Shape feature variation for recognition

Raashid Malik; Hyeon-June Kim

doi:10.1117/12.189101

13 October 1994 Shape feature variation for recognition

Raashid Malik, Hyeon-June Kim

Proceedings Volume 2354, Intelligent Robots and Computer Vision XIII: 3D Vision, Product Inspection, and Active Vision; (1994) https://doi.org/10.1117/12.189101
Event: Photonics for Industrial Applications, 1994, Boston, MA, United States

Abstract

Geometric features of an object in an image vary with the view angle of the camera or with the orientation of the object. The variation in measured features is often expressed using probability density functions. The probabilistic bases of this approach arise from assumptions concerning the unconstrained pose of the object and the consequent that the two orientation angles are random variables with a known joint density. In this paper we concentrate on recognizing the faces of polyhedral surfaces. We start by quantifying the minimal features in a face that are scale invariant and rotation invariant (about the optical axis). Two features we found to be analytically tractable were the normalized area between two edges and the normalized innerproduct of two edge vectors. We refer to the features as quadrature line ratios. The joint density of these measured features in orthographic images has been derived. The variation of these features in images are analyzed and plotted. Likelihood functions based on this density have been developed and used in distinguishing and recognizing faces of polyhedra. Experiments with real and simulated data have been conducted to verify the efficacy of the proposed schemes and the results show that the method is promising.

Citation Download Citation

Raashid Malik and Hyeon-June Kim "Shape feature variation for recognition", Proc. SPIE 2354, Intelligent Robots and Computer Vision XIII: 3D Vision, Product Inspection, and Active Vision, (13 October 1994); https://doi.org/10.1117/12.189101

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