Wednesday, February 03, 2016

CfP: 1st Workshop on SPARSITY AND COMPRESSIVE SENSING IN MULTIMEDIA, July 15, 2016 - Seattle, USA

 
 
Here is a new CfP as posted on the G+ group:

1st Workshop on SPARSITY AND COMPRESSIVE SENSING IN MULTIMEDIA


An IEEE ICME 2016 workshop - July 15, 2016 - Seattle, USA

Organizing committee



Scope
Technology improvements in multimedia content generation have led to a continuous cycle of creation, processing, communication and consumption of information as part of our social interactions and entertainment. This has spurred the development of efficient ways to acquire, process, communicate and store vast amounts of data. One of the most prominent recent breakthroughs is the development of compressed sensing and sparse representations. To-date these techniques have had significant success in numerous areas of signal acquisition, parameter estimation, communication, and machine learning. These developments have found a home in a number of applications, including media retrieval, radar and medical imaging systems, and video compression, just to mention a few.


This workshop’s objective is to bring together leading researchers around the world to present their newest accomplishments, exchange latest experiences, and explore future directions in the important and evolving fields of CS and sparse representations. In particular, it is focused on the dissemination of recent results in multimedia methods and applications that take advantage of these techniques.


We are inviting original submission to this workshop in the form of full-length 6-pages papers, or short 2-pages position papers (these latter are not going to be published in the proceedings). Topics of interest include, but are not restricted to:

  • Theoretical aspects of compressive sensing and sparsity
  • Communication and networking applications
  • CS and sparsity for 5G communication systems
  • CS and sparsity for big data
  • CS and sparsity for media retrieval
  • CS and sparsity for speech, audio, image and video compression
  • CS and sparsity for detection, estimation and machine learning
  • Compressive imaging
  • Distributed applications of compressive sensing
  • Emerging applications of CS and sparsity  
 
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