PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Dense 3D Correspondence
Florian Steinke, Bernhard Schölkopf and Volker Blanz
In: 20th Annual Conference on Neural Information Processing Systems, 4-9 Dec 2006, Vancouver / Whistler, Canada.

Abstract

Establishing correspondence between distinct objects is an important and nontrivial task: correctness of the correspondence hinges on properties which are difficult to capture in an a priori criterion. While previous work has used a priori criteria which in some cases led to very good results, the present paper explores whether it is possible to learn a combination of features that, for a given training set of aligned human heads, characterizes the notion of correct correspondence. By optimizing this criterion, we are then able to compute correspondence and morphs for novel heads.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3118
Deposited By:Bernhard Schölkopf
Deposited On:21 December 2007