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检索条件"任意字段=Neural and Stochastic Methods in Image and Signal Processing II"

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methods for combination of evidence in function-based 3D object recognition

Methods for combination of evidence in function-based 3D obj...

引用

neural and stochastic methods in image and signal processing

作者： Stark, Louise Hall, Lawrence O. Bowyer, Kevin W. Univ. of South Florida Stockton CA USA

ISBN: (纸本)0819409391

A system which utilizes a function-based representation has been implemented and tested, using the object category `chair' for a case study. Functional description is used to recognize classes and identify subclasses of known categories of objects, even if the specific object has never been encountered previously. Interpretation of the functionality of an object is accomplished through qualitative reasoning about its 3-D shape. During the recognition process, evidence is gathered as to how well the functional requirements are met by the input shape. An investigation of different types of operators used in the combination of the functional evidence has been made. Three pairs of conjunctive and disjunctive operators have been used in the recognition process of the 100+ object shapes. The results are compared and differences are discussed.

关键词： image processing

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Satellite imagery and exogenous data integration by neural network in automatic land-cover classification

Satellite imagery and exogenous data integration by neural n...

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neural and stochastic methods in image and signal processing

作者： Barros, Maria S. Amorim, Maria C. Rodrigues, Valter Inst. de Pesquisas Espaciais Sao Jose dos Campos SP Brazil

ISBN: (纸本)0819409391

Certainly data integration for land-cover classification requires a non-linear system to associate satellite imagery with exogenous imagery. In this study we present some results of a neural network based methodology to provide land-cover classifications. Two approaches are investigated: (1) The monolithic integration: all required registered images are the inputs of only one back-error propagation (BEP) network. The network is trained on purpose to get the final classification. (2) The class-distributed integration: for each class a specific network learns from all satellite imageries its class characteristics. In both approaches, topographic mapping is taken into account as exogenous data.

关键词： neural networks

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Nonlinear multigrid methods of optimization in Bayesian tomographic image reconstruction

Nonlinear multigrid methods of optimization in Bayesian tomo...

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neural and stochastic methods in image and signal processing

作者： Bouman, Charles A. Sauer, Ken D. Purdue Univ. West Lafayette IN USA

ISBN: (纸本)0819409391

Bayesian estimation of transmission tomographic images presents formidable optimization tasks. Numerical solutions of this problem are limited in speed of convergence by the number of iterations required for the propagation of information across the grid. Edge-preserving prior models for tomographic images inject a nonlinear element into the Bayesian cost function, which limits the effectiveness of algorithms such as conjugate gradient, intended for linear problems. In this paper, we apply nonlinear multigrid optimization to Bayesian reconstruction of a two-dimensional function from integral projections. At each resolution, we apply Gauss-Seidel type iterations, which optimize locally with respect to individual pixel values. If the cost function is differentiable, the algorithm speeds convergence;if it is nonconvex and/or nondifferentiable, multigrid can yield improved estimates.

关键词： image reconstruction

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Digital image registration using structured genetic algorithm

Digital image registration using structured genetic algorith...

引用

neural and stochastic methods in image and signal processing

作者： Dasgupta, Dipankar McGregor, Douglas R. Univ. of Strathclyde Glasgow United Kingdom

ISBN: (纸本)0819409391

This paper describes a new genetic approach called the structured genetic algorithm (sGA) for automatic registration of digital images. The specialty of this genetic model lies primarily in its redundant genetic material and a gene activation mechanism which utilizes a multi-layered structure for the chromosome. The additional genetic material serves to retain multiple optional solution spaces in parameter optimization. The structured genetic model is applied here to minimize the registration measures in image transformations, as investigated by Fitzpatrick and Grefenstatte with the simple GA. The results demonstrate that sGA is a much faster and robust search method that is guaranteed to reach a global optimum by adaptively estimating the subspace from the maximum space during the evolutionary process. Preliminary experimental results are reported.

关键词： image analysis

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neural network for improving the performance of nearest neighbor classifiers

Neural network for improving the performance of nearest neig...

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neural and stochastic methods in image and signal processing

作者： Yan, Hong Univ. of Sydney Sydney New South Wales Australia

ISBN: (纸本)0819409391

In a nearest neighbor classifier, an input sample is assigned to the class of the nearest prototype. The decision rule is simple and robust. However, it is computationally expensive in terms of memory space and computer time to implement a nearest neighbor classifier if each training sample is stored as a prototype and used to compare with every testing sample. The performance of the classifier is degraded if only a small number of training samples are used as prototypes. An algorithm is presented in this paper for modifying the prototypes so that the classification rate can be increased. This algorithm makes use of a two-layer perceptron with one second order input. The perceptron is trained and mapped back to a new nearest neighbor classifier. It is shown that the new classifier with only a small number of prototypes can even perform better than the classifier that uses all training samples as prototypes.

关键词： neural networks

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Computer simulation of turbulent jet structure radiography

Computer simulation of turbulent jet structure radiography

引用

neural and stochastic methods in image and signal processing

作者： Kodimer, Kory A. Parnell, Lynn A. Nelson, Robert S. Papin, Patrick J. Naval Command Control and Ocean Surveillance Cent. San Diego CA USA

ISBN: (纸本)0819409391

Liquid metal combustion chambers are under consideration as power sources for propulsion devices used in undersea vehicles. Characteristics of the reactive jet are studied to gain information about the internal combustion phenomena, including temporal and spatial variation of the jet flame, and the effects of phase changes on both the combustion and imaging processes. A ray tracing program which employs simplified Monte Carlo methods has been developed for use as a predictive tool for radiographic imaging of closed liquid metal combustors. A complex focal spot is characterized by either a monochromatic or polychromatic emission spectrum. For the simplest case, the x-ray detection system is modeled by an integrating planar detector having 100% efficiency. Several simple geometrical shapes are used to simulate jet structures contained within the combustor, such as cylinders, paraboloids, and ellipsoids. The results of the simulation and real time radiographic images are presented and discussed.

