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Proceedings Paper

View morphing using linear prediction of sub-space features
Author(s): Abhijit Mahalanobis; Phil Berkowitz; Mubarak Shah
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Paper Abstract

We present a mathematical technique for estimating new perspective views of an object from a single image. Unlike traditional graphics or ray tracing methods, our approach treats the view-morphing problem as a 2-D linear prediction process. We first estimate the prediction parameters in a reduced dimensional space using features extracted from "training" images of the object. Given an arbitrary view of the object, the features of the new view are linearly predicted from which the morphed image of the object is reconstructed. The proposed approach can be used for rapidly incorporating new objects in the knowledge base of a computer vision system and may have advantages in low-contrast situations where it is difficult to establish correspondence between sample views.

Paper Details

Date Published: 19 May 2011
PDF: 9 pages
Proc. SPIE 8049, Automatic Target Recognition XXI, 80490Y (19 May 2011); doi: 10.1117/12.886264
Show Author Affiliations
Abhijit Mahalanobis, Lockheed Martin Missiles and Fire Control (United States)
Phil Berkowitz, Univ. of Central Florida (United States)
Mubarak Shah, Univ. of Central Florida (United States)


Published in SPIE Proceedings Vol. 8049:
Automatic Target Recognition XXI
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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