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Multiple View Object Cosegmentation Using Appearance and Stereo Cues

  • Adarsh Kowdle
  • Sudipta N. Sinha
  • Richard Szeliski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7576)

Abstract

We present an automatic approach to segment an object in calibrated images acquired from multiple viewpoints. Our system starts with a new piecewise planar layer-based stereo algorithm that estimates a dense depth map that consists of a set of 3D planar surfaces. The algorithm is formulated using an energy minimization framework that combines stereo and appearance cues, where for each surface, an appearance model is learnt using an unsupervised approach. By treating the planar surfaces as structural elements of the scene and reasoning about their visibility in multiple views, we segment the object in each image independently. Finally, these segmentations are refined by probabilistically fusing information across multiple views. We demonstrate that our approach can segment challenging objects with complex shapes and topologies, which may have thin structures and non-Lambertian surfaces. It can also handle scenarios where the object and background color distributions overlap significantly.

Keywords

object cosegmentation multiview segmentation multiview stereo 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Adarsh Kowdle
    • 1
  • Sudipta N. Sinha
    • 2
  • Richard Szeliski
    • 2
  1. 1.Cornell UniversityIthacaUSA
  2. 2.Microsoft ResearchRedmondUSA

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