Object recognition in probabilistic 3-d volumetric scenes

ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

Volumn 2, Issue , 2012, Pages 180-190

  Restrepo, Maria I ^a   Mayer, Brandon A ^a   Mundy, Joseph L ^a  

^a Brown University   (United States)

Author keywords

3 d data processing; 3 d object recognition; Bayesian learning; Machine vision

Indexed keywords

3D OBJECT; 3D OBJECT RECOGNITION; BACKGROUND MODELING; BAG-OF-FEATURES; BAYESIAN LEARNING; BAYESIAN UPDATING; CHANGE DETECTION; CLASSIFICATION ALGORITHM; CLASSIFICATION OF CHANGE; FEATURE DESCRIPTION; FEATURE TYPES; GAUSSIAN MIXTURE MODEL; MULTI-VIEW RECONSTRUCTION; OBJECT APPEARANCE; PARKING LOTS; PCA ANALYSIS; RECOGNITION SYSTEMS; RHODE ISLAND; TAYLOR SERIES EXPANSIONS; VIDEO SEQUENCES; VOLUME MODELS;

ALGORITHMS; COMPUTER VISION; DATA PROCESSING; HARVESTING; PARKS; TAYLOR SERIES; THREE DIMENSIONAL; THREE DIMENSIONAL COMPUTER GRAPHICS; VIDEO RECORDING;

OBJECT RECOGNITION;

EID: 84862178114     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (36)

1. Bariya, P. and Nishino, K. (2010). Scale-Hierarchical 3D Object Recognition in Cluttered Scenes. IEEE Conference on Computer Vision and Pattern Recognition.
2. Bradley, P. S. and Fayyad, U. M. (1998). Refining Initial Points for K-Means Clustering. In Proceedings of the 15th International Conference on Machine Learning.
3. Bronstein, A. M., Broinstein, M. M., Guibas, L. J., and Ovsjanikov, M. (2011). Shape Google: Geometric Words and Expressions for Invariant Shape Retrieval. ACM Transactions on Graphics.
4. Chan, T. F., Golub, G. H., and LeVeque, R. J. (1979). Updating Formulae and a Pairwise Algorithm for Computing Sample Variances. Technical report, Department of Computer Science. Stanford University.
5. Crispell, D., Mundy, J., and Taubin, G. (2008). Parallax- Free Registration of Aerial Video. In BMVC.
6. Crispell, D. E. (2010). A Continuous Probabilistic Scene Model for Aerial Imagery. PhD thesis, School of Engineering, Brown University.
7. Csurka, G., Dance, C. R., Fan, L., Willamowski, J., and Bray, C. (2004). Visual categorization with bags of keypoints. In In Workshop on Statistical Learning in Computer Vision, ECCV.
8. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE Conference on Computer Vision and Pattern Recognition.
9. Drost, B., Ulrich, M., Navab, N., and Ilic, S. (2010). Model Globally, Match Locally: Efficient and Robust 3D Object Recognition. IEEE Conference on Computer Vision and Pattern Recognition.
10. Elkan, C. (2003). Using the triangle inequality to accelerate k-means. In Proceedings of the Twentieth International Conference on Machine Learning.
11. Felzenszwalb, P., McAllester, D., and Ramanan, D. (2008). A discriminatively trained, multiscale, deformable part model. In IEEE Conference on Computer Vision and Pattern Recognition.
12. Fergus, R., Perona, P., and Zisserman, A. (2003). Object class recognition by unsupervised scale-invariant learning. In IEEE Conference on Computer Vision and Pattern Recognition.
13. Freeman, W. and Adelson, E. (1991). The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence.
14. Gupta, P., Arrabolu, S., Brown, M., and Savarese, S. (2009). Video scene categorization by 3D hierarchical histogram matching. In International Conference on Computer Vision.
15. Hamerly, G. and Elkan, C. (2003). Learning the k in kmeans. In Seventeenth annual conference on neural information processing systems (NIPS).
16. Harris, C. and Stephens, M. (1988). A combined corner and edge detector. Alvey vision conference.
17. Joachims, T. (1997). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In ECML.
18. Judd, D., McKinley, P., and Jain, A. (1998). Large-scale parallel data clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence.
19. Leung, T. and Malik, J. (1999). Recognizing surfaces using three-dimensional texton. In Proceedings of the Seventh IEEE International Conference on Computer Vision.
20. Lowe, D. G. (2004). Distinctive Image Features from Scale- Invariant Keypoints. International Journal of Computer Vision.
21. Maitra, R., Peterson, A. D., and Ghosh, A. P. (2010). A systematic evaluation of different methods for initializing the K-means clustering algorithm. In IEEE Transactions of Knowledge and Data Engineering.
22. Miller, A., Jain, V., and Mundy, J. (2011). Real-time Rendering and Dynamic Updating of 3-d Volumetric Data. In Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units.
23. Mundy, J. L. and Ozcanli, O. C. (2009). Uncertain geometry: a new approach to modeling for recognition. In 2009 SPIE Defense, Security and Sensing Conference.
24. Ö zcanli, O. and Mundy, J. (2010). Vehicle Recognition as Changes in Satellite Imagery. In International Conference on Pattern Recognition.
25. Papadakis, P., Pratikakis, I., and Theoharis, T. (2010). PANORAMA: A 3D Shape Descriptor Based on Panoramic Views for Unsupervised 3D Object Retrieval. International Journal of Computer Vision.
26. Pelleg, D. and Moore, A. (2000). X-means: Extending Kmeans with E cient Estimation of the Number of Clusters. In International Conference on Machine Learning.
27. Pollard, T. (2008). Comprehensive 3-d Change Detection Using Volumetric Appearance Modeling. PhD thesis, Division of Applied Mathematics, Brown University.
28. Pollard, T. and Mundy, J. (2007). Change Detection in a 3-d World. In IEEE Conference on Computer Vision and Pattern Recognition.
29. Raviv, D., Bronstein, M. M., Bronstein, A. M., and Kimmel, R. (2010). Volumetric heat kernel signatures. In 3DOR'10: Proceedings of the ACM workshop on 3D object retrieval.
30. Shapira, L., Shalom, S., Shamir, A., Cohen-Or, D., and Zhang, H. (2010). Contextual Part Analogies in 3D Objects. International Journal of Computer Vision.
31. Sipiran, I. and Bustos, B. (2010). A Robust 3D Interest Points Detector Based on Harris Operator. In Eurographics Workshop on 3D Object Retrieval.
32. Sivic, J., Russell, B., Efros, A., Zisserman, A., and Freeman, W. (2005). Discovering objects and their location in images. In International Conference on Computer Vision.
33. Snavely, N. and Seitz, S. (2006). Photo tourism: exploring photo collections in 3D. ACM Transactions on Graphics.
34. Thomas, A., Ferrar, V., Leibe, B., Tuytelaars, T., Schiel, B., and Van Gool, L. (2006). Towards Multi-View Object Class Detection. In IEEE Conference on Computer Vision and Pattern Recognition.
35. Varma, M. and Zisserman, A. (2009). A Statistical Approach to Material Classification Using Image Patch Exemplars. IEEE Transactions on Pattern Analysis and Machine Intelligence.
36. Zhang, J., Marszalek, M., Lazebnik, S., and Schmid, C. (2007). Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study. Int. J. Comput. Vision.