Object Identification From Multiple Images Based on Point Matching Under a General Transformation

Authors:
M. C. K. Yang;J. S. Lee
Affiliations:
-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1994

Citing 3
Cited 1

Model-based image matching using location

Model-based image matching using location
A combinatorial approach for classification of patterns with missing information and random orientation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant quantities in regression-induced boundaries under a special linear transformation

Pattern Recognition

Ship identification in sequential ISAR imagery

Machine Vision and Applications

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Abstract

This work is motivated by ship identification from a sequence of ISAR images. Maximum likelihood classification, based on point matching, is formulated when the observed images are subject to missing points and phantoms. The 3-D to 2-D transformation is assumed to be known only in a certain parametric form. Proper weights, based on the noise levels for all images, are derived for the classification formula. The new formulation simplifies the computation of matching and makes its extension to object identification from multiple images feasible. Moreover, some theoretical properties of the identification procedure can now be investigated. Guidelines on which groups of objects are easier to distinguish are found from statistical theory followed by intuitive explanation. This method is then applied to ship identification with simulated ISAR images.