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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing II"

共 707 条记录，以下是581-590 订阅

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Enhanced significance-linked connected component analysis for high performance error resilient wavelet image coding

Enhanced significance-linked connected component analysis fo...

引用

15th International conference on pattern recognition (ICPR-2000)

作者： Vass, J Zhuang, XH Eyeball com Network Inc W Vancouver BC V7T 1A2 Canada

ISBN: (纸本)0769507514

In the paper, error resilient coding strategies are proposed and integrated with the high performance significance-linked connected component analysis (SLCCA) wavelet image codec. First, the source bitstream is packetized so that the error propagation within the bitstream is minimized. Second, hierarchical resynchronization is applied, i.e., resynchronization markers are inserted in the bitstream after each bit-plane and possibly per each scale Or subband. Third, the significance-links that exploit the cross-scale dependency of wavelet coefficients are reduced. Finally, bit-plane-wise unequal error protection is applied, with the parity bits being exponentially distributed among bit-planes. Extensive performance evaluation over a binary symmetric channel demonstrates the high error resilience and efficiency of the integrated joint source-channel coding strategies with the SLCCA source codec.

关键词： image enhancement

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An architecture for content-based retrieval of remote sensing images

An architecture for content-based retrieval of remote sensin...

引用

2000 IEEE Internatinal conference on Multimedia and Expo (ICME 2000)

作者： Del Val Cura, L.M. Leite, N.J. Medeiros, C.B. Institute of Computing Campinas State University Campinas Brazil

This paper presents an architecture of a system for content-based retrieval of remote sensing images (RSI). This architecture relies on combining results from image processing, pattern recognition and Multimedia Databases. RSI retrieval from a database is based on the notion of image similarity. A novel aspect in the paper is that the degree of similarity among images is refined during a query session, and is computed dynamically based on user-provided texture and color image patterns, as well as information on how images were pre-processed before being stored in the database.

关键词： Content based retrieval

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Wavelet compression and data fusion: An investigation into the automatic classification of urban environments using colour photography and laser scanning data

Wavelet compression and data fusion: An investigation into t...

引用

15th International conference on pattern recognition (ICPR-2000)

作者： Kiema, JBK Univ Karlsruhe Inst Photogrammetry & Remote Sensing IPF D-76128 Karlsruhe Germany

ISBN: (纸本)0769507514

The field of wavelets has opened up new opportunities;for the compression of satellite sensor imagery. This paper examines the influence of wavelet compression on the automatic classification of urban environments. Ail borne laser scanning data is introduced as an additional channel alongside the spectral channels of colour infrared imagery. This effectively integrates the local height and multi-spectral information sour-ces. To incorporate conte,ut information, the feature base is expanded to include both spectral and non-spectral features. A maximum likelihood classification approach is then applied. It is demonstrated that the classification of urban scenes is considerably improved by fusing multi-spectral and geometric data sets. The fused imagery is then systematically compressed (channel by channel) at compression rates ranging from 5 to 100 using a wavelet-based algorithm. The compressed imagery is then classified using the approach described here-above. Analysis of the results obtained indicates that a compression rate of up to 20 can convieniently be employed without adversely affecting the segmentation results.

关键词： Maximum likelihood

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A two-step fuzzy-Bayesian classification for high dimensional data

A two-step fuzzy-Bayesian classification for high dimensiona...

引用

15th International conference on pattern recognition, ICPR 2000

作者： Mostafa, Mostafa G.-H. Perkins, Timothy C. Farag, Aly A. Computer Vision and Image Processing Laboratory University of Louisville KY40292 United States

ISBN: (纸本)0769507506

The goal of this paper is twofold. First, we present a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. Comparisons with conventional FCM clustering technique and Bayesian classification technique are also presented. Second, we present a two-step classifier in which the proposed SFCM and Bayesian algorithms ore used in a cooperative way such that classification results of the SFCM algorithm are used to compute the prior probabilities required for the Bayesian classifier. Classification results of the three algorithms are presented on simulated and real remote sensing multispectral data. The results show improvement in the classification accuracy and reliability using the two-step algorithm. © 2000 IEEE.

关键词： Clustering algorithms

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Compression method for real-time systems of remote sensing

Compression method for real-time systems of remote sensing

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International conference on pattern recognition

作者： M. Gashnikov N. Glumov V. Sergeyev Imabe Processing Systems Institute USA

ISBN: (纸本)0769507506

In this paper the method of image compression is presented. It is designed for data processing in real-time systems of remote sensing. In the midpoint there are compression algorithm based on component transformation with pixel interpolation and algorithm of stabilization of encoded image forming speed, which provide high compression ratio, stable speed of an output data flow and controlled error of image reconstruction.

关键词： Real time systems remote sensing image coding Process design Data processing Compression algorithms Pixel Interpolation Error correction image reconstruction

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An architecture for content-based retrieval of remote sensing images

An architecture for content-based retrieval of remote sensin...

