Shape recognition with edge-based features

Krystian Mikolajczyk (1) , Andrew Zisserman (1) , Cordelia Schmid (2)

1 Robotics Research Group
2 MOVI - Modeling, localization, recognition and interpretation in computer vision

In this paper we describe an approach to recognizing poorly textured objects, that may contain holes and tubular parts, in cluttered scenes under arbitrary viewing conditions. To this end we develop a number of novel components. First, we introduce a new edge-based local feature detector that is invariant to similarity transformations. The features are localized on edges and a neighbourhood is estimated in a scale invariant manner. Second, the neighbourhood descriptor computed for foreground features is not affected by background clutter, even if the feature is on an object boundary. Third, the descriptor generalizes Lowe's SIFT method to edges. An object model is learnt from a single training image. The object is then recognized in new images in a series of steps which apply progressively tighter geometric restrictions. A final contribution of this work is to allow sufficient flexibility in the geometric representation that objects in the same visual class can be recognized. Results are demonstrated for various object classes including bikes and rackets.

Mots clés: LEAR; LAVA

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Krystian Mikolajczyk, Andrew Zisserman, Cordelia Schmid. Shape recognition with edge-based features. British Machine Vision Conference (BMVC '03), Sep 2003, Norwich, United Kingdom. pp.779--788. ⟨inria-00548226⟩