Intelligence Semantics

Artificial Intelligence and Signal Processing: International by Ali Movaghar, Mansour Jamzad, Hossein Asadi

By Ali Movaghar, Mansour Jamzad, Hossein Asadi

This ebook constitutes the refereed court cases of the foreign Symposium, on synthetic Intelligence and sign Processing, AISP 2013, held in Tehran, Iran, in December 2013. The 35 complete papers offered have been rigorously reviewed and chosen from 106 submissions. they're prepared in topical sections resembling photograph processing, laptop imaginative and prescient, scientific snapshot processing, sign processing, speech processing, traditional language processing, platforms and AI functions, robotics.

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Additional resources for Artificial Intelligence and Signal Processing: International Symposium, AISP 2013, Tehran, Iran, December 25-26, 2013, Revised Selected Papers

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In this paper we first show that the learning based contour detection method has better results in watermark robustness compared to the traditional Canny edge detection. Then we show that the learning based contour detection method has reliable performance when zero-watermarking highly similar images (images that are taken from different angle views). With this new algorithm we can watermark similar images. The produced keys are distinguishable enough for protecting the ownership of similar images.

Hyperspectral imaging leads to the potential of a more accurate classification and analysis. However, as the spectral dimension increases, image analysis becomes more complex, and methods developed for multispectral images actually fail when confronted to hyperspectral data. Therefore, to take full advantage of the rich information in spectral dimension, new algorithmic developments are required. Hyperspectral remote sensed image classification is an important task in many application domains. First attempts to classify HS data were designed to assign each pixel to one of the classes based on its spectrum.

Extending the depth of focus used multiple cameras. Therefore, the multi-focus image fusion technique is desirable to fuse a set of images. Recently, multi-resolution analysis has become a widely adopted technique to perform image fusion [3–7]. The algorithms based on multi-scale decompositions are more popular. The key step is to perform a multi-scale transform on each source image, and then performed coefficient combination, the process of merge the coefficient in an appropriate way in order to obtain the best quality in the fused image.

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