Some statisticalmethods of texture synthesis are discussed. The statistical models at the pixel level, like the SAR and GMRF models, seem to capture the main characteristics of microtextures like grass, send, cork an...
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ISBN:
(纸本)0941514056
Some statisticalmethods of texture synthesis are discussed. The statistical models at the pixel level, like the SAR and GMRF models, seem to capture the main characteristics of microtextures like grass, send, cork and so on. These models do not synthesize textures like plastic bubbles and brick wall satisfactorily due to macrostructures present in these textures. A combination of statistical and structural techniques should prove more efficient to synthesize macrotextures.
A procedure for analysis and recognition of image patterns is presented. The procedure consists of the following main steps: removal of noise by means of a nonlinear smoothing, extraction of pattern edges, reduction o...
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ISBN:
(纸本)0444875832
A procedure for analysis and recognition of image patterns is presented. The procedure consists of the following main steps: removal of noise by means of a nonlinear smoothing, extraction of pattern edges, reduction of irregularities of boundary curves by a 'statistical' smoothing, identification of the nonconnected image regions corresponding to different scene patterns, and finally recognition of the patterns identified in the previous step. Two different recognition methods are implemented: the first performs the recognition by a comparison of contour point FFTs with pre-stored values;the second uses invariants drawn from inertia central moments. Experiments confirm the efficiency of the procedure.
In this paper, we demonstrate the use of adaptive homomorphic filtering in exposing objects under light cloud cover. In particular, the homomorphic filter invoked is space-varying and is parameterized by the local mea...
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In this paper, we demonstrate the use of adaptive homomorphic filtering in exposing objects under light cloud cover. In particular, the homomorphic filter invoked is space-varying and is parameterized by the local mean level of the degraded image. The local mean serves as an indication of the extent of local cloud cover degradation. We show that this adaptive procedure has greater potential than the long-space methods in exposing objects beneath light cloud cover. In addition, adaptive homomorphic filtering compares favorably with an iterative homomorphic enhancement procedure which is an extension of the one-pass nonadaptive homomorphic filter.
This paper introduces a new iterative image restoration method which is capable of restoring noisy, blurred images by incorporating a priori knowledge about the image and noise statistics into the iterative procedure....
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This paper introduces a new iterative image restoration method which is capable of restoring noisy, blurred images by incorporating a priori knowledge about the image and noise statistics into the iterative procedure. The iteration equation consists of a prediction part which is based on a noncausal image model description and an innovation part which is weighted by a gain factor. The gain is computed using a linear MSE optimization procedure and is updated at each step of the iteration. This image restoration scheme can be interpreted as an iterative procedure with a statistical constraint on the image data.
Several estimators for the digital restoration of a noisy image are presented. These estimators differ from those previously found in the literature in that they are robust for deviations from the assumed signal or no...
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Several estimators for the digital restoration of a noisy image are presented. These estimators differ from those previously found in the literature in that they are robust for deviations from the assumed signal or noise statistics, and the image is assumed to have been corrupted by signal-dependent or nonlinear noise, rather than simple additive noise. The assumption of signal-dependence complicates the problem considerably for non-robust estimators; for robust estimators, the problems are such that analytic solutions become impossible, and numerical methods must be used to derive the estimators. Both point-estimators and multi-parameter estimators are considered. In addition to the description of the various robust estimators, a comparison of their performance on real images corrupted by simulated signal-dependent noise with various (Gaussian and non-Gaussian) distributions is also presented.
A unified theoretical treatment of constrained optimisation methods for tomographic and spectral estimation from discrete data is given. The solution is shown to be equivalent to the unconstrained optimisation of a du...
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A unified theoretical treatment of constrained optimisation methods for tomographic and spectral estimation from discrete data is given. The solution is shown to be equivalent to the unconstrained optimisation of a dual functional in which the image or spectrum is modelled in terms of a Lagrange multiplier vector and the kernel of the constraint integrals. In order to obtain the best possible solution it is important to consider the effects of noise in the constraints. The problem is reformulated using the above models and the exact data is replaced with the noise statistics as constraints; this is solved using a penalty method. A very fast direct algorithm is also introduced which matches the noise variance provided the signal to noise ratio is approximately known.
The coherent optical filtering techniques provide a general concept for the classification of patterns. This paper describes the design and testing of a hybrid optical-digital imageprocessing system and the developme...
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statisticalmethods for estimating the parameters of a system are often based on assuming that the system inputs are Gaussian. As a result least-squares criteria are commonly used for estimating the system parameters....
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statisticalmethods for estimating the parameters of a system are often based on assuming that the system inputs are Gaussian. As a result least-squares criteria are commonly used for estimating the system parameters. In this paper we will describe methods that are based on uniformly distributed system inputs. The estimates of the system parameters will then be found from min/max operations on linear combinations of the output samples. A new method for solving the resulting min/max problems is described and the min/max criterion is used in an application where it performs better than the commonly used least-squares criterion.
In this paper Mendel and Hsueh's [10] state-variable modeling technique for transforming a 1-D non-causal system into a causal system is extended to 2-dimensional systems which have half-plane (HP) supports. The 2...
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In this paper Mendel and Hsueh's [10] state-variable modeling technique for transforming a 1-D non-causal system into a causal system is extended to 2-dimensional systems which have half-plane (HP) supports. The 2-D impulse responses treated here are restricted to a class in which their associated 2-D z-transforms have separable denominators. The final state space model is a special type of so-called Roesser's model. It is a very low-order model, which is important from a computational point of view, when, for example, 2-D recursive estimation algorithms are applied to 2-D systems with HP supports.
A model for the reconstruction of real color textures is presented. It is an extension of a bidimensionnal Markov model previously proposed for the synthesis of gray level textures. The main problem is to limit the nu...
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A model for the reconstruction of real color textures is presented. It is an extension of a bidimensionnal Markov model previously proposed for the synthesis of gray level textures. The main problem is to limit the number of colors to avoid computationnal complexity. This is solved by operating a suboptimal quantization of the color information included in the real texture: i) firstly by transforming the color components into a perceptive uniform space, based on a vision model; ii) then by optimally clustering this color space.
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