The Growing Neural Gas (GNG) patternrecognition algorithm is an unsupervised algorithm which inserts nodes into the state space of the training data. Observations of the behavior of the algorithm lead to the hypothes...
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ISBN:
(纸本)0780390482
The Growing Neural Gas (GNG) patternrecognition algorithm is an unsupervised algorithm which inserts nodes into the state space of the training data. Observations of the behavior of the algorithm lead to the hypothesis that this method may be an efficient pre-classification clustering algorithm for data in highly discrete state spaces, as in satellite remotesensingimages. The GNG algorithm was used to train a network using a Landsat image from Wyoming. The initial results of this investigation were extremely positive. The image derived from the trained GNG network is difficult to distinguish from the source image. Preliminary statistical results also indicate a high degree of correlation between the source and resultant images.
A huge number of clustering methods have been applied to many different kinds of data set including multivariate images, such as magnetic resonance images (MRI) and remotesensingimages. However, not many methods inc...
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A huge number of clustering methods have been applied to many different kinds of data set including multivariate images, such as magnetic resonance images (MRI) and remotesensingimages. However, not many methods include spatial information of the image data. In this tutorial, the major types of clustering techniques are summarized. Particular attention will be devoted to the extension of clustering techniques to take into account both spectral and spatial information of the multivariate image data. General guidelines for the optimal use of these algorithms are given. The application of pre- and post-processing methods is also discussed. (c) 2004 Elsevier B.V All rights reserved.
This paper imported wavelet analysis and wavelet descriptor into the building recognition of high resolution satellite remotesensingimage, and brought forward the building recognition method based on wavelet descrip...
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We present a new approach based on Discriminant Analysis to map a high dimensional image feature space onto a subspace which has the following advantages: 1. each dimension corresponds to a semantic likelihood, 2. an ...
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ISBN:
(纸本)0780391349
We present a new approach based on Discriminant Analysis to map a high dimensional image feature space onto a subspace which has the following advantages: 1. each dimension corresponds to a semantic likelihood, 2. an efficient and simple multiclass classifier is proposed and 3. it is low dimensional. This mapping is learnt from a given set of labeled images with a class groundtruth. In the new space a classifier is naturally derived which performs as well as a linear SVM. We will show that projecting images in this new space provides a database browsing tool which is meaningful to the user. Results are presented on a remotesensing database with eight classes, made available online. The output semantic space is a low dimensional feature space which opens perspectives for other recognition tasks.
Many applications such as image compression, pre-processing or segmentation require some information from the regions composing an image. The main objective of this paper is to define a methodology to extract some loc...
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ISBN:
(纸本)3540288333
Many applications such as image compression, pre-processing or segmentation require some information from the regions composing an image. The main objective of this paper is to define a methodology to extract some local information from an image. Each region is characterized in terms of homogeneity (region composed with the same grey-level or a single texture) and its type (textured or uniform). The decision criterion is based on the use of classical texture attributes (cooccurrence matrix and grey-levels moments) and a support vector machine in order to realize the fusion of the different attributes. We then characterize each region considering its type by appropriate features.
In the paper, experiments and analysis of three pixel-based fusion methods had been discussed. The fusion methods include IHS, PCA and Brovey transform method. The fusion experiments were carried out in two circs, tha...
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ISBN:
(纸本)0819456187
In the paper, experiments and analysis of three pixel-based fusion methods had been discussed. The fusion methods include IHS, PCA and Brovey transform method. The fusion experiments were carried out in two circs, that is, between Landsat TM multi-spectral data and SPOT-4 Pan data, Landsat TM multi-spectral data and IRS-C Pan data. From the fusion results, the definition of all fusion images were improved greatly compared to the Landsat TM image. Especially the linear ground objects are much clear, such as the roads, the residents, the bridges, etc. According to the fusion between Landsat TM data and SPOT-4 Pan data, the Brovey fusion method was the best one. The PCA fusion method was better than the IHS fusion method. According to the fusion between Landsat TM data and IRS-C Pan data, the Brovey fusion method was also the best one. But the IHS fusion method was better than the PCA fusion method. Maximum likelihood method of classification was carried out on the fusion result, and classification accuracy of the classification results were evaluated. From the evaluation result, it can be concluded that classification accuracy of the Brovey fusion result is the highest between Landsat TM data and IRS-C Pan data. Classification accuracy of the IHS fusion result is the highest between Landsat TM data and SPOT-4 Pan data.
In the fight that there is no deducing ability or learning ability of traditional hypertext of multimedia computing, this paper provides a new method of pattern-matching based on thinking in terms of images through de...
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ISBN:
(纸本)0780388402
In the fight that there is no deducing ability or learning ability of traditional hypertext of multimedia computing, this paper provides a new method of pattern-matching based on thinking in terms of images through developing the pattrn-matching in the Artificial Intelligence field, proposes a corresponding algorithm. and the learning problem of multimedia computing based on model recognition is discussed.
Edge detection is an important topic in imageprocessing and a main tool in patternrecognition and image segmentation. Many edge detection techniques are available in the literature. 'A number of recent edge dete...
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Edge detection is an important topic in imageprocessing and a main tool in patternrecognition and image segmentation. Many edge detection techniques are available in the literature. 'A number of recent edge detectors are multiscale and include three main processing steps: smoothing, differentiation and labeling' (Ziau and Tabbone, 1997). This paper, presents a proposed method which is suitable for edge detection in images. This method is based on the use of the clustering algorithms (Self-Organizing Map (SOM), K-Means) and a gray scale edge detector (Canny, Generalized Edge Detector (GED)). It is shown that using the grayscale edge detectors may miss some parts of the edges which can be found using the proposed method.
This article presents the results of an unsupervised segmentation algorithm in multispectral images. The algorithm uses a minimization function which takes into account each band intensity information together with gl...
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ISBN:
(纸本)3540261540
This article presents the results of an unsupervised segmentation algorithm in multispectral images. The algorithm uses a minimization function which takes into account each band intensity information together with global edge criterion. Due to the unsupervised nature of the procedure, it can adapt itself to the huge variability of intensities and shapes of the image regions. Results shows the effectiveness of the method in multispectral fruit inspection applications and in remotesensing tasks.
In this paper we introduced a parallel file system based on the spatial information object storage, the PIPFS system. PIPFS is a special-purpose parallel file system which designed in view of the remotesensingimage ...
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ISBN:
(数字)9783540322467
ISBN:
(纸本)354029810X
In this paper we introduced a parallel file system based on the spatial information object storage, the PIPFS system. PIPFS is a special-purpose parallel file system which designed in view of the remotesensingimageprocessing. It uses the server/client pattern and bases on the metadata mechanism. It simultaneously accesses disks on several nodes for application I/O operations, which improves the efficiency of the operation on large scale data. A high performance is shown on high-data-complexity application, such as remotesensingimageprocessing.
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