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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
(2012) · Encyclopedia of the Sciences of Learning · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,370
Russell, E. L., Chiang, L. H., & Braatz, R. D. (2000) · Advances in Industrial Control · doi:10.1007/978-1-4471-0409-4_3 · Cited by 7,862
Introduction to Robotics mechanics and Control
Craig, J. (1986) · Cited by 5,024
Computer graphics: principles and practice
(2014) · Choice Reviews Online · doi:10.5860/choice.51-2713 · Cited by 4,651
Estimation and Tracking: Principles, Techniques, and Software
Bar‐Shalom, Y., & Li, X. (1993) · Medical Entomology and Zoology · Cited by 1,910
Introduction to robotics: Mechanics and control
Rodd, M. G. (1987) · Automatica · doi:10.1016/0005-1098(87)90105-1 · Cited by 1,505
Perceptual organization and visual recognition
(1985) · Computer Vision, Graphics, and Image Processing · doi:10.1016/0734-189x(85)90041-6 · Cited by 1,022
Model-based object pose in 25 lines of code
Dementhon, D. F., & Davis, L. S. (1995) · International Journal of Computer Vision · doi:10.1007/bf01450852 · Cited by 1,009
Fitting parameterized three-dimensional models to images
Lowe, D. G. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.134043 · Cited by 837
Support vector machines for 3D object recognition
Pontil, M., & Verri, A. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.683777 · Cited by 812
Model-based object tracking in monocular image sequences of road traffic scenes
Roller, D., Daniilidis, K., & Nagel, H. H. (1993) · International Journal of Computer Vision · doi:10.1007/bf01539538 · Cited by 651
Dynamic monocular machine vision
Dickmanns, E. D., & Graefe, V. (1988) · Machine Vision and Applications · doi:10.1007/bf01212361 · Cited by 353
Stable real-time 3D tracking using online and offline information
Vacchetti, L., Lepetit, V., & Fua, P. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.92 · Cited by 328
Subspace methods for robot vision
Nayar, S. K., Nene, S. A., & Murase, H. (1996) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.538979 · Cited by 208
Object Recognition using Local Affine Frames on Distinguished Regions
Obdrzalek, S., & Matas, J. (2002) · Procedings of the British Machine Vision Conference 2002 · doi:10.5244/c.16.9 · Cited by 198
Object recognition with color cooccurrence histograms
Chang, P., & Krumm, J. (2003) · Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) · doi:10.1109/cvpr.1999.784727 · Cited by 164
A real-time tracker for markerless augmented reality
Comport, A. I., Marchand, É., & Chaumette, F. (2003) · doi:10.5555/946248.946787 · Cited by 157
A real-time tracker for markerless augmented reality
Comport, A. I., Marchand, É., & Chaumette, F. (2004) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar.2003.1240686 · Cited by 111
Real-time visual tracking of 3d objects with dynamic handling of occlusion
Wunsch, P., & Hirzinger, G. (2002) · Proceedings of International Conference on Robotics and Automation · doi:10.1109/robot.1997.606722 · Cited by 93
Object recognition using composed receptive field histograms of higher dimensionality
Linde, O., & Lindeberg, T. (n.d.) · Cited by 74
Experiments on augmenting CONDENSATION for mobile robot localization
Jensfelt, P., Wijk, O., Austin, D. J., & Andersson, M. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2000.846407 · Cited by 65
Robust Vision for Vision-Based Control of Motion
Vincze, M., & Hager, G. D. (2000) · Institute of Electrical and Electronics Engineers eBooks · doi:10.1109/9780470546369 · Cited by 61
Object recognition using composed receptive field histograms of higher dimensionality
Linde, O., & Lindeberg, T. (2004) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2004.1333965 · Cited by 59
Fusion of Multimodal Visual Cues for Model-Based Object Tracking
Taylor, G., & Kleeman, L. (2003) · Cited by 49
Real-Time Tracking of Multiple Articulated Structures in Multiple Views
Drummond, T. W., & Cipolla, R. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45053-x_2 · Cited by 47
Visual Servoing for Manipulation : Robustness and Integration Issues
Kragić, D. (2001) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 47
Real-time pose estimation of 3D objects from camera images using neural networks
Wunsch, P., WINKLER, S. S., & Hirzinger, G. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1997.606781 · Cited by 44
Systems integration for real-world manipulation tasks
Petersson, L., Jensfelt, P., Tell, D., Strandberg, M., Kragić, D., & Christensen, H. I. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2002.1013607 · Cited by 44
Probabilistic object recognition and localization
Schiele, B., & Pentland, A. (1999) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1999.791215 · Cited by 39
DCA: a distributed control architecture for robotics
Petersson, L., Austin, D. J., & Christensen, H. I. (2002) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2001.976423 · Cited by 31
Model based techniques for robotic servoing and grasping
Kragić, D., & Christensen, H. I. (2003) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/irds.2002.1041405 · Cited by 28
Human visual servoing for reaching and grasping: the role of 3D geometric features
Hu, Y., Eagleson, R. A., & Goodale, M. A. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1999.774087 · Cited by 26
A Fully Projective Formulation for Lowe's Tracking Algorithm
Araújo, H. J., Carceroni, R. L., & Brown, C. C. (1996) · UR Research (University of Rochester) · Cited by 25
An Integrating Framework for Robust Real-Time 3D Object Tracking
Vincze, M., Ayromlou, M., & Kubinger, W. (1999) · Lecture notes in computer science · doi:10.1007/3-540-49256-9_9 · Cited by 20
Edge-Projected Integration of Image and Model Cues for Robust Model-Based Object Tracking
Vincze, M., Ayromlou, M., Ponweiser, W., & Zillich, M. (2001) · The International Journal of Robotics Research · doi:10.1177/02783640122067534 · Cited by 20
Automatic dismantling integrating optical flow into a machine vision-controlled robot system
Gengenbach, V., Nagel, H.-H., Tonko, M., & Schafer, K. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1996.506889 · Cited by 19
Pedagogical support vector learning : a pure learning approach to object recognition
Roobaert, D. (2001) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 19
Multi-scale feature tracking and motion estimation
Bretzner, L. (1999) · Cited by 13
Two-Dimensional ModelBased Tracking of Complex Shapes for Visual Servoing Tasks
(2009) · Institute of Electrical and Electronics Engineers eBooks · doi:10.1109/9780470546369.ch6 · Cited by 5
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) · 2009 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2009.5152739 · Cited by 275
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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