Call for Papers

Click here to download SPLINE2016 CFP

Call for Papers

Machines are playing an ever-increasing role in how we conduct our daily lives and business, and this trend is certainly not expected to slow down any time soon. Intelligent machines and robots penetrate into every corner of our environments, and interlink humans, physical and digital worlds. Key scientific areas include sensing, signal and data processing, and machine learning. These areas are challenging, but exciting ones, and require bringing together several central technologies in a harmonious way.  It is therefore not surprising that interest in these areas has surged in recent years, and activities are now very high.

This workshop aims at creating a forum for researchers and engineers from a wide variety of disciplines related to creating intelligent machines and robots. We encourage contributions that will bring state-of-the-art forward, and facilitate an active and constructive exchange of ideas on current areas of interest. The workshop will feature keynote speeches, industrial talks, invited presentations and presentations with full paper submissions, and demos.


We invite previously unpublished manuscripts directly targeting the following areas: sensing and processing, machine learning and pattern recognition, social and service robots, big data, biometrics and de-identification. The scope includes, but is not limited to:

  • Sensing Technology
  • Audio and Speech Processing
  • Computer Vision and Image Processing
  • Signal Processing
  • Data Science and Big Data
  • Recommender Systems
  • Pattern Recognition and Machine Learning
  • Deep Learning
  • Artificial Intelligence
  • Perceptual Models
  • Social and Service Robots
  • Human-Robot Interaction
  • Biometrics, Soft-Biometrics and De-identification
  • Privacy Protection

Paper Submission

All accepted papers are expected to be included in IEEE Xplore and will be indexed by EI. Authors of selected papers will be invited to submit extensions of their work to a special issue of NEUROCOMPUTING (IF: 2.083): Machine Learning for Non-Gaussian Data Processing.

For more information please visit the Submissions page.