Detecting and segmenting un-occluded items by actively casting shadows

  • Authors:

  • Tze K. Koh;Amit Agrawal;Ramesh Raskar;Steve Morgan;Nicholas Miles;Barrie Hayes-Gill

  • Affiliations:

  • Mitsubishi Electric Research Labs, Cambridge, MA and School of Chemical, Environmental and Mining Engineering, University of Nottingham, UK and School of Electrical and Electronic Engineering, Uni ...;Mitsubishi Electric Research Labs, Cambridge, MA;Mitsubishi Electric Research Labs, Cambridge, MA;School of Electrical and Electronic Engineering, University of Nottingham, UK;School of Chemical, Environmental and Mining Engineering, University of Nottingham, UK;School of Electrical and Electronic Engineering, University of Nottingham, UK

  • Venue:
  • ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
  • Year:
  • 2007

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Abstract

We present a simple and practical approach for segmenting unoccluded items in a scene by actively casting shadows. By 'items', we refer to objects (or part of objects) enclosed by depth edges. Our approach utilizes the fact that under varying illumination, un-occluded items will cast shadows on occluded items or background, but will not be shadowed themselves. We employ an active illumination approach by taking multiple images under different illumination directions, with illumination source close to the camera. Our approach ignores the texture edges in the scene and uses only the shadow and silhouette information to determine the occlusions. We show that such a segmentation does not require the estimation of a depth map or 3D information, which can be cumbersome, expensive and often fails due to the lack of texture and presence of specular objects in the scene. Our approach can handle complex scenes with self-shadows and specularities. Results on several real scenes along with the analysis of failure cases are presented.