Detecting and segmenting un-occluded and occluded items by actively casting shadows

Tze Ki Koh, Amit Agrawal, Ramesh Raskar, Stephen P. Morgan, Nicholas J. Miles, Barrie R. Hayes-Gill

Research output: Journal PublicationArticlepeer-review

Abstract

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. In addition, we show how to identify regions belonging to occluded objects and segment the scene into multiple layers. Our approach is able to recover the shape of occluded objects if none of its depth edges are occluded. Results on several real scenes along with the analysis of failure cases are presented.

Original languageEnglish
Pages (from-to)33-45
Number of pages13
JournalIPSJ Transactions on Computer Vision and Applications
Volume1
DOIs
Publication statusPublished - 2009
Externally publishedYes

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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