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Vision, Image and Signal Processing, IEE Proceedings -

Issue 5 • Date 22 Oct. 2003

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Displaying Results 1 - 9 of 9
  • Novel fuzzy reinforced learning vector quantisation algorithm and its application in image compression

    Publication Year: 2003
    Cited by:  Papers (4)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (824 KB)  

    A new approach to the design of optimised codebooks using vector quantisation (VQ) is presented. A strategy of reinforced learning (RL) is proposed which exploits the advantages offered by fuzzy clustering algorithms, competitive learning and knowledge of training vector and codevector configurations. Results are compared with the performance of the generalised Lloyd algorithm (GLA) and the fuzzy K-means (FKM) algorithm. It has been found that the proposed algorithm, fuzzy reinforced learning vector quantisation (FRLVQ), yields an improved quality of codebook design in an image compression application when FRLVQ is used as a pre-process. The investigations have also indicated that RL is insensitive to the selection of both the initial codebook and a learning rate control parameter, which is the only additional parameter introduced by RL from the standard FKM View full abstract»

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  • Integrated recurrent neural network for image resolution enhancement from multiple image frames

    Publication Year: 2003
    Cited by:  Patents (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (588 KB)  

    The paper presents a new method for image resolution enhancement from multiple image frames using an integrated recurrent neural network (IRNN). The IRNN is a set of feedforward neural networks working collectively with the ability of having feedback of information from its output to its input. As such, it is capable of both learning and searching the optimal solution in the solution space for optimisation problems. In other words, it combines the advantages of both the Hopfield network and the multilayered feedforward network in solving the enhanced image reconstruction problem. Simulation results demonstrate that the proposed IRNN can successfully be used to enhance image resolution. The proposed neural network based method is promising for real-time applications, especially when the inherent parallelism of computation of the neural network is explored. Further, it can adapt itself to the various conditions of the reconstruction problem by learning View full abstract»

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  • Design of two-channel linear-phase QMF banks based on real IIR all-pass filters

    Publication Year: 2003
    Cited by:  Papers (3)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (350 KB)  

    The design of two-channel linear-phase quadrature mirror filter (QMF) banks constructed by real infinite impulse response (IIR) digital all-pass filters is considered. The design problem is appropriately formulated to result in a simple optimisation problem. Using a variant of Karmarkar's algorithm, the optimisation problem can be efficiently solved through a frequency sampling and iterative approximation method to find the real coefficients for the IIR digital all-pass filters. The resulting two-channel QMF banks possess an approximately linear phase response without magnitude distortion. The effectiveness of the proposed technique is achieved by forming an appropriate Chebyshev approximation of the desired phase response and then finding its solution from a linear subspace in a few iterations. Finally, several simulation examples are presented for illustration and comparison View full abstract»

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  • Erratum

    Publication Year: 2003 , Page(s): 339
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (156 KB)  

    First Page of the Article
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  • Dynamic programming approach to optimal vertex selection for polygon-based shape approximation

    Publication Year: 2003
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (252 KB)  

    A new vertex selection scheme for polygon-based contour coders is presented. In the proposed method, final vertex points are determined by a 'two-step procedure'. In the first step, the initial vertices are simply selected from the contour, thereby constituting a subset of the original contour, using conventional methods such as the iterated refinement method (IRM) or progressive vertex selection (PVS) method. In the second step, a vertex adjustment process is incorporated to generate final vertices that are no longer confined to the contour and are optimal in view of the given distortion measure. For the optimality of the final vertices, a dynamic programming (DP)-based solution for the adjustment of the vertices is proposed. Consequently, the authors offer two main contributions. First, it is shown that DP can be successfully applied to vertex adjustment. Secondly, the use of DP enables global optimality to be achieved in vertex selection without any iterative processes. Experimental results are presented to demonstrate the superiority of the proposed method over traditional methods View full abstract»

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  • Robust adaptive quasi-Newton algorithms for eigensubspace estimation

    Publication Year: 2003
    Cited by:  Papers (2)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (416 KB)  

    A novel quasi-Newton algorithm for adaptively estimating the principal eigensubspace of a covariance matrix by making use of an approximation of its Hessian matrix is derived. A rigorous analysis of the convergence properties of the algorithm by using the stochastic approximation theory is presented. It is shown that the recursive least squares (RLS) technique can be used to implement the quasi-Newton algorithm, which significantly reduces the computational requirements from O( pN2 ) to O( pN), where N is the data vector dimension and p is the number of desired eigenvectors. The algorithm is further generalised by introducing two adjustable parameters that efficiently accelerate the adaptation process. The proposed algorithm is applied to different applications such as eigenvector estimation and the Comon-Golub (1990) test in order to study the convergence behaviour of the algorithm when compared with others such as PASTd, NIC, and the Kang et al. (see IEEE Trans. Signal Process., vol. 48, p.3328-33, 2000) quasi-Newton algorithm. Simulation results show that the new algorithm is robust against changes of the input scenarios and is thus well suited to parallel implementation with online deflation View full abstract»

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  • Low complexity concurrent constant modulus algorithm and soft decision directed scheme for blind equalisation

    Publication Year: 2003
    Cited by:  Papers (9)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (4118 KB)  

    The recently introduced concurrent constant modulus algorithm (CMA) and decision-directed (DD) scheme provides a state-of-the-art low-complexity blind equalisation technique for high-order quadrature amplitude modulation (QAM) channels. At a small cost of slightly more than doubling the complexity of the standard CMA blind equaliser, this concurrent CMA and DD blind equaliser achieves a dramatic improvement in equalisation performance over the CMA. In the paper, a new blind equalisation scheme is proposed based on concurrent CMA and a novel soft decision-directed (SDD) adaptation. The proposed concurrent CMA and SDD blind equaliser has simpler computational requirements than the concurrent CMA and DD algorithm. Extensive simulation shows that it has the same steady-state equalisation performance as the concurrent CMA and DD algorithm and a faster convergence speed over the latter scheme View full abstract»

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  • Psychovisually tuned attribute operators for pre-processing digital video

    Publication Year: 2003
    Cited by:  Papers (2)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (473 KB)  

    In video compression, image pre-processing is used to improve the overall compression performance by removing noise from the image sequence. The compressibility can further be improved by removing other visually redundant information that the human visual system is not sensitive to, provided that the visual quality of the image sequence is preserved. A new approach to pre-processing is presented, based on attribute morphology in which extrema regions are removed provided that they meet some perceptual criterion, given by the attribute limit. Both the standard area and a new attribute, based on the power within the removed component, are investigated. Psychovisual experiments are used to determine the psychovisually lossless attribute limits and the limits expressed in a generalised form. The performance gain achieved by this approach is determined by comparing the codec outputs for original and the pre-processed images. Results show the compressibility of the pre-processed images, assessed by a number of compression methods, is significantly improved, thus demonstrating the advantages of the attribute morphology methods, with the power attribute providing the greatest improvement View full abstract»

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  • Parameterisation of slant-Haar transforms

    Publication Year: 2003
    Cited by:  Papers (3)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (402 KB)  

    A parameterisation of the slant-Haar transform is presented, which includes an existing version of the slant-Haar transform. An efficient algorithm for the slant-Haar transform is developed and its computational complexity is estimated. The parametric slant-Haar transforms are compared to the Karhunen-Loeve transform. The parametric slant-Haar is shown to perform better than the commonly used slant-Haar and slant-Hadamard transforms for the first-order Markov model and also performs better than the discrete cosine transform for images approximated by the generalised image correlation model View full abstract»

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