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Vision, Image and Signal Processing, IEE Proceedings -

Issue 2 • Date Apr 2000

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Displaying Results 1 - 13 of 13
  • Image sequence stabilisation based on DFT filtering

    Page(s): 95 - 102
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1008 KB)  

    An image sequence stabilisation system based on DFT filtering of absolute frame displacements to compensate for undesired fluctuations in the sequence by shifting image frames into correct positions is reported. The system compensates for undesired jitter, while preserving desired global camera motions. Robustness is introduced to interframe motion estimation by averaging motion vectors detected in the phase correlation surface View full abstract»

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  • Design of doubly complementary filters with approximate linear phase

    Page(s): 103 - 108
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (456 KB)  

    A new procedure for the design of a real doubly complementary (DC) pair of digital filters obtained from an all-pass structure is presented. The filter design is based on a zero-phase FIR filter design with multi-band frequency specifications and approximate linear-phase characteristic. The resulting complex or real all-pass filter structure is guaranteed to be stable. Some examples illustrating the design method including comparisons with conventional approximately linear phase IIR filters are also shown View full abstract»

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  • Recurrent neural network speech predictor based on dynamical systems approach

    Page(s): 149 - 156
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (576 KB)  

    A nonlinear predictive model of speech, based on the method of time delay reconstruction, is presented and approximated using a fully connected recurrent neural network (RNN) followed by a linear combiner. This novel combination of the well established approaches for speech analysis and synthesis is compared with traditional techniques within a unified framework to illustrate the advantages of using an RNN. Extensive simulations are carried out to justify the expectations. Specifically, the network's robustness to the selection of reconstruction parameters, the embedding time delay and dimension, is intuitively discussed and experimentally verified. In all cases, the proposed network was found to be a good solution for both prediction and synthesis View full abstract»

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  • Efficient hierarchical chaotic image encryption algorithm and its VLSI realisation

    Page(s): 167 - 175
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1428 KB)  

    An efficient hierarchical chaotic image encryption algorithm and its VLSI architecture are proposed. Based on a chaotic system and a permutation scheme, all the partitions of the original image are rearranged and the pixels in each partition are scrambled. Its properties of high security, parallel and pipeline processing, and no distortion are analysed. To implement the algorithm, its VLSI architecture with pipeline processing, real-time processing capability, and low hardware cost is designed and the FPGA realisation of its key modules is given. Finally, the encrypted image is simulated and its fractal dimension is computed to demonstrate the effectiveness of the proposed scheme View full abstract»

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  • Colour image filters based on hypercomplex convolution

    Page(s): 89 - 93
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (560 KB)  

    There are very few examples of true vector filters known for colour images. The authors introduce a new class of filter based on convolution with hypercomplex masks, and present three colour edge detecting filters inspired by the Prewitt, Sobel and Kirsch filters. These filters, when applied to a colour image, produce an almost greyscale image with colour edges where the original image had a sharp change of colour. They rely on a three-space rotation about the grey line of RGB space, implemented with the rotation operator R[]R-1 where R is a quaternion and R-1 is its inverse. Pixels of similar colour corresponding to opposing positions in the filter masks cancel to give a grey or monochromatic result, while pixels of different colour are differenced in a vector sense to give coloured edge pixels. The linearity of the new filters is discussed and the paper concludes with a discussion of the impulse response of a hypercomplex filter View full abstract»

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  • Efficient implementation of the Volterra filter

    Page(s): 109 - 114
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (428 KB)  

    An efficient implementation of the Volterra filter is presented which uses a frequency domain representation to reduce the number of computations. The multidimensional convolution of the Volterra filter is transformed to the frequency domain giving a transformed input matrix which is sparse and obtained directly from a one-dimensional Fourier transform, in addition to the sparse nature of the transformed input matrix, symmetries in both the Volterra filter and the frequency domain representation are exploited to increase the efficiency of the algorithm. The computational saving is demonstrated by comparing it with the direct implementation of the time domain representation and another technique which uses a frequency domain representation but does not utilise symmetry View full abstract»

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  • Reconstruction from 2-D wavelet transform modulus maxima using projection

    Page(s): 176 - 184
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (896 KB)  

    Wavelet transform modulus maxima can be used to characterise sharp variations such as edges and contours in an image. The authors analyse the a priori constraints present in the wavelet transform modulus maxima representation. A new projection-based algorithm which enforces all the a prior constraints in the representation is proposed. Quadratic programming is used to obtain a sequence which satisfies the maxima constraint. Thus realising the projection onto the maxima constraint space. To save computation, an approximate method to obtain a sequence which satisfies the maxima constraint is given. The new algorithm is shown to provide better solution than the original reconstruction algorithm of Mallat and Zhong (1992). The authors also propose a simple method to accelerate the algorithm. The acceleration is achieved by the incorporation of a momentum term which exploits the high correlation between the difference images between two consecutive iterations. The simulation results show that the proposed algorithm gives good reconstruction and the simple acceleration method can significantly improve the convergence rate View full abstract»

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  • Higher order statistics based IIR notch filtering scheme for enhancing sinusoids in coloured noise

    Page(s): 115 - 121
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (492 KB)  

