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
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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“Rajeswaran et al. [33] explore different training strategies for learning from an ensemble of models, including adversarial training and adapting the ensemble distribution using data from the target domain, but also do not demonstrate successful real-world transfer.”
From Domain randomization for transferring deep neural networks from simulation to the real world (2017) · cites this paper as [33]
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Cites
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Cited by
Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202133 · Cited by 3,124
“, Scale-Invariant Feature Transform [SIFT] features [14] or color co-occurrence histograms [12]) are matched with the 3D models (or features from the 3D models).”
MethodHERB: a home exploring robotic butler
Srinivasa, S. S., Ferguson, D., Helfrich, C. J., Berenson, D., Collet, A., Diankov, R., Gallagher, G., Hollinger, G., Kuffner, J., & Weghe, M. V. (2010) · Autonomous Robots · doi:10.1007/s10514-009-9160-9 · Cited by 306
Object recognition and full pose registration from a single image for robotic manipulation
Collet, A., Berenson, D., Srinivasa, S. S., & Ferguson, D. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152739 · Cited by 280
Learning and Evaluation of the Approach Vector for Automatic Grasp Generation and Planning
Ekvall, S., & Kragić, D. (2007) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2007.364205 · Cited by 109
Self-citationBonev, B., Escolano, F., & Cazorla, M. (2008) · Pattern Analysis and Applications · doi:10.1007/s10044-008-0107-0 · Cited by 70
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)
- 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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- Ekvall, S., Kragic, D., & Hoffmann, F. (2005). Object recognition and pose estimation using color cooccurrence histograms and geometric modeling. Image and Vision Computing. https://doi.org/10.1016/j.imavis.2005.05.006
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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