In this paper, the design and analysis of adaptive subband coders is discussed. Both the analysis/synthesis filters and quantizers of the subband coding system are adapted to exploit non-stationarities of the image da...
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In this paper, the design and analysis of adaptive subband coders is discussed. Both the analysis/synthesis filters and quantizers of the subband coding system are adapted to exploit non-stationarities of the image data. These adaptive systems are designed so that theoretical coding gain (TCG) is maximized, thereby minimizing mean-squared-error. The relative merits of adapting various components of a subband codec are investigated.
Wavelet analysis comes in two versions: the continuous one, used mostly for signal or image analysis, and the discrete one, originating from multiresolution analysis and particularly efficient in reconstruction and da...
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Wavelet analysis comes in two versions: the continuous one, used mostly for signal or image analysis, and the discrete one, originating from multiresolution analysis and particularly efficient in reconstruction and data compression. We review the construction of continuous wavelet transforms adapted to a given symmetry. Then we discuss in detail successively spatial wavelets, wavelets on the sphere and space-time wavelets.
The region for estimation is limited to the surface of the cortex. Considering this limitation, the cortex is extracted from MR images. We assume that the points on the extracted cortex are current dipoles. All the ac...
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The region for estimation is limited to the surface of the cortex. Considering this limitation, the cortex is extracted from MR images. We assume that the points on the extracted cortex are current dipoles. All the acquired points are considered as candidate sources. We perform the estimation by means of the multiple signal classification (MUSIC) method which is applied to the limited region. In the MUSIC method, the measured magnetic field data is divided into two subspaces, the signal subspace and the noise subspace. The minimum description length (MDL) criterion is used for this division.
This paper presents SPSIMD (single processor single instruction multiple data), a parallel processing paradigm for SISD (single instruction single data) processors. A SPSIMD thresholding algorithm, which is up to 6 ti...
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This paper presents SPSIMD (single processor single instruction multiple data), a parallel processing paradigm for SISD (single instruction single data) processors. A SPSIMD thresholding algorithm, which is up to 6 times as fast as the conventional SISD implementation, is used to demonstrate its effectiveness. Two variations of the threshold algorithm are examined for the SISD implementation to obtain the actual speedup for the SPSIMD approach to this algorithm. The effectiveness of the parallel data algorithms is analysed on the I486, TMS320C40 DSP, Sparc 10 and Alpha architectures. Near optimal speedups are demonstrated.
We propose an architecture for multiresolution classification based on a wavelet decomposition and a hierarchical neural network. Each layer of the neural network is a "frequency expert" associated with a si...
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We propose an architecture for multiresolution classification based on a wavelet decomposition and a hierarchical neural network. Each layer of the neural network is a "frequency expert" associated with a single frequency channel. Higher layers in the hierarchy integrate the information provided by the layers below, leading to a network where different "experts" cooperate to explain the input data. We illustrate the performance of this architecture on edge detection, a problem which is well known to be best addressed in a multiresolution framework.
This paper presents a multiprocessor unit for a fast video image data pre-processing in real time *** made pipelined multiprocessor architecture of specialised hardware *** paper presents reconfigurable specialised ha...
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This paper presents a multiprocessor unit for a fast video image data pre-processing in real time *** made pipelined multiprocessor architecture of specialised hardware *** paper presents reconfigurable specialised hardware processor for this pipelined *** reconfigurable processor implementation in the Xilinx FPGA.
This paper describes a region merging method for joint motion estimation and segmentation of digital video sequences. The region merging criterion is based on the measure of the matching error for a region when applyi...
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This paper describes a region merging method for joint motion estimation and segmentation of digital video sequences. The region merging criterion is based on the measure of the matching error for a region when applying a previously estimated motion to it. A region adjacency graph is used for data representation, which allows a scan independent processing and gives a high-level view. The method is simple-shape object-oriented and starts from a block-based segmentation. The aim of the proposed technique is to define simple shaped objects in a scene using motion information and a simple test.
We characterise the features of a regularised ML estimate, or equivalently a MAP estimate, of an image (or a signal) in relation with the form of the regularisation. The unknown image (signal) is observed through a li...
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We characterise the features of a regularised ML estimate, or equivalently a MAP estimate, of an image (or a signal) in relation with the form of the regularisation. The unknown image (signal) is observed through a linear operator and the data are corrupted by white Gaussian noise. Its reconstruction is regularised by the energy of a first-order Markov random field where the contributions of the transitions between adjacent neighbours are weighted using general potential functions (PFs). We exhibit the relationship between several features of the estimate and the form of the PF. Points of interest are the edge recovery, the stability of the estimator, the estimation of locally constant regions, the bias over large transitions, the resolution. The exposed theoretical considerations are corroborated by numerical simulations.
An Occam filter employs lossy data compression to separate signal from noise. Previously it was shown that Occam filters are useful for filtering random noise from discrete samples of a deterministic and continuous si...
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An Occam filter employs lossy data compression to separate signal from noise. Previously it was shown that Occam filters are useful for filtering random noise from discrete samples of a deterministic and continuous signal. In this paper, we show that Occam filters can also be used to separate two stochastic sources, with the effectiveness of the separation depending on their relative compressibility. We then construct an Occam filter based on singular value decomposition (SVD) and apply it to digital images corrupted with Gaussian noise. We observe that the SVD-based Occam filter outperforms the wavelet-based denoising method of Donoho and Johnstone (1994) and DeVore and Lucier (1992).
Most traditional methodologies in digital image processing start with a discrete model which is directly related to the subsequent discrete processing of the image data. We review some examples and focus on some detai...
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Most traditional methodologies in digital image processing start with a discrete model which is directly related to the subsequent discrete processing of the image data. We review some examples and focus on some details of an emerging new methodology that starts from some continuous models provided by PDEs and proceeds with discrete processing of the image data via the numerical implementation of these PDEs on some discrete grid. We examine the PDEs modeling multiscale morphological image analysis and the related eikonal PDE of optics. The PDE approach is very promising because it provides many new mathematical models and has connections with the physics of imaging.
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