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ACCV
2010
Springer
13 years 11 days ago
An Efficient RANSAC for 3D Object Recognition in Noisy and Occluded Scenes
In this paper, we present an efficient algorithm for 3D object recognition in presence of clutter and occlusions in noisy, sparse and unsegmented range data. The method uses a robu...
Chavdar Papazov, Darius Burschka
CVPR
2009
IEEE
14 years 11 months ago
D - Clutter: Building object model library from unsupervised segmentation of cluttered scenes
Autonomous systems which learn and utilize a limited visual vocabulary have wide spread applications. Enabling such systems to segment a set of cluttered scenes into objects is ...
Chandra Kambhamettu, Dimitris N. Metaxas, Gowri So...
BMVC
1998
13 years 6 months ago
Learning Enhanced 3D Models for Vehicle Tracking
This paper presents an enhanced hypothesis verification strategy for 3D object recognition. A new learning methodology is presented which integrates the traditional dichotomic obj...
James M. Ferryman, Anthony D. Worrall, Stephen J. ...
DAGM
2006
Springer
13 years 9 months ago
Analysis on a Local Approach to 3D Object Recognition
Abstract. We present a method for 3D object modeling and recognition which is robust to scale and illumination changes, and to viewpoint variations. The object model is derived fro...
Elisabetta Delponte, Elise Arnaud, Francesca Odone...
ECCV
2004
Springer
14 years 7 months ago
Recognizing Objects in Range Data Using Regional Point Descriptors
Recognition of three dimensional (3D) objects in noisy and cluttered scenes is a challenging problem in 3D computer vision. One approach that has been successful in past research i...
Andrea Frome, Daniel Huber, Ravi Kolluri, Thomas B...