Presents a statistical approach to texture analysis. Texture is regarded as a two-dimensional random field defined by a suitable autoregressive model. Two methods are considered. The former employs a two-dimensional l...
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Presents a statistical approach to texture analysis. Texture is regarded as a two-dimensional random field defined by a suitable autoregressive model. Two methods are considered. The former employs a two-dimensional linear estimation technique: the grey level of a texture pixel is estimated from a weighted sum of grey levels of its neighbour pixels and the estimator that minimizes the mean-square error is used for texture characterization. The latter uses a simultaneous autoregressive (SAR) model, that characterizes spatial interactions of texture grey levels along fixed directions. Eight parameters corresponding with two different SAR models are extracted as textural features. These parameters capture texture characteristics in horizontal-vertical and diagonal-off diagonal directional pairs. These new techniques were applied to meteorological radar images, where precipitation and clutter regions correspond with two different and well distinct textures. Good results were provided by a minimum distance classification.< >
A transform image coding method is presented. A discrete cosine transform (DCT) is performed on blocks of 16*16 size. A threshold mask is applied by using a fixed threshold determined by the statistical properties of ...
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A transform image coding method is presented. A discrete cosine transform (DCT) is performed on blocks of 16*16 size. A threshold mask is applied by using a fixed threshold determined by the statistical properties of the DCT coefficients. Selected coefficients are coded with a Huffman code. Three methods for coding the threshold mask are compared. zigzag, orthogonal, and quadtree scan. Three Huffman code tables are presented for coding the runlengths of zeros. A high-quality decoded image is shown at 1 bit/pixel.< >
Feature extraction is an important component in all areas of imageprocessing a fact demonstrated by the wide variety and diversity of the methods available. These range from statistical to human vision based approach...
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Feature extraction is an important component in all areas of imageprocessing a fact demonstrated by the wide variety and diversity of the methods available. These range from statistical to human vision based approaches. Although progress has been fruitful and uninterrupted, it is also apparent that as yet to unified theory of feature identification or representation has emerged. It is towards this goal that this work is directed. The approach adopted has two fundamental principles: meaningful image features are inherently localised in both the spatial and spatial frequency domains; the degree of this locality is not constant across the range of features, in general, image features exist within a multiresolution space. Based on these principles, an attempt is made in this work to provide a unified feature extraction framework. Starting from a general image model, a feature estimation scheme is developed which, by way of example, assumes the image to consist of a multiresolution set of line or edge features. The estimation is achieved by the use of an invertible transform, which by definition incorporates the multiresolution structure underlying the model. The work concludes with a discussion on the appropriateness of the approach to more complex features, such as curvature and shape.< >
Summary form only given. Relaxation labeling (RL) processes and Gibbs sampler (GS) can be considered a class of iterative parallel algorithms that solve the consistent labeling problems. They are widely used in image ...
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Summary form only given. Relaxation labeling (RL) processes and Gibbs sampler (GS) can be considered a class of iterative parallel algorithms that solve the consistent labeling problems. They are widely used in imageprocessing and the recognition of figures by way of reducing ambiguities of labeling. Although they have similar properties, their iterative improvement methods are quite distinct. That is, RL is essentially a deterministic process while GS is stochastic. The author has evaluated and compared the performance of the models for coloring problems and found from the experimental results that for this kind of problem RL is more efficient than GS.< >
The use of a priori information and its effect on multiband image restoration are considered. The Wiener solution that takes into account the statistical correlations between image bands is shown to be extremely sensi...
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The use of a priori information and its effect on multiband image restoration are considered. The Wiener solution that takes into account the statistical correlations between image bands is shown to be extremely sensitive to between-channel correlation estimates. In fact, the Wiener restoration using the cross correlations, estimated from a prototype image similar to the original, may give worse results than the Wiener restoration obtained by simply ignoring the cross correlations and independently restoring the channels. With the aim of obtaining a more robust algorithm, a unified set-theoretic framework that allows the simultaneous use of single and multiband a priori information is developed. A number of convex constraint sets are provided for use in a convex projections restoration algorithm.< >
The authors propose a novel approach to the problem of detecting events, i.e. significant differences between pictures of a given scene taken at different wavelengths or with different sensors. It is shown that event ...
