Computational intelligence for multimedia understanding international workshop, MUSCLE 2011, Pisa, Italy, December 13-15, 2011, revised selected papers /

Computational intelligence for multimedia understanding international workshop, MUSCLE 2011, Pisa, Italy, December 13-15, 2011, revised selected papers /

<p>This book constitutes the refereed proceedings of the International Workshop  MUSCLE 2011 on Computational Intelligence for Multimedia Understanding, organized by the ERCIM working group in Pisa, Italy on December 2011.</p><p>The 18 revised full papers were carefully reviewed an...

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Соавтор: SpringerLink (Online service)
Другие авторы: Çetin, A. Enis, Salvetti, Ovidio, Salerno, Emanuele
Формат: eКнига
Язык:English
Опубликовано: Berlin, Heidelberg Imprint: Springer 2012.
Серии:Lecture Notes in Computer Science 7252
Предметы:
Computer science.
Electronic data processing.
Data mining.
Multimedia systems.
Artificial intelligence.
Computer vision.
Optical pattern recognition.
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Оглавление:
  • Learning an ontology for visual tasks
  • ontology and algorithms integration for image analysis
  • emotiword: affective lexicon creation with application to interaction and multimedia data
  • a bayesian active learning framework for a two-class classification problem
  • unsupervised classification of SAR images using hierarchical agglomeration and EM
  • geometrical and textural component separation with adaptive scale selection
  • bayesian shape from silhouettes
  • shape retrieval and recognition on mobile devices
  • directionally selective fractional wavelet transform using a 2-D non-separable unbalanced lifting structure
  • Visible and Infrared Image Registration Employing Line-Based Geometric Analysis
  • Texture Recognition Using Robust Markovian Features
  • A Plausible Texture Enlargement and Editing Compound Markovian Mode
  • Bidirectional Texture Function Simultaneous Autoregressive Model
  • Analysis of Human Gaze Interactions with Texture and Shape
  • Rich Internet Application for Semi-automatic Annotation of Semantic Shots on Keyframes
  • Labeling TV Stream Segments with Conditional Random Fields
  • Foreground Objects Segmentation for Moving Camera Scenarios Based on SCGMM
  • Real Time Image Analysis for Infomobilit
  • Tracking the Saliency Features in Images Based on Human Observation Statistics.

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