Skip to main content
Article thumbnail
Location of Repository

Bayesian Online Learning of Corresponding Points of Objects with Sequential Monte Carlo

By Miika Toivanen and Jouko Lampinen

Abstract

Abstract—This paper presents an online method that learns the corresponding points of an object from un-annotated grayscale images containing instances of the object. In the first image being processed, an ensemble of node points is automatically selected which is matched in the subsequent images. A Bayesian posterior distribution for the locations of the nodes in the images is formed. The likelihood is formed from Gabor responses and the prior assumes the mean shape of the node ensemble to be similar in a translation and scale free space. An association model is applied for separating the object nodes and background nodes. The posterior distribution is sampled with Sequential Monte Carlo method. The matched object nodes are inferred to be the corresponding points of the object instances. The results show that our system matches the object nodes as accurately as other methods that train the model with annotated training images. Keywords—Bayesian modeling, Gabor filters, Online learning, Sequential Monte Carlo

Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.192.8490
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.waset.org/journals/... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.