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Combining low-level segmentation with relational classification | IEEE Conference Publication | IEEE Xplore

Combining low-level segmentation with relational classification


Abstract:

A novel approach is presented that classifies multiple independently moving objects by taking into account existing object relations, closing the loop to low-level scene ...Show More

Abstract:

A novel approach is presented that classifies multiple independently moving objects by taking into account existing object relations, closing the loop to low-level scene segmentation. The method partitions a stereo image sequence into its most prominent moving groups with similar 3-dimensional (3D) motion. Object motion is estimated using the expectation-maximization (EM) algorithm. The EM formulation is used to account for the unknown associations between objects and observations. In a segregation step, each image point is assigned to the object hypothesis with maximum a posteriori (MAP) association probability. This segmentation is fed into a multiple object classification scheme based on Markov logic which integrates relational scene knowledge. Class probabilities for the individual object hypotheses are then used within the association process for track enhancement.
Date of Conference: 27 September 2009 - 04 October 2009
Date Added to IEEE Xplore: 03 May 2010
ISBN Information:
Conference Location: Kyoto, Japan

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