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By Sebastian Bader, Steffen Hölldobler (auth.), Stefanos Kollias, Andreas Stafylopatis, Włodzisław Duch, Erkki Oja (eds.)
This ebook contains the court cases of the foreign convention on man made Neural Networks (ICANN 2006) hung on September 10-14, 2006 in Athens, Greece, with tutorials being awarded on September 10, the most convention occurring in the course of September 11-13 and accompanying workshops on conception, cognition and interplay hung on September 14, 2006. The ICANN convention is geared up every year via the ecu Neural community Society in cooperation with the foreign Neural community Society, the japanese Neural community Society and the IEEE Computational Intelligence Society. it's the superior eu occasion masking all themes fascinated about neural networks and comparable parts. The ICANN sequence of meetings was once initiated in 1991 and shortly grew to become the key eu amassing for specialists in those fields. In 2006 the ICANN convention was once geared up via the clever platforms Laboratory and the picture, Video and Multimedia platforms Laboratory of the nationwide Technical college of Athens in Athens, Greece. From 475 papers submitted to the convention, the foreign software Committee chosen, following a radical peer-review strategy, 208 papers for ebook and presentation to 21 typical and 10 distinct classes. the standard of the papers acquired used to be as a rule very excessive; accordingly, it was once impossible to simply accept and contain within the convention application many papers of excellent quality.
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Extra resources for Artificial Neural Networks – ICANN 2006: 16th International Conference, Athens, Greece, September 10-14, 2006. Proceedings, Part II
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Witzel. Integrating first-order logic programs and connectionist networks. Project Thesis, Technische Universit¨ at Dresden, Informatik, 2005. 32. A. Witzel. Neural-symbolic integration – constructive approaches. Master’s thesis, Technische Universit¨ at Dresden, Informatik, 2006. com Abstract. In this paper, we present a connectionist approach for detecting and precisely localizing transparent logos in TV programs. Our system automatically synthesizes simple problem-specific feature extractors from a training set of logo images, without making any assumptions or using any hand-made design concerning the features to extract or the areas of the logo pattern to analyze.
The curve in Fig. 5 shows the number of false alarms as a function of the volume threshold ThrVol. 5. For illustration purposes, Fig. 7 shows some images of the first test set with detected transparent logos. There are examples containing logos of very low contrast due to light background. Other examples show logos over a high contrasted nonuniform background which considerably falsifies the logo contours in the image region. There are also some examples of FR2 logos of different sizes at different positions in the image.
De Abstract. Knowledge based artificial networks networks have been applied quite successfully to propositional knowledge representation and reasoning tasks. However, as soon as these tasks are extended to structured objects and structure-sensitive processes it is not obvious at all how neural symbolic systems should look like such that they are truly connectionist and allow for a declarative reading at the same time. The core method aims at such an integration. It is a method for connectionist model generation using recurrent networks with feed-forward core.