This volume in the Springer Lecture Notes in computerscience (LNCS) series contains the papers presented at the S+SSPR 2010 Workshops, which was the seventh occasion that SPR and SSPR workshops have been held jointly...
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ISBN:
(数字)9783642149801
ISBN:
(纸本)9783642149795
This volume in the Springer Lecture Notes in computerscience (LNCS) series contains the papers presented at the S+SSPR 2010 Workshops, which was the seventh occasion that SPR and SSPR workshops have been held jointly. S+SSPR 2010 was organized by TC1 and TC2, Technical Committees of the International Association for Pattern Recognition(IAPR), andheld inCesme, Izmir, whichis a seaside resort on the Aegean coast of Turkey. The conference took place during August 18–20, 2010, only a few days before the 20th International Conference on Pattern Recognition (ICPR) which was held in Istanbul. The aim of the series of workshops is to create an international forum for the presentation of the latest results and exchange of ideas between researchers in the ?elds of statistical and structural pattern recognition. SPR 2010 and SSPR 2010 received a total of 99 paper submissions from many di?erent countries around the world, giving it a truly international perspective, as has been the case for previous S+SSPR workshops. This volume contains 70 accepted papers, 39 for oral and 31 for poster presentation. In addition to par- lel oral sessions for SPR and SSPR, there were two joint oral sessions of interest to both SPR and SSPR communities. Furthermore, to enhance the workshop experience, there were two joint panel sessions on “Structural Learning” and “Clustering,” in which short author presentations were followed by discussion. Another innovation this year was the ?lming of the proceedings by Videol- tures.
This volume constitutes the refereed proceedings of the Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2012) and Statistical Techniques in Pattern Recognition (SPR 2012), held...
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ISBN:
(数字)9783642341663
ISBN:
(纸本)9783642341656
This volume constitutes the refereed proceedings of the Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2012) and Statistical Techniques in Pattern Recognition (SPR 2012), held in Hiroshima, Japan, in November 2012 as a satellite event of the 21st International Conference on Pattern Recognition, ICPR 2012.
The 80 revised full papers presented together with 1 invited paper and the Pierre Devijver award lecture were carefully reviewed and selected from more than 120 initial submissions. The papers are organized in topical sections on structural, syntactical, and statistical pattern recognition, graph and tree methods, randomized methods and image analysis, kernel methods in structural and syntactical pattern recognition, applications of structural and syntactical pattern recognition, clustering, learning, kernel methods in statistical pattern recognition, kernel methods in statistical pattern recognition, as well as applications of structural, syntactical, and statistical methods.
The Metaverse presents an emerging creative expression and collaboration frontier where generative artificial intelligence (GenAI) can play a pivotal role with its ability to generate multimodal content from simple pr...
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The Metaverse presents an emerging creative expression and collaboration frontier where generative artificial intelligence (GenAI) can play a pivotal role with its ability to generate multimodal content from simple prompts. These prompts allow the metaverse to interact with GenAI, where context information, instructions, input data, or even output indications constituting the prompt can come from within the metaverse. However, their integration poses challenges regarding interoperability, lack of standards, scalability, and maintaining a high-quality user experience. This paper explores how GenAI can productively assist in enhancing creativity within the contexts of the Metaverse and unlock new opportunities. We provide a technical, in-depth overview of the different generative models for image, video, audio, and 3D content within the Metaverse environments. We also explore the bottlenecks, opportunities, and innovative applications of GenAI from the perspectives of end users, developers, service providers, and AI researchers. This survey commences by highlighting the potential of GenAI for enhancing the Metaverse experience through dynamic content generation to populate massive virtual worlds. Subsequently, we shed light on the ongoing research practices and trends in multimodal content generation, enhancing realism and creativity and alleviating bottlenecks related to standardization, computational cost, privacy, and safety. Lastly, we share insights into promising research directions toward the integration of GenAI with the Metaverse for creative enhancement, improved immersion, and innovative interactive applications.
This volume brings together the advanced research results obtained by the European COST Action 2102 "Cross Modal Analysis of Verbal and Nonverbal Communication", primarily discussed at the PINK SSPnet-COST21...
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ISBN:
(数字)9783642257759
ISBN:
(纸本)9783642257742
This volume brings together the advanced research results obtained by the European COST Action 2102 "Cross Modal Analysis of Verbal and Nonverbal Communication", primarily discussed at the PINK SSPnet-COST2102 International Conference on Analysis of Verbal and Nonverbal Communication and Enactment: The Processing Issues, held in Budapest, Hungary, in September 2010.
The 40 papers presented were carefully reviewed and selected for inclusion in the book. The volume is arranged into two scientific sections. The first section, Multimodal Signals: Analysis, Processing and Computational Issues, deals with conjectural and processing issues of defining models, algorithms, and heuristic strategies for data analysis, coordination of the data flow and optimal encoding of multi-channel verbal and nonverbal features. The second section, Verbal and Nonverbal Social Signals, presents original studies that provide theoretical and practical solutions to the modelling of timing synchronization between linguistic and paralinguistic expressions, actions, body movements, activities in human interaction and on their assistance for an effective human-machine interactions.
Biometric authentication refers to identifying an individual based on his or her distinguishing physiological and/or behavioral characteristics. It associates an individual with a previously determined identity based ...
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ISBN:
(数字)9783540479178
ISBN:
(纸本)9783540437239
Biometric authentication refers to identifying an individual based on his or her distinguishing physiological and/or behavioral characteristics. It associates an individual with a previously determined identity based on that individual s appearance or behavior. Because many physiological or behavioral characteristics (biometric indicators) are distinctive to each person, biometric identifiers are inherently more reliable and more capable than knowledge-based (e.g., password) and token-based (e.g., a key) techniques in differentiating between an authorized person and a fraudulent impostor. For this reason, more and more organizations are looking to automated identity authentication systems to improve customer satisfaction, security, and operating efficiency as well as to save critical resources. Biometric authentication is a challenging pattern recognition problem; it involves more than just template matching. The intrinsic nature of biometric data must be carefully studied, analyzed, and its properties taken into account in developing suitable representation and matching algorithms. The intrinsic variability of data with time and environmental conditions, the social acceptability and invasiveness of acquisition devices, and the facility with which the data can be counterfeited must be considered in the choice of a biometric indicator for a given application. In order to deploy a biometric authentication system, one must consider its reliability, accuracy, applicability, and efficiency. Eventually, it may be necessary to combine several biometric indicators (multimodal-biometrics) to cope with the drawbacks of the individual biometric indicators.
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