引用

IEEE International conference on Multimedia and Expo (ICME)

作者： L.M. del Val Cura N.J. Leite C.B. Medeiros Institute of Computing Campinas State University Brazil Computer Science Department University of Havana Cuba

This paper presents an architecture of a system for content-based retrieval of remote sensing images (RSI). This architecture relies on combining results from image processing, pattern recognition and multimedia databases. RSI retrieval from a database is based on the notion of image similarity. A novel aspect in the paper is that the degree of similarity among images is refined during a query session, and is computed dynamically based on user-provided texture and color image patterns, as well as information on how images were pre-processed before being stored in the database.

关键词： Content based retrieval remote sensing image retrieval image databases Computer architecture image processing pattern recognition Multimedia databases Information retrieval Color

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Fully unsupervised fuzzy clustering with entropy criterion

Fully unsupervised fuzzy clustering with entropy criterion

引用

International conference on pattern recognition

作者： A. Lorette X. Descombes J. Zerubia INRIA Ariana Joint group CNRS/INRIA/INSA France

ISBN: (纸本)0769507506

We present a fully unsupervised clustering algorithm in order to overcome the problem of a priori defining the number of clusters. We propose to optimize an objective function which is the sum of two terms. The first one is a generalization of intra-cluster distance within the framework of fuzzy sets. The second one is an entropy term. Our clustering algorithm has been applied to the problem of clustering both remote sensing data and medical images.

关键词： Entropy Fuzzy sets image processing Optimization methods Clustering algorithms remote sensing Biomedical imaging image analysis Parameter estimation Iterative methods

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A two-step fuzzy-Bayesian classification for high dimensional data

A two-step fuzzy-Bayesian classification for high dimensiona...

引用

International conference on pattern recognition

作者： M.G.-H. Mostafa T.C. Perkins A.A. Farag Computer Vision and Image Processing Laboratory University of Louisville KY USA

ISBN: (纸本)0769507506

The goal of this paper is twofold. First, we present a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. Comparisons of the conventional FCM clustering technique and Bayesian classification technique are also presented. Next, we present a two-step classifier in which the proposed SFCM and Bayesian algorithms are used in a cooperative way such that classification results of the SFCM algorithm are used to compute the prior probabilities required for the Bayesian classifier. Classification results of the three algorithms are presented on simulated and real remote sensing multispectral data. The results obtained show improvements in the classification accuracy and reliability using the two-step algorithm.

关键词： Clustering algorithms Bayesian methods remote sensing Uncertainty High-resolution imaging Hyperspectral sensors Hyperspectral imaging Fuzzy logic image segmentation Classification algorithms

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Optimal line detector

Optimal line detector

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International conference on pattern recognition

作者： Djemel Ziou Department de Mathematiques et d informatique Universite de Sherbrooke QUE Canada

ISBN: (纸本)0769507506

An optimal line detector for the one-dimensional case is derived from Canny's criteria (1986). The detector is extended to the two-dimensional case by operating separately in the x and y directions. An efficient implementation using an infinite impulse response (iiR) filter is provided. This implementation has an additional advantage that increasing the filter scale affects neither temporal nor spatial complexity. Our detector is faster than the Gaussian used by Steger (1998); e.g., when the scale is 3 our detector is 33 times faster. Experimental results using real images demonstrate the validity of the algorithm.

关键词： Detectors image edge detection iiR filters White noise Signal processing Lighting remote sensing Biomedical imaging Eigenvalues and eigenfunctions Large-scale systems

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A methodology for woody crop location and discrimination in remote sensing

A methodology for woody crop location and discrimination in ...

引用

10th International conference on image Analysis and processing, ICIAP 1999

作者： Garcia-Consuegra, J. Cisneros, G. Martinez, A. Castilla-La Mancha University Campus Universitario s/n 02071 Albacete Spain Grupo de Tratamiento de Imágenes Universidad Politécnica de Madrid Ciudad Universitaria 28040 Madrid Spain Castilla-La Mancha University Campus Universitario s/n 02071 Albacete Spain

ISBN: (纸本)0769500404

In this paper we provide a solution to a common problem in remote sensing when woody crops (almond and olive fields, vineyards, and so on) must be located and discriminated. Experience has taught us that, currently, the usual way to accomplish this task is with a photointerpreter digitizing on the images. Our solution is based on a widely used and accurate texture segmentation such as multichannel filtering (MCF) based on Gabor functions (GF) and on the ECHO (extraction and classification of homogeneous objects) approach to carry out the segmentation process. The test bed consisted of high-spatial-resolution images such as aerial photography and those images scanned by the Daedalus-1268 multispectral scanner on board an airborne platform. Favorable results were yielded with both types of images, in particular the discrimination between almond and olive fields was carried out in the case of multispectral images. © 1999 IEEE.

关键词： remote sensing

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