By Layachi Bentabet, Hui Zhang (auth.), Abderrahim Elmoataz, Driss Mammass, Olivier Lezoray, Fathallah Nouboud, Driss Aboutajdine (eds.)

This booklet constitutes the refereed complaints of the fifth foreign convention on photo and sign Processing, ICISP 2012, held in Agadir, Morocco, in June 2012.
The seventy five revised complete papers provided have been rigorously reviewed and chosen from 158 submissions. The contributions are grouped into the subsequent topical sections: multi/hyperspectral imaging; photograph itering and coding; sign processing; biometric; watermarking and texture; segmentation and retieval; photo processing; development recognition.

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Extra resources for Image and Signal Processing: 5th International Conference, ICISP 2012, Agadir, Morocco, June 28-30, 2012. Proceedings

Example text

Where LL represents the Rao distance between luminance models, and LCr the Rao distance between chrominance models. The coefficient λ is calculated empirically by considering the learning set as the test set and then returning value of λ that leads to the best classification rates. In [5], we determined a closed form expression of Rao distance between two copula based pdfs as the sum of the Rao distances between the two copulas and the Rao distances between the marginal distributions. , d represents the eigenvalues of Σ1−1 Σ2 .

Intell. 18, 837–842 (1996) 10. : Fonctions de r´epartition a ` n dimensions et leurs marges. Publications de l’institut de Statistique de l’Universit´e de Paris 8, 229–231 (1959) 11. : Fast scalable retrieval of multispectral images with kullback-leibler divergence. In: Proceedings of the 17th IEEE Interational Conference on Image Processing (ICIP 2010), Hong Kong, pp. 2333–2336 (2010) 12. : Multivariate Models and Dependence Concepts. Monographs on Statistics and Applied Probability. Chapman & Hall (1997) 13.

It is to note that we consider the same aforementioned experience conditions for all methods. We can clearly observe from these results that considering multi-model for both luminance and chrominance information, beside accounting for the circular Hue component improves the classification rates, when compared with the uni modeling approach wether when components are characterized using MGGD or copula based joint Weibull models. 87% for models proposed in [3] and [4] respectively. We also deduce from Table 1 that the multi-model 1 leads to better rates, this is due to the ability of CopMGam in modeling spatial structure as stressed in [13] and the suitability of Weibul marginal for characterizing the Saturation component.

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