Paper Info
Title
Active multi-camera object recognition in presence of occlusion
Abstract
This paper is concerned with the problem of appearance-based active multi-sensor object recognition/pose estimation in the presence of structured noise. It is assumed that multiple cameras acquire images from an object belonging to a set of known objects. An algorithm is proposed for optimal sequential positioning of the cameras in order to estimate the class and pose of the object from sensory observations. The principle component analysis is used to produce the observation vector from the acquired images. Object occlusion and sensor noise have been explicitly incorporated into the recognition process using a probabilistic approach. A recursive Bayesian state estimation problem is formulated that employs the mutual information in order to determine the best next camera positions based on the available information. Experiments with a two-camera system demonstrate that the proposed method is highly effective in object recognition/pose estimation in the presence of occlusion.
Year
DOI
Venue
2005
10.1109/IROS.2005.1545591
IROS
Keywords
Field
DocType
hidden feature removal,mutual information,active sensing,active vision,recursive bayesian state estimation,bayes methods,computer vision,object occlusion,pose estimation,camera position,image acquisition,object recognition,recursive estimation,position measurement,multisensor object recognition,principal component analysis,principle component analysis,sensor noise,active multicamera object recognition,sensor fusion,optimal sequential positioning
Computer vision,3D single-object recognition,Pattern recognition,Computer science,3D pose estimation,Sensor fusion,Pose,Mutual information,Artificial intelligence,Probabilistic logic,Cognitive neuroscience of visual object recognition,Bayesian probability
Conference
ISBN
Citations 
PageRank 
0-7803-8912-3
1
0.35
References 
Authors
11
3
Name
Order
Citations
PageRank
F. Farshidi171.15
Shahin Sirouspour222921.84
Thia Kirubarajan321530.81