Geometric features in images of polyhedra

Raashid Malik; Hyeon-June Kim

doi:10.1117/12.222701

3 October 1995 Geometric features in images of polyhedra

Raashid Malik, Hyeon-June Kim

Proceedings Volume 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling; (1995) https://doi.org/10.1117/12.222701
Event: Photonics East '95, 1995, Philadelphia, PA, United States

Abstract

In model-based object recognition, the features used to describe a model often represent various geometric properties of the model. But there is a difficulty in recognizing a 3D object when the orientation of the object or the view angle of the camera in three space is unknown. This occurs because measurements of the geometric features of an object in a 2D image vary with different view directions. The variations of measured features may be expressed using probability density functions. These densities may be used to completely characterize the observed variations. In this paper we introduce a recognition scheme based on the probabilistic analysis of view variations of geometric features. Our previous work quantified the view variations of a certain pair of features (referred to as quadrature line ratios) for planar surfaces which were scale invariant and image rotation invariant. That work is now extended to a complete 3D convex polyhedral object recognition scheme. We derive the joint density of two pairs of features which are measured from two non-coplanar faces of an object. Likelihood functions based on this density have been developed for each aspect of a polyhedral object and used in the recognition scheme. Experiments have been conducted to verify the efficiency of the proposed scheme.

Citation Download Citation

Raashid Malik and Hyeon-June Kim "Geometric features in images of polyhedra", Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); https://doi.org/10.1117/12.222701

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available