Dynamic Depth Field Data Driven Learning, Recognition and Computation

  in Special Issue   Posted on August 29, 2015

Information for the Special Issue

Submission Deadline: Tue 15 Sep 2015
Journal Impact Factor : 3.317
Journal Name : Neurocomputing
Journal Publisher:
Website for the Special Issue: http://www.journals.elsevier.com/neurocomputing/call-for-papers/special-issue-on-dynamic-depth-field-data-driven-learning/
Journal & Submission Website: https://www.journals.elsevier.com/neurocomputing

Special Issue Call for Papers:

Depth field records all the depth information in 3D world, and it is the core of stereo vision, shows great potentials in the application of military, aerospace, medical, machinery, digital content generation, and other fields. Recently, great challenges arose when switching the methods of static scene depth field to dynamic one. With the help of RGB-D and TOF techniques, dynamic depth field sensing is available for many applications. A series of new problems have been arisen from dynamic depth field data driven learning, recognition and computation for above multidiscipline researches.

This special issue focuses on the most recent progresses on the dynamic depth field data driven learning, recognition and computation, from basic tools including spatial-temporal refinement of depth field, to advanced techniques such as complex gesture recognition and natural human-computer interaction. With the rapid development of computation capacity and sensing techniques, dynamic depth field data driven applications include driverless car, X-Box games, motion capture and others have received abundant attentions from public. This special issue also targets on novel dynamic depth field data driven applications and prototypes. The primary objective of this special issue fosters focused attention on the latest research progress in these interesting areas.

The special issue seeks for original contribution of works which addresses the challenges from dynamic depth field data driven learning, recognition and computation. The list of possible topics includes, but not limited to:

Dynamic depth field data driven learning

Learning methods with depth field data available

Learning methods in depth field data enhancement

Dynamic depth field data driven computation

Dynamic depth field sensing methods

Spatial and temporal refinement for dynamic depth field

3D modeling based on dynamic depth field data

Compression and transmission methods for dynamic depth field data

Dynamic depth field data driven recognition

Shape recognition and processing in dynamic depth field data

3D object retrieval in dynamic scenario

Object detection and recognition in outdoor environment

3D Object labeling and sentiment segmentation

Applications and Prototypes of dynamic depth field

Gesture based natural human-computer interaction

Interactive 3D and freeview point vision application

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