Unified interpretation of feature values for object recognition

Thorsten Koelzow; Lars Schoepfer

doi:10.1117/12.477028

25 July 2002 Unified interpretation of feature values for object recognition

Thorsten Koelzow, Lars Schoepfer

Proceedings Volume 4726, Automatic Target Recognition XII; (2002) https://doi.org/10.1117/12.477028
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

One trend in modern object recognition from images is the use of multiple features and sensors which are combined for the object recognition task. To get better classification results the features used for the classification of the objects should be physically 'orthogonal'. To be independent of the kind of features and of their combination method, it is necessary to represent each feature in a unified measure. This measure should define the quality of the feature in the examined image. The measure must be unified, because only such a measure can be combined to a meaningful global result. This paper presents a method which normalizes different kinds of local features. A probabilistic approach is used which provides the unified measure. To map the feature information to a probabilistic interpretation, a generalized function model is used. It is largely independent of the type of application. Two examples of the presented method are shown. The first example uses the Chamfer-Distance to measure edge-features, the second one uses a gray-value correlation coefficient.

Citation Download Citation

Thorsten Koelzow and Lars Schoepfer "Unified interpretation of feature values for object recognition", Proc. SPIE 4726, Automatic Target Recognition XII, (25 July 2002); https://doi.org/10.1117/12.477028

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