PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Shape-from-recognition: Recognition enables meta-data transfer
Alexander Thomas, Vittorio Ferrari, Bastian Leibe, Tinne Tuytelaars and Luc Van Gool
Computer Vision and Image Understanding 2009.

Abstract

Low-level cues in an image not only allow to infer higher-level information like the presence of an object, but the inverse is also true. Category-level object recognition has now reached a level of maturity and accuracy that allows to successfully feed back its output to other processes. This is what we refer to as cognitive feedback. In this paper, we study one particular form of cognitive feedback, where the ability to recognize objects of a given category is exploited to infer different kinds of meta- data annotations for images of previously unseen object instances, in particular information on 3D shape. Meta-data can be discrete, real- or vector-valued. Our approach builds on the Implicit Shape Model of Leibe and Schiele [1], and extends it to transfer annotations from training images to test images. We focus on the inference of approximative 3D shape information about objects in a single 2D image. In experiments, we illustrate how our method can infer depth maps, surface normals and part labels for previously unseen object instances.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:5464
Deposited By:Alexander Thomas
Deposited On:24 September 2009