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.
Bruno, A., Greco, L., & LA CASCIA, M. (2013). Object Recognition and Modeling Using SIFT Features. In Advanced Concepts for Intelligent Vision Systems (pp.250-261). Poznan : Springer International Publishing.
|Autori:||Bruno, A.; Greco, L.; LA CASCIA, M.|
|Titolo:||Object Recognition and Modeling Using SIFT Features|
|Settore Scientifico Disciplinare:||Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni|
|Data di creazione:||2013|
|Nome del convegno:||Advanced Concepts for Intelligent Vision Systems (15th International Conference, ACIVS 2013, Poznań, Poland, October 28-31, 2013. Proceedings)|
|Luogo del convegno:||Poznań, Poland|
|Anno del convegno:||October 28-31, 2013.|
|Data di pubblicazione:||2013|
|Numero di pagine:||12|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-3-319-02895-8_23|
|Citazione:||Bruno, A., Greco, L., & LA CASCIA, M. (2013). Object Recognition and Modeling Using SIFT Features. In Advanced Concepts for Intelligent Vision Systems (pp.250-261). Poznan : Springer International Publishing.|
|Tipologia:||0 - Proceedings (TIPOLOGIA NON ATTIVA)|
|Appare nelle tipologie:||0 - Proceedings (TIPOLOGIA NON ATTIVA)|