Sampling of images for efficient model-based vision

Author

Akra, Mohamad ; Bazzi, Louay ; Mitter, Sanjoy

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., MIT, Cambridge, MA, USA

Volume

21

Issue

1

fYear

1999

fDate

1/1/1999 12:00:00 AM

Firstpage

4

Lastpage

11

Abstract

The problem of matching two planar sets of points in the presence of geometric uncertainty has important applications in pattern recognition, image understanding, and robotics. The first set of points corresponds to the “template.” The other set corresponds to the “image” that-possibly-contains one or more deformed versions of the “template” embedded in a cluttered image. Significant progress has been made on this problem and various polynomial-time algorithms have been proposed. We show how to sample the “image” in linear time, reducing the number of foreground points n by a factor of two-six (for commonly occurring images) without degrading the quality of the matching results. The direct consequence is a time-saving by a factor of 2^p-6^p for an O(n^p) matching algorithm. Our result applies to a fairly large class of available matching algorithms

Keywords

computer vision; image matching; image sampling; O(n^p) matching algorithm; foreground points; geometric uncertainty; model-based vision; polynomial-time algorithms; Computational geometry; Degradation; Helium; Image sampling; Pattern matching; Pattern recognition; Polynomials; Robot vision systems; Solid modeling; Uncertainty;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.745729

Filename

745729