NDLI: Compressing higher-order co-occurrences for texture analysis using the self-organizing map

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Proceedings of International Conference on Neural Networks (ICNN'97)

Proceedings of ICNN'95 - International Conference on Neural Networks

Volume 6 - 1995

Volume 5 - 1995

Volume 4 - 1995

Volume 3 - 1995

Volume 2 - 1995

'NEURAL-MAINE': intelligent on-line multiple sensor diagnostics for steam turbines in power generation

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Transient stability assessment: an artificial neural network approach

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New forms of combined matrices in fuzzy cognitive maps

A functional analytic approach to incremental learning in optimally generalizing neural networks

An /spl epsi/-approximation approach for global optimization with an application to neural networks

Topology analysis of data space using self-organizing feature map

Unit-growing learning optimizing the solvability condition for model-free regression

Second-order training for recurrent neural networks without teacher-forcing

Inserting background knowledge in perceptrons through modification of the learning algorithm

A hybrid neural network approach to the pricing of options

Utilization of neural network for constructing a user friendly decision support system to deal stocks

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A RPCL-CLP architecture for financial time series forecasting

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Artificial intelligence in financial markets

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Compressing higher-order co-occurrences for texture analysis using the self-organizing map

Transient discrimination in nonlinear time series using linear-phase time-delay neural networks

A method of selecting similar learning data in the prediction of time series using neural networks

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Training and evaluation of neural networks for multi-variate time series processing

Volume 1 - 1995

IEEE International Conference on Neural Networks

IEEE 1988 International Conference on Neural Networks

Proceedings of International Conference on Neural Networks (ICNN'96)

Compressing higher-order co-occurrences for texture analysis using the self-organizing map

Content Provider	IEEE Xplore Digital Library
Author	Oja, E. Valkealahti, K.
Copyright Year	1995
Description	Author affiliation: Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Espoo, Finland (Oja, E.; Valkealahti, K.)
Abstract	Texture analysis is useful in many computer vision applications. One of the most useful texture feature sets is based on second-order co-occurrences of gray levels of pixel pairs. An extension of the co-occurrences to higher orders is prevented by the large size of the multidimensional arrays. We quantize the higher-order co-occurrences by the self-organizing map, called the co-occurrence map, which allows a flexible two-dimensional representation of co-occurrence histograms of any order. Experiments with natural gray level and color textures show that the method is effective in texture classification and segmentation.
Starting Page	1160
Ending Page	1164
File Size	612337
Page Count	5
File Format	PDF
ISBN	0780327683
DOI	10.1109/ICNN.1995.487689
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-11-27
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Neural networks Histograms Information analysis Computer vision Vector quantization Laboratories Information science Application software Digital images
Content Type	Text
Resource Type	Article

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