关键词： image processing

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Relaxation network for a feature-driven visual attention system

Relaxation network for a feature-driven visual attention sys...

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neural and stochastic methods in image and signal processing

作者： Milanese, Ruggero Bost, Jean-Marc Pun, Thierry Univ. of Geneva Geneva Switzerland

ISBN: (纸本)0819409391

In this paper an attention module is described, which can be used by an active vision system to generate gaze changes. This module is based on a bottom-up, feature-driven analysis of the image. The results are regions of the input image which contain strange features, i.e., locations of the most `interesting' and `important' information. The method proposed for detecting such regions is based on the decomposition of the input image into a set of independent retinotopic feature maps. Each map represents the value of a certain attribute computed on a set of low-level primitives such as contours and regions. Relevant objects can be detected if the corresponding primitives have a feature value strongly different from the neighboring ones. Local comparisons of feature values are used to compute such measures of `difference' for each feature map and give rise to a corresponding set of conspicuity maps. In order to obtain a single measure of interest for each location and to make the process robust to noise, a relaxation algorithm is run on the set of conspicuity maps. A dozen iterations are sufficient to detect a binary mask identifying the attention regions. Results on real scenes are presented.

关键词： neural networks

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Unsupervised learning of contextual constraints in neural networks for simultaneous visual processing of multiple objects

Unsupervised learning of contextual constraints in neural ne...

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neural and stochastic methods in image and signal processing

作者： Marshall, Jonathan A. Univ. of North Carolina/Chapel Hill Chapel Hill NC USA

ISBN: (纸本)0819409391

A simple self-organizing neural network model, called an EXIN network, that learns to process sensory information in a context-sensitive manner, is described. EXIN networks develop efficient representation structures for higher-level visual tasks such as segmentation, grouping, transparency, depth perception, and size perception. Exposure to a perceptual environment during a developmental period serves to configure the network to perform appropriate organization of sensory data. A new anti-Hebbian inhibitory learning rule permits superposition of multiple simultaneous neural activations (multiple winners), while maintaining contextual consistency constraints, instead of forcing winner-take-all pattern classifications. The activations can represent multiple patterns simultaneously and can represent uncertainty. The network performs parallel parsing, credit attribution, and simultaneous constraint satisfaction. EXIN networks can learn to represent multiple oriented edges even where they intersect and can learn to represent multiple transparently overlaid surfaces defined by stereo or motion cues. In the case of stereo transparency, the inhibitory learning implements both a uniqueness constraint and permits coactivation of cells representing multiple disparities at the same image location. Thus two or more disparities can be active simultaneously without interference. This behavior is analogous to that of Prazdny's stereo vision algorithm, with the bonus that each binocular point is assigned a unique disparity. In a large implementation, such a NN would also be able to represent effectively the disparities of a cloud of points at random depths, like human observers, and unlike Prazdny's method.

关键词： neural networks

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Fuzzy classification of remote-sensing images: a pseudocolor representation of fuzzy partitions

Fuzzy classification of remote-sensing images: a pseudocolor...

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neural and stochastic methods in image and signal processing

作者： Casas, Josep R. Hillion, Alain Roux, Christian Torres, Luis Gasull, Antoni Univ. Politecnica de Catalunya Barcelona Spain

ISBN: (纸本)0819409391

In the field of remote sensing (RS) image classification, pattern indeterminacy due to inherent data variability is always present. Class mixture, too, is a serious handicap to conventional classifiers in order to settle proper class patterns. Fuzzy classification techniques improve the extraction of information yielded by conventional methods, i.e., statistical classification procedures, because both in the design of the classifier and when bringing out classification results, natural fuzziness present in real-world recognition processes is considered. This paper presents first the application of a fuzzy classification algorithm from Kent and Mardia to RS images, along with the analysis of the results and comparison against `hard' classifications. Secondly, we put forward one particular method to display these results (fuzzy partitions) by coding pixels' membership into a pseudocolor representation. This representation is intended to serve as an interface between fuzzy coefficients resulting from the classification process and a very natural way for humans to perceive information such as that of color mixtures.

关键词： image coding

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Parallel distributed algorithm for texture boundary localization

Parallel distributed algorithm for texture boundary localiza...

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neural and stochastic methods in image and signal processing

作者： Yhann, Stephan D. Young, Tzay S. Scripps Inst. of Oceanography Coral Gables FL USA

ISBN: (纸本)0819409391

In most instances the boundaries between textured regions are defined by the gray level contrasts which result from the local interaction between the texture elements in each region. In such cases, the boundaries can be accurately characterized by gray level edge segments. Using these edge segments to localize the texture boundary directly addresses the major problem associated with texture segmentation, namely the localization verses classification accuracy conflict. The accuracy of segmentation methods which rely only on spatially distributed properties to characterize the texture, is limited to the spacial extent of the property used. In contrast, gray level edges are significantly more localized. However, before they can be of any use, the gray level edge segments defining the texture boundary must be isolated from the edges defining the texture elements. In this paper, we define a set of properties to do this. We also incorporate these properties into a parallel distributed algorithm which is used to segment a set of sample texture images.

关键词： neural networks

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