Object recognition and modeling using SIFT features

Authors: Bruno, A., Greco, L. and La Cascia, M.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 8192 LNCS

Pages: 250-261

eISSN: 1611-3349

ISBN: 9783319028941

ISSN: 0302-9743

DOI: 10.1007/978-3-319-02895-8_23

Abstract:

In this paper we present a technique for object recognition and modelling based on local image features matching. Given a complete set of views of an object the goal of our technique is the recognition of the same object in an image of a cluttered environment containing the object and an estimate of its pose. The method is based on visual modeling of objects from a multi-view representation of the object to recognize. The first step consists of creating object model, selecting a subset of the available views using SIFT descriptors to evaluate image similarity and relevance. The selected views are then assumed as the model of the object and we show that they can effectively be used to visually represent the main aspects of the object. Recognition is done making comparison between the image containing an object in generic position and the views selected as object models. Once an object has been recognized the pose can be estimated searching the complete set of views of the object. Experimental results are very encouraging using both a private dataset we acquired in our lab and a publicly available dataset. © 2013 Springer-Verlag.

Source: Scopus