Abstract
We present a probabilistic 3D object recognition approach using multiple interpretations generation in cluttered domestic environment. How to handle pose ambiguity and uncertainty is the main challenge in most recognition systems. In our approach, invariant 3D lines are employed to generate the pose hypotheses as multiple interpretations, especially ambiguity from partial occlusion and fragment of 3D lines are taken into account. And the estimated pose is represented as a region instead of a point in pose space by considering the measurement uncertainties. Then, probability of each interpretation is computed reliably using Bayesian principle in terms of both likelihood and unlikelihood. Finally, fusion strategy is applied to a set of top ranked interpretations, which are further verified and refined to make more accurate pose estimation in real time. The experimental results support the potential of the proposed approach in the real cluttered domestic environment.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Roberts, L.G.: Machine perception of three-dimensional solids. In: Tipett, J.T. (ed.) Optical and Electrooptical Information Processing, pp. 159–197. MIT Press, Cambridge (1965)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)
Beis, J.S., Lowe, D.G.: Indexing without invariants in 3d object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 1000–1015 (1999)
Costa, M.S., Shapiro, L.G.: 3d object recognition and pose with relational indexing. Comput. Vis. Image Underst. 79, 364–407 (2000)
David, P., Dementhon, D., Duraiswami, R., Samet, H.: Softposit: Simultaneous pose and correspondence determination. International Journal of Computer Vision 59, 259–284 (2004)
Vicente, M.A., Hoyer, P.O., Hyvarinen, A.: Equivalence of some common linear feature extraction techniques for appearance-based object recognition tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 896–900 (2007)
Bicego, M., Castellani, U., Murino, V.: A hidden markov model approach for appearance-based 3d object recognition. Pattern Recognition Letters 26, 2588–2599 (2005)
Cyr, C.M., Kimia, B.B.: A similarity-based aspect-graph approach to 3d object recognition. International Journal of Computer Vision 57, 5–22 (2004)
Min, S., Hao, S., Savarese, S., Li, F.F.: A multi-view probabilistic model for 3d object classes. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1247–1254 (2009)
Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 433–449 (1999)
Frome, A., Huber, D., Kolluri, R., Bulow, T., Malik, J.: Recognizing objects in range data using regional point descriptors. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3023, pp. 224–237. Springer, Heidelberg (2004)
Ohbuchi, R., Osada, K., Furuya, T., Banno, T.: Salient local visual features for shape-based 3d model retrieval. In: IEEE International Conference on Shape Modeling and Applications, SMI 2008, pp. 93–102 (2008)
Shimshoni, I., Ponce, J.: Probabilistic 3d object recognition. International Journal of Computer Vision 36, 51–70 (2000)
David, P., DeMenthon, D.: Object recognition in high clutter images using line features. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1581–1588 (2005)
Zhaojin, L., Seungmin, B., Sukhan, L.: Robust 3d line extraction from stereo point clouds. In: IEEE Conference on Robotics, Automation and Mechatronics, pp. 1–5 (2008)
Bregler, C., Malik, J.: Tracking people with twists and exponential maps. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1998, pp. 8–15 (1998)
Genest, C., Zidek, J.V.: Combining probability distributions: A critique and an annotated bibliography. Statistical Science 1, 114–135 (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lu, Z., Lee, S., Kim, H. (2011). Probabilistic 3D Object Recognition Based on Multiple Interpretations Generation. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19282-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-19282-1_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19281-4
Online ISBN: 978-3-642-19282-1
eBook Packages: Computer ScienceComputer Science (R0)