Object Recognition Using a Human-like Vision Analysis System
Keywords:
3D point cloud, feature extraction, HMM, object recognition.Abstract
This paper addresses the problem of recognizing three-dimensional objects, in which it considers not only the shape of objects but also the surface conditions. These two information sources are combined by using a human-like vision analysis, which can detect the shape features in 2D and the surface condition in 3D. Here we create two kinds of database from CAD models and real world objects for Hidden Markov Model. One is the 3D to 2D omni-directional projected shape data and the other is the surface condition data extracted from each projected direction. The shape feature and the surface condition are encoded experimentally by an easy way and saved as HMMs' observation data. By performing the same process, the hidden state dataset can be obtained from the real world objects. The result shows the surface condition is a very valid argument for an object recognition system.
References
[1] Bebis, S. Louis, and S. Fadali, “Using genetic algorithms for 3d object recognition,” in 11th International Conference on Computer Applications in Industry and Engineering, Las Vegas, USA, Nov. 1998, pp. 13–16.
[2] D. Lowe, “Object recognition from local scale-invariant features,” in The Proceedings of the Seventh IEEE International Conference, Kerkyra, Sep 20-27 1999.
[3] K. Lai, L. Bo, and D. Fox, “A large-scale hierarchical multiview rgb-d object dataset,” EEE International Conference on Robotics and Automation (ICRA), pp. 1817–1824, May 9-13 2011.
[4] K. Lai, L. Bo, X. Ren, and D. Fox, “Consumer depth cameras for computer vision,” Consumer Depth Cameras for Computer Vision, p. 167, 2013.
[5] K. Lai, L. Bo, and D. Fox, “Unsupervised feature learning for 3d scene labeling,” Robotics and Automation (ICRA), 2014 IEEE International Conference on, May 31- June 7 2014.
[6] D. Salomon, Data Compression: The Complete Reference, 2nd ed., 2000.
[7] R. B. Rusu and S. Cousins, “3d is here: Point cloud library (pcl),” in IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9-13 2011.
[8] J. Hornegger, H. Niemann, D. Paulus, and G. Schlottke, “Object recognition using hidden markov models,” Proc. Pattern Recognition in Practice, no. Informatik 5, pp. 1–9, 1994.
[9] GHAHRAMANI and ZOUBIN, “An introduction to hidden markov models and bayesian networks,” pp. 9–42, 2001.
[10] C. Harris and M. Stephens, “A combined corner and edge detector,” in In Proc. of Fourth Alvey Vision Conference, 1988, pp. 147–151
