Paper Info
Title
Probabilistic Models of Appearance for 3-D Object Recognition
Abstract
We describe how to model the appearance of a 3-D object using multiple views, learn such a model from training images, and use the model for object recognition. The model uses probability distributions to describe the range of possible variation in the object's appearance. These distributions are organized on two levels. Large variations are handled by partitioning training images into clusters corresponding to distinctly different views of the object. Within each cluster, smaller variations are represented by distributions characterizing uncertainty in the presence, position, and measurements of various discrete features of appearance. Many types of features are used, ranging in abstraction from edge segments to perceptual groupings and regions. A matching procedure uses the feature uncertainty information to guide the search for a match between model and image. Hypothesized feature pairings are used to estimate a viewpoint transformation taking account of feature uncertainty. These methods have been implemented in an object recognition system, OLIVER. Experiments show that OLIVER is capable of learning to recognize complex objects in cluttered images, while acquiring models that represent those objects using relatively few views.
Year
DOI
Venue
2000
10.1023/A:1026502202780
International Journal of Computer Vision
Keywords
Field
DocType
object recognition,appearance representation,model-based vision,visual learning,clustering,model indexing
Computer science,Probability distribution,Artificial intelligence,Visual learning,Probabilistic logic,Cluster analysis,Computer vision,3D single-object recognition,Pattern recognition,Object model,Active appearance model,Machine learning,Cognitive neuroscience of visual object recognition
Journal
Volume
Issue
ISSN
40
2
1573-1405
Citations 
PageRank 
References 
48
22.04
20
Authors
2
Name
Order
Citations
PageRank
Arthur R. Pope112131.89
D. G. Lowe2157181413.60