By Ali Movaghar, Mansour Jamzad, Hossein Asadi
This booklet constitutes the refereed complaints of the foreign Symposium, on synthetic Intelligence and sign Processing, AISP 2013, held in Tehran, Iran, in December 2013. The 35 complete papers awarded have been rigorously reviewed and chosen from 106 submissions. they're equipped in topical sections corresponding to photo processing, computer imaginative and prescient, scientific photo processing, sign processing, speech processing, normal language processing, platforms and AI functions, robotics.
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Extra info for Artificial Intelligence and Signal Processing: International Symposium, AISP 2013, Tehran, Iran, December 25-26, 2013, Revised Selected Papers
The main advantage of this approach is its simplicity. However, the closest ﬁxed vicinity area does not always accurately reﬂect information about spatial structures, especially at the border of regions. Pesaresi and Benediktsson have proposed to use morphological ﬁlters as an alternative way of performing joint classiﬁcation. The idea behind a morphological proﬁle consists in progressively simplifying the image [6, 7]. This means, that we include the geometrical information in classiﬁcation procedure.
The spatial resolution here is 20 m/pixel. It has 16 classes of interest. Results for Indiana dataset are shown in Figs. 4, 5, and 6. An SVM classiﬁcation on the original hyperspectral image were performed in Figs. 1 and 4, then the classiﬁcation map of SVM with different segmentation maps obtained by proposed techniques were appeared. Figures 2 and 4 were the obtained classiﬁcation maps, with the global accuracies (Overall accuracy, Average accuracy and Kappa factor) and Figs. 3 and 5 presented the average accuracy of approaches for different classes.
1007/978-3-319-10849-0_5 42 M. Borhani and H. Ghassemian Therefore, the including of information about spatial structures into classiﬁer should improve the classiﬁcation. The question is how to deﬁne these structures automatically? This information is not directly accessible from camera recordings as the spectral information. The second question is how to combine spectral and spatial information? Perhaps the most intuitive way to consider the spatial information is to assume that for a given pixel; its closest samples belong with a high probability to the same class, and thus classify a central pixel taking into account its closest samples.