Active Media Technology: 9th International Conference, AMT by Andrzej Jankowski, Andrzej Skowron, Roman Swiniarski

By Andrzej Jankowski, Andrzej Skowron, Roman Swiniarski (auth.), Tetsuya Yoshida, Gang Kou, Andrzej Skowron, Jiannong Cao, Hakim Hacid, Ning Zhong (eds.)

This publication constitutes the refereed complaints of the ninth overseas convention on lively Media expertise, AMT 2013, held in Maebashi, Japan, in October 2013. The 26 revised complete papers offered including 2 brief papers, sixteen workshop papers, and 12 targeted consultation papers have been rigorously reviewed and chosen from a number of submissions. The papers are equipped in topical sections on lively computers, interactive platforms, and alertness of AMT established platforms; lively media laptop studying and knowledge mining suggestions; AMT for semantic internet, social networks, and cognitive foundations. also, the most subject of the workshop papers is: intelligence for strategic foresight; and for the distinctive consultation papers: applied sciences and theories of narrative; evolutionary computation and its program; and clever media seek techniques.

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Extra info for Active Media Technology: 9th International Conference, AMT 2013, Maebashi, Japan, October 29-31, 2013, Proceedings

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Gesture functions Number of Finger Function One finger Mouse cursor The user ‘s performance Fingertip indicates the mouse position Two fingers Mouse click Second finger indicates the mouse clicked Three fingers Mouse wheel Scroll the screen by moving fingers Fig. 1. Recognizing the fingertips We have chosen the C# as the implementation language for our system [15]. This is the language recommended for programming with the Kinect. We use OpenNI API to recognize the fingers of the user [16]. OpenNI API acquires the skeleton information of a human body (the yellow outline) as shown in Fig.

In: International Confernce on Automatic Face and Gesture Recognition, pp. 614–619 (2004) 7. : Analysis of rotational robustness of hand detection with a ViolaJones detector. In: IAPR International Conference of Pattern Recognition, vol. 3, pp. 107–110 (2004) 8. : Hand gesture detection and segmentation based on difference background image with complex background. In: International Conference on Embedded Software and Systems, pp. 338–343 (2008) 9. : Hand pose detection for vision-based gesture interfaces.

F 3. Shamisen clef & positioning Fig. Table 1. String tunings An Au utomated Musical Scoring System for Tsugaru Shamisen 27 (3) Correspondence to thee musical notation. Table 2 shows the partial relationsship between the Western musiical notation and Bunka score. In Bunka sore, notes are shown as Tsubo numbers th hat are held and rest are expressed in black dot. Lengthh of notes and rest are expressed d by the location of bars. Table 2. 3 Indications of the Musical M Instrument (Tsugaru Shamisen) Symbols unique to Tsugaru u Shamisen are on sale in various guides [2].

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