Paper on active 3D object recognition and sensor planning via ANN

[Francesco Callari]

The following paper deals with the problems of combining 3D shape information
and class priors for the purposes of active, model-based object recognition
and sensor planning. The proposed system correlates estimates of shape and
class uncertainty to determine those sensor locations that best disambiguate
the objects. The class and class sensitivity estimates are computed by an MLP
network, trained using MacKay's "evidence" framework and put in the planning
feedback loop of a mobile robot.
 

FTP-host: ftp.cim.mcgill.ca
FTP-file: /pub/people/franco/ambiguity96.ps.gz

      Active Recognition: Using Uncertainty to Reduce Ambiguity 

            Francesco G. Callari and Frank P. Ferrie

       Centre for Intelligent Machines, McGill University
      3480 University St., Montre\'al, Que., Canada, H3A 2A7
       email: franco at cim.mcgill.ca, ferrie at cim.mcgill.ca 

Keywords: Active Vision, Control of Perception, Learning in Computer Vision

                             ABSTRACT

Ambiguity in scene information, due to noisy measurements and uncertain object
models, can be quantified and actively used by an autonomous agent to
efficiently gather new data and improve its information about the
environment. In this work an information-based utility measure is used to
derive from a learned classification of shape models an efficient data
collection strategy, specifically aimed at increasing classification
confidence when recognizing uncertain shapes. Promising experimental results
with real data are reported.

Submitted to: ICPR96.