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Vision, Image and Signal Processing, IEE Proceedings -

Issue 3 • Date June 2000

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Displaying Results 1 - 13 of 13
  • High capacity image steganographic model

    Publication Year: 2000 , Page(s): 288 - 294
    Cited by:  Papers (22)  |  Patents (3)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (805 KB)  

    Steganography is an ancient art of conveying messages in a secret way that only the receiver knows the existence of a message. So a fundamental requirement for a steganographic method is imperceptibility; this means that the embedded messages should not be discernible to the human eye. There are two other requirements, one is to maximise the embedding capacity, and the other is security. The least-significant bit (LSB) insertion method is the most common and easiest method for embedding messages in an image. However, how to decide on the maximal embedding capacity for each pixel is still an open issue. An image steganographic model is proposed that is based on variable-size LSB insertion to maximise the embedding capacity while maintaining image fidelity. For each pixel of a grey-scale image, at least four bits can be used for message embedding. Three components are provided to achieve the goal. First, according to contrast and luminance characteristics, the capacity evaluation is provided to estimate the maximum embedding capacity of each pixel. Then the minimum-error replacement method is adapted to find a grey scale as close to the original one as possible. Finally, the improved grey-scale compensation, which takes advantage of the peculiarities of the human visual system, is used to eliminate the false contouring effect. Two methods, pixelwise and bitwise, are provided to deal with the security issue when using the proposed model. Experimental results show effectiveness and efficiency of the proposed model. View full abstract»

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  • Restricted-structure linear estimators for multiple-model systems

    Publication Year: 2000 , Page(s): 193 - 204
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (612 KB)  

    A new class of discrete-time optimal linear estimators is introduced for multiple-model systems that minimises a minimum-variance criterion but where the structure is prespecified to have a simple low-order form. The restricted-structure estimator can be of much lower order than a Kalman (1961) or Wiener (1949) estimator and it minimises the estimation-error variance, subject to the constraint referred to. The numerical optimisation algorithm is simple to implement and full-order optimal solutions are available as a by-product of the analysis. The algorithm enables low-order optimal estimators to be computed that directly minimise the cost index across a set of possible linear signal or noise source models. The main technical advances lie in the theoretical analysis that enables the expanded cost expression to be simplified before the numerical solution is obtained, and the extension of the restricted-structure optimisation technique to multiple-model systems View full abstract»

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  • Genetic algorithms for feature selection in machine condition monitoring with vibration signals

    Publication Year: 2000 , Page(s): 205 - 212
    Cited by:  Papers (16)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (508 KB)  

    Artificial neural networks (ANNs) can be used successfully to detect faults in rotating machinery. Using statistical estimates of the vibration signal as input features. In any given scenario, there are many different possible features that may be used as inputs for the ANN. One of the main problems facing the use of ANNs is the selection of the best inputs to the ANN, allowing the creation of compact, highly accurate networks that require comparatively little preprocessing. The paper examines the use of a genetic algorithm (GA) to select the most significant input features from a large set of possible features in machine condition monitoring. Using a GA, a subset of six input features is selected from a set of 66 giving a classification accuracy of 99.8%, compared with an accuracy of 87.2% using an ANN without feature selection and all 66 inputs. From a larger set of 156 different features, the GA is able to select a set of six features to give 100% recognition accuracy View full abstract»

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  • Low distortion speech enhancement

    Publication Year: 2000 , Page(s): 247 - 253
    Cited by:  Papers (15)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (328 KB)  

    An innovative approach to speech enhancement is illustrated which minimises distortion to the underlying speech during the noise-reduction process. The key to this approach lies in the identification of whether the additive noise for a particular frequency component is constructive or destructive. Once this can be identified both multiplicative and subtractive filters can be derived using the minimum mean-square error criterion. The optimal combination of the proposed multiplicative and subtractive filter is also shown View full abstract»

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  • Decision feedback equaliser design using support vector machines

    Publication Year: 2000 , Page(s): 213 - 219
    Cited by:  Papers (15)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (356 KB)  

    The conventional decision feedback equaliser (DFE) that employs a linear combination of channel observations and past decisions is considered. The design of this class of DFE is to construct a hyperplane that separates the different signal classes. It is well known that the popular minimum mean square error (MMSE) design is generally not the optimal minimum bit error rate (MBER) solution. A strategy is proposed for designing the DFE based on support vector machines (SVMs). The SVM design achieves asymptotically the MBER solution and is superior in performance to the usual MMSE solution. Unlike the exact MBER solution, this SVM solution can be computed very efficiently View full abstract»

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  • Optimised feature map finite-state vector quantisation for image coding

    Publication Year: 2000 , Page(s): 266 - 270
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (324 KB)  

    An optimised feature map finite-state vector quantisation (referred to as optimised FMFSVQ) is presented for image coding. Based on the block-based gradient descent search algorithm used for motion estimation in video coding, the optimised FMFSVQ system finds a neighbourhood-based optimal codevector for each input vector by extending the associated state codebook stage by stage, thus rendering each state quantiser a variable rate vector quantisation. The optimised FMFSVQ system can be interpreted as a cascade of a finite-state vector quantiser and classified vector quantisers. Furthermore, an adaptive optimised FMFSVQ is obtained. Experiments demonstrate the superior rate-distortion performance of the adaptive optimised FMFSVQ compared with the original adaptive FMFSVQ and the memoryless vector quantisation View full abstract»

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  • Signal-dependent film grain noise generation using homomorphic adaptive filtering

