we show that our proposed LCB method outperforms the state-of-the-art method in terms of remote landmark detection. We then combine our proposed vision-based framework with acoustic sensors in field experiments to demonstrate its effectiveness in the automated recovery of AUVs.

An Improved LBP Blockwise Method for Face Recognition

Face recognition is considered as one of toughest and most crucial leading domains of digital image processing. The human brain also uses a similar kind of technique for face recognition. When scrutinizing a face, the human brain signifies the result. Aside from AN automatic processing system, this technique is very sophisticated, owing to the image variations on account of the picture varieties in as far as area, size, articulation, and stance. In this article, the authors have used the options of native binary pattern and uniform native binary pattern for face recognition. They compute a number of classifiers on publicly available benchmarked ORL image databases to validate the proposed approach. The results clearly show that the proposed LBP-piece shrewd strategy has outperformed the traditional LBP system.

Particle Filtering Based Estimation of Consistent Motion and Disparity With Reduced Search Points

A particle filtering based block-wise estimation method for estimation of motion in a video sequence and joint estimation of disparity and motion in a stereo video sequence is proposed. Parameters of motion and disparity of a block in a sequence are defined as a state, and evolution of the state with respect to the block index is tracked with particle filtering. The state is assumed to be dependent on the states of neighboring blocks. Estimated motion and disparity fields are consistent and suitable for intermediate frame or view generation. The particle filter provides a method to effectively sample the search space. The particles are concentrated in regions where the probability density function for the state has large values. Hence, the locations of the particles are good candidates for search points of a fast search method. The proposed method can estimate motion and disparity with a fraction of search points necessary for conventional estimation methods.

Multi-grid Beam and Warming scheme for the simulation of unsteady flow in an open channel

In this paper, a multi-grid algorithm is applied to a large-scale block matrix that is produced from a Beam and Warming scheme. The Beam and Warming scheme is used in the simulation of unsteady flow in an open channel. The Gauss-Seidel block-wise iteration method is used for a smoothing process with a few iterations. It is also shown that the governing equations determine the type of prolongation and restriction operators for the multi-grid algorithm.

Exact combinatorial bounds on the probability of overfitting for empirical risk minimization

Three general methods for obtaining exact bounds on the probability of overfitting are proposed within statistical learning theory: a method of generating and destroying sets, a recurrent method, and a blockwise method. Six particular cases are considered to illustrate the application of these methods. These are the following model sets of predictors: a pair of predictors, a layer of a Boolean cube, an interval of a Boolean cube, a monotonic chain, a unimodal chain, and a unit neighborhood of the best predictor. For the interval and the unimodal chain, the results of numerical experiments are presented that demonstrate the effects of splitting and similarity on the probability of overfitting.

Enhancement of spatially adaptive smoothing splines via parameterization of smoothing parameters

This paper considers the problem of estimating curve and surface functions when the structures of an unknown function vary spatially. Classical approaches such as using smoothing splines, which are controlled by a single smoothing parameter, are inefficient in estimating the underlying function that consists of different spatial structures. In this paper, we propose a blockwise method of fitting smoothing splines wherein the smoothing parameter λ varies spatially, in order to accommodate possible spatial nonhomogeneity of the regression function. A key feature of the proposed blockwise method is the parameterization of a smoothing parameter function λ ( x ) that produces a continuous spatially adaptive fit over the entire range of design points. The proposed parameterization requires two important ingredients: (1) a blocking scheme that divides the data into several blocks according to the degree of spatial variation of the data; and (2) a method for choosing smoothing parameters of blocks. We propose a block selection approach that is based on the adaptive thinning algorithm and a choice of smoothing parameters that minimize a newly defined blockwise risk. The results obtained from numerical experiments validate the effectiveness of the proposed method.

Sharp adaptive estimation by a blockwise method

We consider a blockwise James–Stein estimator for nonparametric function estimation in suitable wavelet or Fourier bases. The estimator can be readily explained and implemented. We show that the estimator is asymptotically sharp adaptive in minimax risk over any Sobolev ball containing the true function. Further, for a moderately broad range of bounded sets in the Besov space our estimator is asymptotically nearly sharp adaptive in the sense that it comes within the Donoho–Liu constant, 1.24, of being exactly sharp adaptive. Other parameter spaces are also considered. The paper concludes with a Monte-Carlo study comparing the performance of our estimator with that of three other popular wavelet estimators. Our procedure generally (but not always) outperforms two of these and is overall comparable, or perhaps slightly superior, with the third.

