Voting by Grouping Dependent Parts

@inproceedings{Yarlagadda2010VotingBG,
  title={Voting by Grouping Dependent Parts},
  author={Pradeep Yarlagadda and Antonio Monroy and Bj{\"o}rn Ommer},
  booktitle={ECCV},
  year={2010}
}
Hough voting methods efficiently handle the high complexity of multiscale, category-level object detection in cluttered scenes. The primary weakness of this approach is however that mutually dependent local observations are independently voting for intrinsically global object properties such as object scale. All the votes are added up to obtain object hypotheses. The assumption is thus that object hypotheses are a sum of independent part votes. Popular representation schemes are, however, based… CONTINUE READING
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