    It is known that sinusoids generate lines in their spectra, but false lines may appear when the sinusoids are corrupted by coloured additive noise. In the paper, a higher-order statistics-based IIR filtering scheme is suggested to suppress additive coloured noise, thus enhancing the desired spectral peaks due to the sinusoids. The filter used is an unknown pole-zero constrained IIR notch filter. The filter coefficients are estimated by applying the linear prediction (LP) method to a block of a fourth-order mixed cumulant slice (FOMCS) of the input noisy signal. Therefore, the presented scheme automatically handles Gaussian noise (white or coloured). In the non-Gaussian noise case, a novel analysis is presented to show that, associated with the FOMCS, there is a new signal-to-noise ratio called the `signal-to-noise kurtosis ratio' (SNKR). This SNKR is a multiple of the conventional SNR if the additive noise is coloured non-Gaussian. Thus, the presented scheme is capable of handling additive coloured noise (Gaussian or non-Gaussian). The performance of the proposed scheme, compared with a correlation-based counterpart, is demonstrated through computer simulations View full abstract»

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  • Aerial inspection of overhead power lines using video: estimation of image blurring due to vehicle and camera motion

    Page(s): 157 - 166
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1744 KB)  

    One of the principal difficulties of video inspection of overhead power distribution lines from a helicopter is the blurring of the image due to rotation of the camera in its gimbals and the translational motion of the helicopter. The author presents a kinematic model describing the sightline geometry which includes the effect of the helicopter's rectilinear motion in three degrees of freedom. It is shown that, except for locations very near to the object being inspected the velocity flow field of the image is substantially uniform. The image Jacobian is used to predict how much blur is to be expected along a typical flight path during inspection operations. An important conclusion is that this will be greater than the tolerable limit of 1-2% unless the camera is rotated at a rate which compensates for the helicopter's rectilinear motion the implications for the design of an automated object tracking system are discussed. The maximum singular value of the image Jacobian is proposed as a quality measure for inspection from various locations of the helicopter relative to the target object. It is found to be an useful figure of relative merit but too conservative to provide a realistic upper bound on image blur View full abstract»

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  • Order-recursive blind identification of linear models using mixed cumulants

    Page(s): 139 - 148
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (648 KB)  

    The problem of determining the AR order and parameters of a nonminimum phase ARMA model from observations of the system output is considered. The model is driven by a sequence of random variables which is assumed unobservable. A novel identification algorithm based on the second- and third-order cumulants of the output sequences is introduced. It performs order-recursively by minimising a well defined cost function. Strong convergence and consistency of the algorithm are proved and the weight of the cost function is balanced between the second-order and the third-order cumulants of output sequences. The influence of the weight on the estimation accuracy is also evaluated. Theoretical analyses and numerical simulations show that the proposed algorithm is satisfactory for both order and parameter identification of an AR model which is subordinate to a nonminimum phase ARMA model View full abstract»

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  • Analytical design of 3-D wavelet filter banks using the multivariate Bernstein polynomial

    Page(s): 122 - 130
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (832 KB)  

    The design of 3-D multirate filter banks where the downsampling/upsampling is on the FCO (face centred orthorhombic) lattice is addressed. With such a sampling lattice, the ideal 3-D sub-band of the low-pass filter is of the TRO (truncated octahedron) shape. The transformation of variables has been shown previously to be an effective technique for designing M-D (multidimensional) filter banks. A design technique is presented for the transformation function using the multivariate Bernstein polynomial which provides a good approximation to the TRO sub-band shape. The method is analytically based and does not require any optimisation procedure. Closed form expressions are obtained for the filters of any order. Another advantage of this technique is that it yields filters with a flat frequency response at the aliasing frequency (ω1, ω2 , ω3)=(π, π, π). This flatness is important for giving regular discrete wavelet transform systems View full abstract»

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  • Fuzzy image clustering incorporating spatial continuity

    Page(s): 185 - 192
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (772 KB)  

    The authors present a spatial fuzzy clustering algorithm that exploits the spatial contextual information in image data. The objective functional of their method utilises a new dissimilarity index that takes into account the influence of the neighbouring pixels on the centre pixel in a 3×1 window. The algorithm is adaptive to the image content in the sense that influence from the neighbouring pixels is suppressed in nonhomogeneous regions in the image. A cluster merging scheme that merges two clusters based on their closeness and their degree of overlap is presented. Through this merging scheme, an `optimal' number of clusters can be determined automatically as iteration proceeds. Experimental results with synthetic and real images indicate that the proposed algorithm is more tolerant to noise, better at resolving classification ambiguity and coping with different cluster shape and size than the conventional fuzzy c-means algorithm View full abstract»

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  • Fixed-point error analysis of two DCT algorithms

    Page(s): 131 - 137
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (468 KB)  

    A fixed-point error analysis of two fast DCT algorithms proposed by Hou (1987) and Makhoul (1980) is presented. Expressions for error variances are derived and the results are compared with the simulation results. It is found that the simulation results and analysis results agree quite closely. This demonstrates the validity of the analysis. In addition, the two algorithms are compared in terms of their advantages and disadvantages View full abstract»

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