Empirical likelihood confidence intervals for the mean of a long‐range dependent process

Abstract. This paper considers blockwise empirical likelihood for real‐valued linear time processes which may exhibit either short‐ or long‐range dependence. Empirical likelihood approaches intended for weakly dependent time series can fail in the presence of strong dependence. However, a modified blockwise method is proposed for confidence interval estimation of the process mean, which is valid for various dependence structures including long‐range dependence. The finite‐sample performance of the method is evaluated through a simulation study and compared with other confidence interval procedures involving subsampling or normal approximations.

Inégalités d'oracle pour l'estimation d'une densité de probabilité

We study the problem of the nonparametric estimation of a probability density in L 2 ( R ) . Expressing the mean integrated squared error in the Fourier domain, we show that it is close to the mean squared error in the Gaussian sequence model. Then, applying a modified version of Stein's blockwise method, we obtain a linear monotone oracle inequality and a kernel oracle inequality. As a consequence, the proposed estimator is sharp minimax adaptive (i.e. up to a constant) on a scale of Sobolev classes of densities. To cite this article: Ph. Rigollet, C. R. Acad. Sci. Paris, Ser. I 340 (2005).

Communication theoretic image restoration for binary-valued imagery

We present a new image-restoration algorithm for binary-valued imagery. A trellis-based search method is described that exploits the finite alphabet of the target imagery. This algorithm seeks the maximum-likelihood solution to the image-restoration problem and is motivated by the Viterbi algorithm for traditional binary data detection in the presence of intersymbol interference and noise. We describe a blockwise method to restore two-dimensional imagery on a row-by-row basis and in which a priori knowledge of image pixel correlation structure can be included through a modification to the trellis transition probabilities. The performance of the new Viterbi-based algorithm is shown to be superior to Wiener filtering in terms of both bit error rate and visual quality. Algorithmic choices related to trellis state configuration, complexity reduction, and transition probability selection are investigated, and various trade-offs are discussed.

A Primal-Dual Algorithm for MonotropicProgramming and its Application to NetworkOptimization

This paper presents a new primal-dual algorithm for solving a class of monotropic programming problems. This class involves many problems arising in a number of important applications in telecommunications networks, transportation and water distribution. The proposed algorithm is inspired by Kallio and Ruszczynski approach for linear programming [M. Kallio and A. Ruszczynski, WP-94-15, IIASA, 1994]. The problem is replaced by a game using two different augmented Lagrangian functions defined for the primal and the dual problems. It is then possible to develop a block-wise Gauss-Seidel method to reach an equilibrium of the game with alternating steps made in each component of the primal and dual variables. Finally, we show how this algorithm may be applied to some important problems in Network Optimization such as the minimum quadratic cost single flow problems and convex multicommodity flow problems.

Image binarization techniques for correlation-based pattern recognition

Correlation using binary images is suited to efficient digital realization or convenient optical implementation. Binarization algorithms are required in order to match grayscale imagery to these binary corre- lation architectures. We present several novel point-wise and block-wise binarization techniques all of which outperform the grayscale matched filter for large values of input signal-to-noise ratio (SNR50 dB). We dis- cuss direct binarization methods based on global thresholds, local thresholds, histogram equalization, edge-enhancement, and statistical binarization, as well as indirect methods based on auto- and cross- correlation techniques. These point-wise methods are shown to offer poor noise tolerance and a new block-wise binarization method is intro- duced to enhance recognition at low values of SNR. This block-wise technique is motivated by vector quantization-based image compression and offers performance superior to the grayscale matched filter for an input SNR as low as 212 dB. © 1999 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(99)02611-2)

On AR Modeling of Nonstationary Signals

The present paper is dealing with nonstationary signal modeling. Two parameter estimation methods are compared. The first one is a blockwise method performed in a single step over the hole data . The second one is sequential and processes recursively the data. The two methods are discribed and generalised to the noisy case. Both of them are tested on simulated signals and compared using an information creterion.

Results on AR-modelling of nonstationary signals

Modelling of nonstationary signals can be performed using time-varying AR-models. The time-dependent AR-coefficients are assumed to be well represented by a linear combination of a small number of known time functions. This paper intends to compare two methods for the identification of such models. The first one is a blockwise method in which the parameters are estimated using the Morf-Dickinson-Kailath-Vieira algorithm for the resolution of covariance equations. In the second method, the identification is performed by a recursive least-squares algorithm. Finally, an extension of the second method for the detection of abrupt changes in AR-processes is presented.