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Curvature-based signatures for object description and recognition Elli Angelopoulou, James P. Williams, Lawrence B. Wolff
 

Summary: Curvature-based signatures for object description and recognition
Elli Angelopoulou, James P. Williams, Lawrence B. Wolff
Computer Vision Laboratory, Department of Computer Science,
The Johns Hopkins University, Baltimore, MD 21218, USA
e-mail: {angelop, jimbo, wolff}@cs.jhu.edu
ABSTRACT
An invariant related to Gaussian curvature at an object point is developed based upon the covariance matrix of photo-
metric values within a local neighborhood about the point. We employ three illumination conditions, two of which are com-
pletely unknown. We never need to explicitly know the surface normal at a point. The determinant of the covariance matrix
of the intensity three-tuples in the local neighborhood of an object point is shown to be invariant with respect to rotation and
translation. A way of combining these determinants to form a signature distribution is formulated that is rotation, translation,
and scale invariant. This signature is shown to be invariant over large ranges of poses of the same objects, while being sig-
nificantly different between distinctly shaped objects. A new object recognition methodology is proposed by compiling sig-
natures for only a few viewpoints of a given object.
Keywords: object representation, object recognition, Gaussian curvature, covariance matrix
1. INTRODUCTION
The recognition of three-dimensional objects using two-dimensional images and the efficient representation of the
shape information are of fundamental importance in computer vision and robotics. A typical approach is to extract a set of
high-level features from input images and associate these features with the geometry of the objects in the scene. Such fea-
tures can vary from simple primitives like 2-D points14, junctions23 and 2-D curves11 to complex structures like deformable

  

Source: Angelopoulou, Elli - Department of Computer Science, Friedrich Alexander University Erlangen Nürnberg

 

Collections: Computer Technologies and Information Sciences