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Titlebook: Computer Vision in Human-Computer Interaction; ECCV 2004 Workshop o Nicu Sebe,Michael Lew,Thomas S. Huang Conference proceedings 2004 Sprin

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21#
發(fā)表于 2025-3-25 04:22:08 | 只看該作者
Location-Based Information Support System Using Multiple Cameras and LED Light Sources with the Compin an event, a?method for calibration of multiple cameras and light sources which is easy to use for inexperienced persons and to setup quickly is needed. To calibrate multiple cameras and LED light sources simultaneously, we employ a calibration technique for the multiple cameras based on the self-calibration method.
22#
發(fā)表于 2025-3-25 10:36:17 | 只看該作者
23#
發(fā)表于 2025-3-25 15:21:56 | 只看該作者
24#
發(fā)表于 2025-3-25 19:48:47 | 只看該作者
Exploring Interactions Specific to Mixed Reality 3D Modeling Systemsmore intuitive input methods than the conventional desktop modelers or Virtual Reality (VR) immersive systems, our prototype system enables the user to have an accurate perception of the shapes modeled. Thus, the user can recognize correctly the spacial location and real sizes of the models.
25#
發(fā)表于 2025-3-25 21:06:17 | 只看該作者
An AR Human Computer Interface for Object Localization in a?Cognitive Vision Framework describes the basic idea of the 3D cursor. In addition to the mobile system, at least one camera is used to obtain different views of an object which could be employed to improve e.g.?view based object recognition. Accuracy of the 3D cursor for pointing in a?scene coordinate system is evaluated experimentally.
26#
發(fā)表于 2025-3-26 00:53:19 | 只看該作者
27#
發(fā)表于 2025-3-26 04:50:13 | 只看該作者
0302-9743 Prague, Czech Republic in May 2004...The 19 revised full papers presented together with an introductory overview and an invited paper were carefully reviewed and selected from 45 submissions. The papers are organized in topical sections on human-robot interaction, gesture recognition and body tracki
28#
發(fā)表于 2025-3-26 11:17:25 | 只看該作者
29#
發(fā)表于 2025-3-26 12:41:31 | 只看該作者
30#
發(fā)表于 2025-3-26 19:39:55 | 只看該作者
Final comments and open problems, for a kind of typical sequence. Then Threshold Model is used to segment and recognize continuous sequence. The method has been tested on unsupervised event analysis in video surveillance and model learning of athlete actions.
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