Title

View Morphing Using Linear Prediction Of Sub-Space Features

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. © 2011 SPIE.

Publication Date

6-29-2011

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

8049

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.886264

Socpus ID

79959560325 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/79959560325

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