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Cited by 54Advanced Image and Video Retrieval TechniquesRobot Manipulation and LearningAdvanced Neural Network Applications

The DOI matches its Crossref record; 39 of the 45 references it cites were found in a registry or an index.

Object recognition and pose estimation using color cooccurrence histograms and geometric modeling

Ekvall, Staffan, Kragic, Danica, Hoffmann, Frank·2005·Image and Vision Computing
DOI 10.1016/j.imavis.2005.05.006
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Abstract

Robust techniques for object recognition and pose estimation are essential for robotic manipulation and object grasping. In this paper, a novel approach for object recognition and pose estimation based on color cooccurrence histograms and geometric modelling is presented. The particular problems addressed are: (i) robust recognition of objects in natural scenes, (ii) estimation of partial pose using an appearance based approach, and (iii) complete 6DOF model based pose estimation and tracking using geometric models. Our recognition scheme is based on the color cooccurrence histograms embedded in a classical learning framework that facilitates a 'winner-takes-all' strategy across different views and scales. The hypotheses generated in the recognition stage provide the basis for estimating the orientation of the object around the vertical axis. This prior, incomplete pose information is subsequently made precise by a technique that facilitates a geometric model of the object to estimate and continuously track the complete 6DOF pose of the object. Major contributions of the proposed system are the ability to automatically initiate an object tracking process, its robustness and invariance towards scaling and translations as well as the computational efficiency since both recognition and pose estimation rely on the same representation of the object. The performance of the system is evaluated in a domestic environment with changing lighting and background conditions on a set of everyday objects.

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Metadata

Authors
Ekvall, S., Kragic, D., & Hoffmann, F.
Affiliations
KTH Royal Institute of Technology (SE)
Venue
Image and Vision Computing
Publisher
Elsevier BV
Published
2005-10
Volume
23
Issue
11
Pages
943-955
ISSN
0262-8856 (ISSN-L)
DOI
10.1016/j.imavis.2005.05.006
OpenAlex
W2162866804
Cited by
52
References
45
Citations by year
2025: 1 · 2020: 3 · 2019: 1 · 2017: 1 · 2016: 5 · 2015: 3 · 2014: 5 · 2013: 6 · 2012: 7
FWCI
2.26
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
Image and Vision Computing (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
  • Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
object recognition, pose estimation, geometric modeling, object tracking, appearance-based recognition, robotic manipulation, object grasping, scale and translation invariance, natural images

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.