Customizing a similarity filter for object recognition

Gregory J. Power

doi:10.1117/12.504497

12 December 2003 Customizing a similarity filter for object recognition

Gregory J. Power

Proceedings Volume 5201, Photonic Devices and Algorithms for Computing V; (2003) https://doi.org/10.1117/12.504497
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

Abstract

In object recognition, one goal of matched filter design has been to define a matching function that produces an ideal correlation peak when a target object in an image scene precisely matches the pre-defined template object. The benefit of such a function is that it guarantees a precise detection/identification. The ideal correlation-based function that defines the match has been described as a dirac delta in the correlation plane. This paper suggests that if similarity as opposed to precise matching is the goal of the correlation function, then ony using current two-dimensional correlation techniques will result in a non-dirac delta in the correlation plane. This paper suggests basing the design of the function on the object recognition goal. The approach for correlation function design is demonstrated using psychophysical evidence for class differentiation. A function is designed based on psychophysical experimental results for distinguishing between two simple objects and their deformations: a square and a circle.

Citation Download Citation

Gregory J. Power "Customizing a similarity filter for object recognition", Proc. SPIE 5201, Photonic Devices and Algorithms for Computing V, (12 December 2003); https://doi.org/10.1117/12.504497

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