We show how algebraic methods can be used to provide a mathematical framework suitable for the definition of multidimensional hypersurfaces in digital space, and for proofs of separation theorems. Our work is motivate...
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ISBN:
(纸本)0819413259
We show how algebraic methods can be used to provide a mathematical framework suitable for the definition of multidimensional hypersurfaces in digital space, and for proofs of separation theorems. Our work is motivated by the need for a mathematical basis to provide a strong foundation for the creation of imageprocessing algorithms in multidimensions; multidimensional images have been shown to arise naturally in areas as diverse as medical diagnosis and agricultural imaging. Whereas previous work in the area has been either combinatorial or has used the tools of point-set topology, we show how homology and cohomology groups can be defined in digital space. Our definitions are of a broad nature encompassing many of the standard adjacencies used to define digital objects. Given that in Euclidean space these groups satisfy conditions which provide for very neat proofs of separation theorems, we conjecture that an analogous theorem is true in digital space. We further show that the concept of orientability can be given a meaning in digital space more closely analogous to its classical meaning than definitions given previously in the imageprocessing literature.
Research in the last decade emphasized the potential of designing adaptive patternrecognition classifiers based on algorithms using multi-layered artificial neural nets. The greatest potential in such endeavors was a...
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ISBN:
(纸本)0819413267
Research in the last decade emphasized the potential of designing adaptive patternrecognition classifiers based on algorithms using multi-layered artificial neural nets. The greatest potential in such endeavors was anticipated to be not only in the adaptivity but also in the high-speed processing through massively parallel VLSI implementation and optical computing. Computational advantages of such algorithms have been demonstrated in a number of papers. Neural networks particularly the self-organizing types have been found quite suitable crisp pattern for clustering of unlabeled datasets. The generalization of Kohonen-type learning vector quantization (LVQ) clustering algorithm to fuzzy LVQ clustering algorithm and its equivalence to fuzzy c-means has been clearly demonstrated recently. On the other hand, Carpenter/Grossberg's ART-type self organizing neural networks have been modified to perform fuzzy clustering by a number of researches in the past few years. The performance of such neuro-fuzzy models in clustering unlabeled data patterns is addressed in this paper. A recent development of a new similarity measure and a new learning rule for updating the centroid of the winning cluster in a fuzzy ART-type neural network is also described. The capability of the above neuro-fuzzy model in better partitioning of datasets into clusters of any shape is demonstrated.
This work is motivated by the observation that Computer Vision and image Understanding processes are not very robust. Small changes in exposure parameters or in internal parameters of algorithms used can lead to signi...
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Stereo SPOT satellite images were matched by extracting and then matching linear features. The features were extracted by grouping pixels into regions upon similar gradient orientation. Lines are fitted to regions and...
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Two methods of automating the process of ocean feature tracking for estimating surface currents in coastal areas are outlined. These methods involve patternrecognition and have certain advantages over the more famili...
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Two methods of automating the process of ocean feature tracking for estimating surface currents in coastal areas are outlined. These methods involve patternrecognition and have certain advantages over the more familiar maximum cross-correlation technique of Emery et al. (1986). The first method requires three steps in its application, pattern selection, patternrecognition and geometrical calculations to determine both the cross- and the along-isotherm displacements. The second method calculates certain surface motion parameters including rotation and translation in Hough parameter space. Each method is applied to sequential AVHRR IR satellite imagery off the U.S. east coast. Finally, some of the practical problems encountered in the application of these methods are described.
Computer vision and image understanding processes are not very robust; small changes in exposure parameters or in internal parameters of algorithms can lead to significantly different results. A combination (fusion) o...
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Computer vision and image understanding processes are not very robust; small changes in exposure parameters or in internal parameters of algorithms can lead to significantly different results. A combination (fusion) of these results is profitable. The authors introduce an extended fusion concept dealing with different sources of information at external (world, scene, image) and internal (image description, scene description) levels and define the process of fusion. Each level requires its own procedure of quality measure and information fusion in order to yield a combination of components from several sources. Related work in the field is reviewed. Examples from the authors' own work cover remotesensing (improvement of classification results by fusion at the image level), medical imageprocessing of ocular fundus images (automatic control point selection by fusion at the image description level) and the interpretation of Billard scenes (object identification by fusion at the scene description level).< >
＜正＞It is very difficult for the traditional patternrecognition methods toprovide satisfactory classification performance in remotesensingpattern *** Neural Networks such as Multi-layered Net can achieve certain ...
＜正＞It is very difficult for the traditional patternrecognition methods toprovide satisfactory classification performance in remotesensingpattern *** Neural Networks such as Multi-layered Net can achieve certain improvement in this *** the remotesensingimage data is complicated and of high dimension,in this paper we discuss upon how to construct proper High-order Net to capture the high order internal structure representation of the pattern features in some direct *** results in simplified training strategies and high training ***,the classification precision of High-order Net is remarkably higher than that of Maximum Likelihood Estimation and Multi-layered BP Net.
The authors present a novel neural network model for visual information processing. The model uses a hierarchical network with local connectivity as a stem network. This network generates hypotheses about the expected...
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The authors present a novel neural network model for visual information processing. The model uses a hierarchical network with local connectivity as a stem network. This network generates hypotheses about the expected image content, and then selectively uses small neural network modules on parts of the image to check these hypotheses. The resulting neural network is able to use different spatial resolutions, and is both modular and hierarchical. Applying this model to remotely sensed image classification (Landsat TM) is described. A slightly better classification accuracy was achieved at reduced computational cost, compared to classification without the model.< >
There have been many new developments in neural network (NN) research, and many new applications have been studied. The classification of remotely sensed multispectral data using classical statistical methods has been...
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There have been many new developments in neural network (NN) research, and many new applications have been studied. The classification of remotely sensed multispectral data using classical statistical methods has been worked on for several decades. Among the multispectral data, we concentrate on the Landsat-5 Thematic Mapper (TM) image data which has been available since 1984. Using this classical maximum likelihood approach, a category is modeled as a multivariate normal distribution; however, the distribution for Landsat images is unknown. It is well known that NN approaches have the ability to classify without assuming a distribution. We apply the NN approach to the classification of Landsat TM images in order to investigate the robustness of this approach for multi-temporal data classification. The authors confirmed that the NN approach is effective for the classification even if the test data is taken at the different time.< >
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