Analysis of Recent Advances in Optical Flow Estimation Methods

  • Javier Sánchez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6927)

Abstract

One of the key problems in computer vision is the estimation of motion in image sequences. The apparent displacement of the pixels through the image sequence is generally called optical flow. This is a low-level task that is the base for many other high-level applications, such us stereoscopic vision and 3D scene reconstruction, object tracking, ambient intelligence, video surveillance, medical image analysis, meteorological prediction and analysis, and so on. After many years of intense research, we may consider that the optical flow research field is not mature yet. The quality and amount of recent publications, with many important contributions, reflect that this is a very active field. It is attracting many researchers in computer vision that make evolve the field in a steady way. In this paper we examine the last contributions and most important ideas about optical flow that have appeared during the last years.

Keywords

Temporal Derivative Data Term Temporal Coherence Smoothness Constraint Benchmark Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Javier Sánchez
    • 1
  1. 1.Imaging Technology Center (www.ctim.es)University of Las Palmas de Gran CanariaLas Palmas de Gran CanariaSpain

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