    Publication Year: 2000 , Page(s): 283 - 287
    Cited by:  Patents (26)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (408 KB)  

    A technique for the generation of signal-dependent film grain noise is presented. It finds applications in television and motion picture productions where artificial film grain noise is added to computer-generated synthetic images to give them a realistic appearance. After decoupling the signal from the noise, using the generalised homomorphic transformation, the noise is removed from the noisy image using the proposed adaptive filtering scheme. The noise parameter is then estimated using the higher-order statistics (skewness or kurtosis) of the observed image and the filtered image statistics, and it is used to generate film grain noise. Computer simulation results demonstrate the effectiveness of the proposed method View full abstract»

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  • Proposal of standards for intelligibility tests of Chinese speech

    Publication Year: 2000 , Page(s): 254 - 260
    Cited by:  Papers (5)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (348 KB)  

    The authors propose standards for evaluating the intelligibility of coded, synthesised or distorted Chinese speech over a wired or wireless voice channel in a communication system. The standards are based on the English equivalent diagnostic rhyme test (DRT) and modified rhyme test (MRT). The relative features of Chinese phonetics are outlined and the underlying principles of choosing phonetic units for the proposed standards, the Chinese diagnostic rhyme test (CDRT) and the Chinese modified rhyme test (CMRT), are presented View full abstract»

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  • Locally adaptive conductance in geometry-driven-diffusion filtering of magnetic resonance tomograms

    Publication Year: 2000 , Page(s): 271 - 282
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (844 KB)  

    A novel methodology for locally adapting the exponential conductance in geometry-driven diffusion (GDD) is proposed which employs pixel dissimilarity measures. Two alternative approaches are developed; both are based on a transient interval, within which the relaxation parameter K is selected. In the first case, the limits of the interval are derived from global quantiles of the intensity gradients; in the second case, they are derived from the optimal variable parameter K0pt, calculated from a specific cost function. This function is designed using intensity gradient histograms of region interiors and boundaries in an appropriate image template of an MR brain tomogram. As a local measure, the mean direction dissimilarity has been used. Computer experiments with the locally adaptive geometry-driven diffusion filtering of an MR-head phantom have been performed and quantitatively evaluated. They include, as a reference, two other GDD filtering methods View full abstract»

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  • Universal perceptual weighted zerotree coding for image and video compression

    Publication Year: 2000 , Page(s): 261 - 265
    Cited by:  Papers (3)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (340 KB)  

    A universal representation for the perceptual weighted zerotree coding algorithm is developed, in which the perceptual weighted zerotree coding is decomposed into two separate parts, i.e. visual weighting and zerotree representation, which can be realised independently. Prior to zerotree processing, the extracted full-tree is weighted by using a visual weighting matrix. Any zerotree algorithm like EZW, SPIHT and zerotree space-frequency quantisation can be used to encode the weighted coefficients of the wavelet transform. In other words, any previous algorithm without perceptual weighting can be easily extended to form a new perceptual coder using the proposed framework. Several examples of visual weighting matrices are given to show the effect of the new method View full abstract»

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  • Convergence performance of adaptive lattice filters with nonlinear parameter updates

    Publication Year: 2000 , Page(s): 238 - 246
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (364 KB)  

    The convergence properties of adaptive lattice filters with nonlinear parameter updates have been analysed. The theoretical expressions for the convergence rate and the asymptotic error variance of the PARCOR coefficient are derived. Using these expressions, the convergence performance in the absence of impulsive noise is investigated and compared with those of the linear-type and sign-type adaptive lattice filters. Furthermore, the mean parameter variation caused by an impulsive noise and the parameter recovery time are evaluated to comprehensively compare the convergence performances in the presence of impulsive noise View full abstract»

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  • Second-order H optimal LMS and NLMS algorithms based on a second-order Markov model

    Publication Year: 2000 , Page(s): 231 - 237
    Cited by:  Papers (2)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (344 KB)  

    It is shown that two algorithms obtained by simplifying a Kalman filter considered for a second-order Markov model are H suboptimal. Similar to least mean squares (LMS) and normalised LMS (NLMS) algorithms, these second order algorithms can be thought of as approximate solutions to stochastic or deterministic least squares minimisation. It is proved that second-order LMS and NLMS are exact solutions causing the maximum energy gain from the disturbances to the predicted and filtered errors to be less than one, respectively. These algorithms are implemented in two steps. Operation of the first step is like conventional LMS/NLMS algorithms and the second step consists of the estimation of the weight increment vector and prediction of weights for the next iteration. This step applies simple smoothing on the increment of the estimated weights to estimate the speed of the weights. Also they are cost-effective, robust and attractive for improving the tracking performance of smoothly time-varying models View full abstract»

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  • Recursive total least squares algorithm for single-user blind channel equalisation

    Publication Year: 2000 , Page(s): 220 - 230
    Cited by:  Patents (17)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (556 KB)  

    The problem of blind channel identification/equalisation using second-order statistics or equivalent deterministic properties of the oversampled channel output has attracted considerable attention. Deterministic blind subspace algorithms are particularly attractive because of their finite sample convergence property and because their solution can be obtained in closed form. Most subspace algorithms developed up until now, however, are based on block processing and have high computational and memory requirements. In the paper, adaptive techniques are used to lower the computational burden. A single-user direct symbol estimation algorithm is presented. The first step in the algorithm consists of an adaptive matrix singular value decomposition for a (virtual) channel identification-type operation. A recursive total least squares algorithm is then used to recover the input symbols. The algorithm is able to track time-varying channels View full abstract»

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