25 July 2002 Unified interpretation of feature values for object recognition
Author Affiliations +
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.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thorsten Koelzow, Lars Schoepfer, "Unified interpretation of feature values for object recognition", Proc. SPIE 4726, Automatic Target Recognition XII, (25 July 2002); doi: 10.1117/12.477028; https://doi.org/10.1117/12.477028
PROCEEDINGS
8 PAGES


SHARE
RELATED CONTENT

Four-mirror optical system with ultra-low distortion
Proceedings of SPIE (August 21 2014)
Gait recognition based on Kinect sensor
Proceedings of SPIE (May 15 2014)
Optimal target recognition method using accumulated evidence
Proceedings of SPIE (September 15 1998)
A Multi-Sensor Robotics System For Object Recognition
Proceedings of SPIE (March 27 1989)
The PANIC software system
Proceedings of SPIE (July 19 2010)

Back to Top