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The authors propose a novel approach to the problem of detecting events, i.e. significant differences between pictures of a given scene taken at different wavelengths or with different sensors. It is shown that event detection can be expressed, within a Bayesian decision framework, as a contextual estimation problem. The unknown process to be estimated corresponds to the significant interimage changes. A grey-level map assigned to the unknown event maximizes the a posteriori distribution of the event image, given the observed images. A Markov random field model is used to describe the spatial statistics of the unknown process. The authors present an application to the fine arts, that of finding an underpainting from a visible/X-ray pair of images of the same painting.< >
Summary form only given, as follows. A method is introduced where image intensity and edge information from a pair of stereo images are integrated into a single stereo vision technique. A Bayesian model is used to der...
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Summary form only given, as follows. A method is introduced where image intensity and edge information from a pair of stereo images are integrated into a single stereo vision technique. A Bayesian model is used to derive the maximum a posteriori (MAP) stereo matched solution for the proposed integrated matching algorithm. The disparity is modeled as a Markov random field (MRF) and the input image data as a coupled MRF (intensity and edge orientation process together). The left and right stereo images are considered as degraded observations and external inputs to the system. The well-known MRF-Gibbs distribution equivalence is used to reduce the MAP problem to that of finding an appropriate energy function (cost function) that describes the constraints on the solution. A stochastic relaxation algorithm (simulated annealing) is used to find the best disparity solution by minimizing the energy equation. Results are presented for the proposed integrated stereo technique.< >
A signal-analysis expert system (SAES) has been developed to address the problem of noise-editing pulse-position modulation (PPM) signals in preparation for analysis. These signals have historically required manual no...
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A signal-analysis expert system (SAES) has been developed to address the problem of noise-editing pulse-position modulation (PPM) signals in preparation for analysis. These signals have historically required manual noise editing, or a combination of manual and semiautomatic editing techniques. Due to the nature of the signal, standard frequency-domain noise-reduction techniques are not applicable. The objectives for SAES were to provide a substantially improved tool for automatically noise-editing the signal and to provide intelligent assistance to the signal analyst. To meet these goals, SAES uses a combination of statistical pattern recognition, imageprocessing, and expert system technologies. The result is a tool which reduces the noise editing workload on the human analyst by approximately 80% over existing methods while retaining accuracy comparable to a human editor working manually. Implementation on a parallel-processing transputer system provides real-time processing rates.< >
A novel approach to robot guidance in an unfamiliar environment is presented. In previous guiding methods, a preinstalled map or predefined path is required for a robot navigating in its working space. The present met...
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A novel approach to robot guidance in an unfamiliar environment is presented. In previous guiding methods, a preinstalled map or predefined path is required for a robot navigating in its working space. The present method uses an indication post (IP) to provide the information on the destination, the direction, and the distance from the source point to the destination point, assuming that the mobile robot has no information concerning its workplace. To realize the concept, many IPs depicting simple information were designed. The content of IPs can be described by using a finite-state grammar. image-processing, statistical, and syntactic pattern recognition approaches are integrated to solve the problems of IP finding, robot location determination, IP identification, and understanding. Experiments simulating practical environments were performed. The results verify that the IP candidates can be automatically found and the depicted information can be extracted and understood correctly, so that the mobile robot is able to plan a global optimal path from an arbitrary start point to an arbitrary end point.< >
The performance is compared of a linear space-invariant (LSI) maximum a posteriori filter, an LSI reduced update Kalman filter (RUKF), an edge-adaptive RUKF, and an adaptive convex-type constraint-based restoration im...
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The performance is compared of a linear space-invariant (LSI) maximum a posteriori filter, an LSI reduced update Kalman filter (RUKF), an edge-adaptive RUKF, and an adaptive convex-type constraint-based restoration implemented via the method of projection onto convex sets. The finite impulse response Wiener filter is taken as a benchmark in this comparison. In image restoration, the LSI techniques are found to have some important drawbacks, such as producing ringing artifacts. As expected, the space-variant restoration methods which are adaptive to local image properties provide the best results.
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