Structural Indexing: Efficient 2-D Object Recognition

  • Stein F
  • Medioni G
  • 51


    Mendeley users who have this article in their library.
  • 82


    Citations of this article.


The problem of recognition of multiple flat objects in a cluttered environment from an arbitrary viewpoint is addressed. The models are acquired automatically and approximated by polygons with multiple line tolerances for robustness. Groups of consecutive segments (super segments) are then encoded and entered into a table. This provides the essential mechanism for indexing and fast retrieval. Once the database of all models is built, the recognition proceeds by segmenting the scene into a polygonal approximation; the code for each super segment retrieves model hypotheses from the table. Hypotheses are clustered if they are mutually consistent and represent the instance of a model. Finally, the estimate of the transformation is refined. This methodology makes it possible to recognize models despite noise, occlusion, scale rotation translation, and a restricted range of weak perspective. A complexity bound is obtained

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Fridtjof Stein

  • Gerard Medioni

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free