19 May 2011 View morphing using linear prediction of sub-space features
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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.
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Abhijit Mahalanobis, Abhijit Mahalanobis, Phil Berkowitz, Phil Berkowitz, Mubarak Shah, Mubarak Shah, } "View morphing using linear prediction of sub-space features", Proc. SPIE 8049, Automatic Target Recognition XXI, 80490Y (19 May 2011); doi: 10.1117/12.886264; https://doi.org/10.1117/12.886264

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