0000000000276794

AUTHOR

Janvier Fotsing

showing 3 related works from this author

SAR Image Classification Combining Structural and Statistical Methods

2011

The main objective of this paper is to develop a new technique of SAR image classification. This technique combines structural parameters, including the Sill, the slope, the fractal dimension and the range, with statistical methods in a supervised image classification. Thanks to the range parameter, we define the suitable size of the image window used in the proposed approach of supervised image classification. This approach is based on a new way of characterising different classes identified on the image. The first step consists in determining relevant area of interest. The second step consists in characterising each area identified, by a matrix. The last step consists in automating the pr…

010504 meteorology & atmospheric sciencesContextual image classificationbusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONProcess (computing)Pattern recognition02 engineering and technology01 natural sciencesFractal dimensionImage (mathematics)Range (mathematics)Matrix (mathematics)Fractal[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligenceVariogrambusinessComputingMilieux_MISCELLANEOUS0105 earth and related environmental sciencesMathematics
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Apport du variogramme dans la classification d'images satellitaires radar RSO,

2012

International audience

[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][ INFO.INFO-TI ] Computer Science [cs]/Image ProcessingComputingMilieux_MISCELLANEOUS
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A Parallel Approach for Statistical Texture Parameter Calculation

2014

This chapter focusses on the development of a new image processing technique for the processing of large and complex images, especially SAR images. We propose here a new and effective approach that outperforms the existing methods for the calculation of high order textural parameters. With a single processor, this approach is about \(256^{n-1}\) times faster than the co-occurrence matrix approach considered as classical, where \(n\) is the order of the textural parameter for a 256-gray scales image. In a parallel environment made of N processor, this performance can almost be multiply by the factor N. Our approach is based on a new modeling of textural parameters of a generic order \(n>1\) …

Matrix (mathematics)Texture (cosmology)Computer Science::Computer Vision and Pattern RecognitionImage (category theory)ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONOrder (ring theory)ByteImage processingDevelopment (differential geometry)Space (mathematics)AlgorithmMathematics
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