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21.
Hyung Seok Kim Chris Joslin Thomas Di Giacomo Stephane Garchery Nadia Magnenat-Thalmann 《The Visual computer》2006,22(5):332-345
The goal of this research was the creation of an adaptation mechanism for the delivery of three-dimensional content. The adaptation of content, for various network and terminal capabilities – as well as for different user preferences, is a key feature that needs to be investigated. Current state-of-the art research of the adaptation shows promising results for specific tasks and limited types of content, but is still not well-suited for massive heterogeneous environments. In this research, we present a method for transmitting adapted three-dimensional content to multiple target devices. This paper presents some theoretical and practical methods for adapting three-dimensional content, which includes shapes and animation. We also discuss practical details of the integration of our methods into MPEG-21 and MPEG-4 architectures. 相似文献
22.
We perform continuous collision detection (CCD) for articulated bodies where motion is governed by an adaptive dynamics simulation.
Our algorithm is based on a novel hierarchical set of transforms that represent the kinematics of an articulated body recursively,
as described by an assembly tree. The performance of our CCD algorithm significantly improves as the number of active degrees
of freedom in the simulation decreases. 相似文献
23.
Ducasse Stephane Pollet Damien 《IEEE transactions on pattern analysis and machine intelligence》2009,35(4):573-591
To maintain and understand large applications, it is important to know their architecture. The first problem is that unlike classes and packages, architecture is not explicitly represented in the code. The second problem is that successful applications evolve over time, so their architecture inevitably drifts. Reconstructing the architecture and checking whether it is still valid is therefore an important aid. While there is a plethora of approaches and techniques supporting architecture reconstruction, there is no comprehensive software architecture reconstruction state of the art and it is often difficult to compare the approaches. This paper presents a state of the art in software architecture reconstruction approaches. 相似文献
24.
Julien Ah-Pine Marco Bressan Stephane Clinchant Gabriela Csurka Yves Hoppenot Jean-Michel Renders 《Multimedia Tools and Applications》2009,42(1):31-56
This paper deals with multimedia information access. We propose two new approaches for hybrid text-image information processing
that can be straightforwardly generalized to the more general multimodal scenario. Both approaches fall in the trans-media
pseudo-relevance feedback category. Our first method proposes using a mixture model of the aggregate components, considering
them as a single relevance concept. In our second approach, we define trans-media similarities as an aggregation of monomodal
similarities between the elements of the aggregate and the new multimodal object. We also introduce the monomodal similarity
measures for text and images that serve as basic components for both proposed trans-media similarities. We show how one can
frame a large variety of problem in order to address them with the proposed techniques: image annotation or captioning, text
illustration and multimedia retrieval and clustering. Finally, we present how these methods can be integrated in two applications:
a travel blog assistant system and a tool for browsing the Wikipedia taking into account the multimedia nature of its content.
Dr. Julien Ah-Pine joined the XRCE Grenoble as Research Engineer in 2007. He is part of the Textual and Visual Pattern Analysis group and his current research activities are related to multi-modal information retrieval and machine learning. He received his PhD degree in mathematics from Pierre and Marie Curie University (University of Paris 6). From 2003 to 2007, he was with Thales Communications, working on relational analysis, data and text mining methods and social choice theory. Dr. Marco Bressan is Area Manager of the Textual and Visual Pattern Analysis area at Xerox Research Centre Europe. His main research interests are statistical learning and classification; image and video semantic scene understanding; image enhancement and aesthetics; object detection and recognition, particularly when dealing with uncontrolled environments. Prior to Xerox, several of his contributions in these fields were applied to a variety of scenarios including biometric solutions, data mining, CBIR and industrial vision. Dr. Bressan holds a BA in Applied Mathematics from the University of Buenos Aires, a M.Sc. in Computer Vision from the Computer Vision Centre in Spain and a Ph.D. in Computer Science and Artificial Intelligence from the Autonomous University of Barcelona. He is an active member of the network of Argentinean researchers abroad and one of the founders of the network of computer vision and cognitive science researchers. Stephane Clinchant is Ph.D. Student at University Joseph Fourier (Grenoble, France) and at the Xerox Research Centre Europe, that he joined in 2005. Before joining XRCE, Stephane obtained a Master Degree in Computer Sciences in 2005 from the Ecole Nationale Superieure d’Electrotechnique, d’Informatique, d’Hydraulique et des Telecommunications (France). His current research interests mainly focus on Machine Learning for Natural Language Processing and Multimedia Information Access. Dr. Gabriela Csurka is a research scientist in the Textual and Visual Pattern Analysis team at Xerox Research Centre Europe (XRCE). She obtained her Ph.D. degree (1996) in Computer Science from University of Nice Sophia - Antipolis. Before joining XRCE in 2002, she worked in fields such as stereo vision and projective reconstruction at INRIA (Sophia Antipolis, Rhone Alpes and