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...
Wedi'i Gadw mewn:
Awdur Corfforaethol: | |
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Awduron Eraill: | , , |
Fformat: | eLyfr |
Iaith: | English |
Cyhoeddwyd: |
Berlin, Heidelberg
Imprint: Springer
2012.
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Cyfres: | Lecture Notes in Computer Science
7252 |
Pynciau: | |
Mynediad Ar-lein: | Click here to view the full text content |
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Ychwanegu Tag
Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
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Tabl Cynhwysion:
- 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.