PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Trains of keypoints for 3D object recognition
Elise Arnaud, ELisabetta Delponte, Francesca Odone and Alessandro Verri
In: IEEE International Conference on Pattern Recognition, Hong Kong(2006).


This paper presents a 3D object recognition method that exploits the spatio-temporal coherence of image sequences to capture the object most relevant features. We start from an image sequence that describes the object's visual appearance from different view points. We extract local features (SIFT) and track them over the sequence. The tracked interest points form {\em trains of features} that are used to build a vocabulary for the object. Training images are represented with respect to that vocabulary and an SVM classifier is trained to recognize the object. We present very promising results on a dataset of 11 objects. Tests are performed under varying illumination, scale, and scene clutter.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:2915
Deposited By:Alessandro Verri
Deposited On:23 November 2006