Detecting and segmenting un-occluded items by actively casting shadows

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

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


We present a simple and practical approach for segmenting un-occluded 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.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings
PublisherSpringer Verlag
Number of pages11
EditionPART 1
ISBN (Print)9783540763857
Publication statusPublished - 2007
Externally publishedYes
Event8th Asian Conference on Computer Vision, ACCV 2007 - Tokyo, Japan
Duration: 18 Nov 200722 Nov 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4843 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th Asian Conference on Computer Vision, ACCV 2007

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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