IRISA) and image and video watermarking at University of Geneva and Institute Eurécom, Sophia Antipolis. Author of several publications in main journals and international conferences, she is also an active reviewer both for journals and conferences. Her current research interest concerns the exploration of new technologies for image content and aesthetic analysis, cross-modal image categorization and semantic based image segmentation. Yves Hoppenot is in charge of the development and integration of new technologies in our European research Technology Showroom. He is a software expert for the production, office and services sectors. Yves joined the Xerox Research Centre Europe in 2001. He graduated from the Ecole National Superieure des Telecommunications, Brest in France, and received a Master of Science degree from the Tampere University of Technology in Finland. Dr. Jean-Michel Renders joined the XRCE Grenoble as Research Engineer in 2001. His current research interests mainly focus on Machine Learning techniques applied to Statistical Natural Language Processing and Text Mining. Before joining XRCE, Jean-Michel obtained a PhD in Applied Sciences from the University of Brussels in 1993. He started his research activities in 1988, in the field of Robotics Dynamics and Control. Then, he joined the Joint Research Center of the European Communities to work on biologial metaphors (Genetic Algorithms, Neural Networks and Immune Networks) applied to process control. After spending one year as Visiting Scientist at York University (England), he spent 4 years applying Artificial Intelligence and Machine Learning Techniques in Industry (Tractebel - Suez). Then, he worked as Data Mining Senior Consultant and led projects in most major Belgian banks and utilities. 相似文献
Gabriela CsurkaEmail: |
Dr. Julien Ah-Pine joined the XRCE Grenoble as Research Engineer in 2007. He is part of the Textual and Visual Pattern Analysis group and his current research activities are related to multi-modal information retrieval and machine learning. He received his PhD degree in mathematics from Pierre and Marie Curie University (University of Paris 6). From 2003 to 2007, he was with Thales Communications, working on relational analysis, data and text mining methods and social choice theory. Dr. Marco Bressan is Area Manager of the Textual and Visual Pattern Analysis area at Xerox Research Centre Europe. His main research interests are statistical learning and classification; image and video semantic scene understanding; image enhancement and aesthetics; object detection and recognition, particularly when dealing with uncontrolled environments. Prior to Xerox, several of his contributions in these fields were applied to a variety of scenarios including biometric solutions, data mining, CBIR and industrial vision. Dr. Bressan holds a BA in Applied Mathematics from the University of Buenos Aires, a M.Sc. in Computer Vision from the Computer Vision Centre in Spain and a Ph.D. in Computer Science and Artificial Intelligence from the Autonomous University of Barcelona. He is an active member of the network of Argentinean researchers abroad and one of the founders of the network of computer vision and cognitive science researchers. Stephane Clinchant is Ph.D. Student at University Joseph Fourier (Grenoble, France) and at the Xerox Research Centre Europe, that he joined in 2005. Before joining XRCE, Stephane obtained a Master Degree in Computer Sciences in 2005 from the Ecole Nationale Superieure d’Electrotechnique, d’Informatique, d’Hydraulique et des Telecommunications (France). His current research interests mainly focus on Machine Learning for Natural Language Processing and Multimedia Information Access. Dr. Gabriela Csurka is a research scientist in the Textual and Visual Pattern Analysis team at Xerox Research Centre Europe (XRCE). She obtained her Ph.D. degree (1996) in Computer Science from University of Nice Sophia - Antipolis. Before joining XRCE in 2002, she worked in fields such as stereo vision and projective reconstruction at INRIA (Sophia Antipolis, Rhone Alpes and IRISA) and image and video watermarking at University of Geneva and Institute Eurécom, Sophia Antipolis. Author of several publications in main journals and international conferences, she is also an active reviewer both for journals and conferences. Her current research interest concerns the exploration of new technologies for image content and aesthetic analysis, cross-modal image categorization and semantic based image segmentation. Yves Hoppenot is in charge of the development and integration of new technologies in our European research Technology Showroom. He is a software expert for the production, office and services sectors. Yves joined the Xerox Research Centre Europe in 2001. He graduated from the Ecole National Superieure des Telecommunications, Brest in France, and received a Master of Science degree from the Tampere University of Technology in Finland. Dr. Jean-Michel Renders joined the XRCE Grenoble as Research Engineer in 2001. His current research interests mainly focus on Machine Learning techniques applied to Statistical Natural Language Processing and Text Mining. Before joining XRCE, Jean-Michel obtained a PhD in Applied Sciences from the University of Brussels in 1993. He started his research activities in 1988, in the field of Robotics Dynamics and Control. Then, he joined the Joint Research Center of the European Communities to work on biologial metaphors (Genetic Algorithms, Neural Networks and Immune Networks) applied to process control. After spending one year as Visiting Scientist at York University (England), he spent 4 years applying Artificial Intelligence and Machine Learning Techniques in Industry (Tractebel - Suez). Then, he worked as Data Mining Senior Consultant and led projects in most major Belgian banks and utilities. 相似文献
25.
Ayoub Moradi Laurent Metivier Olivier de Viron Stephane Calmant Catherine Mering 《International journal of remote sensing》2013,34(16):6060-6075
Moderate-Resolution Imaging Spectroradiometer (MODIS) optical and infrared data are used to monitor changes in the Caspian Sea coastline. The information extracted from MODIS images is converted into total water volume and mean lake level by combining a digital elevation model (DEM) with remote-sensing data. The elevation estimates were enhanced by reprocessing the MODIS data at the sub-pixel scale. The water volume variations estimated from MODIS data along with DEM are compared to other estimations derived from altimetry data sets, and show fair agreement. 相似文献
26.
William S. Sheldrick Michael Wachhold Stephane Jobic Raymond Brec Enric Canadell 《Advanced materials (Deerfield Beach, Fla.)》1997,9(8):669-675
The crystal and electronic structure of several recently reported tellurium-rich alkali metal tellurides is discussed. Some of these materials contain defective square planar tellurium lattices, which on the basis of band structure calculations are expected to be new low-dimensional metals. The structural interrelations between some of these new phases can be understood once the primordial template role of the alkali metal as well as the secondary Te…Te interactions are considered. Tellurium-rich alkali metal tellurides seem to be promising materials in the search for new solids with interesting structural and electronic properties. 相似文献
27.
In this paper we present new work done on the bin-picking problem. The work was triggered by the advent of a new solid state range camera which enables the economic and robust use of range imagery in industrial robotic automation tasks. The application presented is that of pick-and-place of randomly oriented but known polyhedral objects in an industrial robotic work cell. The algorithms for segmentation, pose estimation, and grasp point determination are presented along with practical results from a real industrial grade work cell. 相似文献
28.
EDnline PhillippeRabier StephaneSerrier 《电子设计应用》2003,(9):66-67
异步数字用户线路(ADSL)调制解调器由于同普通电话线相连,容易遭受电击。因此,必须施加恰当保护,以抵抗破坏性过压和压流的冲击。通晓保护标准、指导原则和器件,可以提高设计人员保护ADSL调制解调器的电话线及数据线免遭过压冲击的能力。如表1所示,标准的选择取决于设备类型、在网络中的位置和网络的地理区域。对于并联元器件,特别是具有急剧短路特性的器件,可以提供必要的雷电冲击保护功能。急剧短路器件的优点是可以高效地短接电话双绞线,消除过压,而且热量极小。在正常电压下,急剧短路器件保持开路状态。器件的电流处理能力取决于器… 相似文献
29.
Predictions from interpersonal traits to affect were examined in the context of 3 models. In the global trait model, traits were used to predict affect aggregated over a 20-day period. In the situational congruence model, traits were used to predict affect in trait-relevant situations. In the behavioral concordance model, the co-occurrence between behaviors and affect was examined for individual participants, and then traits were used to predict the degree to which behavior and affect co-occurred. No support was found for the global trait and situational congruence models. Support was found for the behavioral concordance model for 3 of the 4 traits. Individuals high on agreeableness and quarrelsomeness experienced pleasant affect when they engaged in behaviors concordant with their traits. Individuals high on agreeableness, quarrelsomeness, and dominance experienced unpleasant affect when they engaged in behaviors opposite to their traits. (PsycINFO Database Record (c) 2010 APA, all rights reserved) 相似文献
30.