研究生: |
楊仁川 Edwin - Setiawan |
---|---|
論文名稱: |
Object Orientation Recognition Based on SIFT and SVM by Using Stereo Camera System Object Orientation Recognition Based on SIFT and SVM by Using Stereo Camera System |
指導教授: |
林其禹
Chyi-Yeu Lin |
口試委員: |
徐繼聖
G.S. Hsu 鍾國亮 Kuo-Liang Chung |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 機械工程系 Department of Mechanical Engineering |
論文出版年: | 2008 |
畢業學年度: | 96 |
語文別: | 英文 |
論文頁數: | 86 |
外文關鍵詞: | orientation recognition, stereo camera system |
相關次數: | 點閱:211 下載:0 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
The goal of this research is to recognize an object and its orientation by using stereo camera. The principle of object orientation recognition in this paper was based on the Scale Invariant Feature Transform (SIFT) and Support Vector Machine (SVM). SIFT was success on object recognition but it had a problem on recognizing the object orientation. Object recognition is important for industrial application, for example: many industries using industrial robot hand to grab some products. Not only object recognition will be needed in this process but also object orientation recognition. Object orientation is important for industrial robot hand to grab the object. In this paper we used SVM to recognize object orientation. SVM has been known as a promising method for classification accuracy and its generalization ability. In our experiments, we used stereo camera system because it was better than only by using one camera. Stereo camera system provided more information compare to single camera. I also implemented object orientation recognition for industrial robot hand. In the real application, our proposed method was used to recognize an object orientation. The gained information then became the input for the industrial robot hand which made it able to rotate the first object and put it above the second object with the same orientation.
The goal of this research is to recognize an object and its orientation by using stereo camera. The principle of object orientation recognition in this paper was based on the Scale Invariant Feature Transform (SIFT) and Support Vector Machine (SVM). SIFT was success on object recognition but it had a problem on recognizing the object orientation. Object recognition is important for industrial application, for example: many industries using industrial robot hand to grab some products. Not only object recognition will be needed in this process but also object orientation recognition. Object orientation is important for industrial robot hand to grab the object. In this paper we used SVM to recognize object orientation. SVM has been known as a promising method for classification accuracy and its generalization ability. In our experiments, we used stereo camera system because it was better than only by using one camera. Stereo camera system provided more information compare to single camera. I also implemented object orientation recognition for industrial robot hand. In the real application, our proposed method was used to recognize an object orientation. The gained information then became the input for the industrial robot hand which made it able to rotate the first object and put it above the second object with the same orientation.
[1] Lowe, D. G. 1999. Object recognition from local scale-invariant features. In International Conference on Computer Vision, Corfu, Greece, pp. 1150-1157.
[2] Grimson, Eric, and Lozano-Perez, T. 1987. Localizing overlapping parts by searching the interpretation tree. IEEE Trans. On Pattern Analysis and Machine Intelligence, 9, pp. 469-482.
[3] Lowe, D.G. 1987. Three-dimensional object recognition from single two-dimensional images. Artificial Intelligence, 31, 3, pp. 355-395.
[4] Nelson, R.C., and Selinger, A. 1998. Large-scale tests of a keyed, appearance-based 3-D object recognition system. Vision Research 38, 15, pp.2469-88.
[5] Basri, R., and Jacobs, D.W. 1997. Recognition using region correspondences. International Journal of Computer Vision, 25(2), pp. 141-162.
[6] Zhang, Z., Deriche, R., Faugeras, O., and Luong, Q.T. 1995. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence, 78, pp. 87-119.
[7] Schmid, C., and Mohr, R.1997. Local grayvalue invariants for image retrieval. IEEE PAMI, 19, 5, pp. 530-534.
[8] Baumberg, A. 2000. Reliable feature matching across widely separated views. In Conference on Computer Vision and Pattern Recognition, Hilton Head, South Carolina, pp. 774-781.
[9] Tuytelaars, T., and Van Gool, L. 2000. Wide baseline stereo based on local, affinely invariant regions. In British Machine Vision Conference, Bristol, UK, pp. 412-422.
[10] Mikolajczyk, K., and Schmid, C. 2002. An affine invariant interest point detector. In European Conference on Computer Vision (ECVV), Copenhagen, Denmark, pp. 128-142.
[11] Schaffalitzky, F., and Zisserman, A. 2002. Multi-view matching for unordered image sets, or ‘How do I organize my holiday snaps?’’’ In European Conference on Computer Vision, Copenhagen, Denmark, pp. 414-431.
[12] Brown, M. and Lowe, D.G. 2002. Invariant features from interest point groups. In British Machine Vision Conference, Cardiff, Wales, pp. 282-296.
[13] Lowe, D.G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60, 2, pp. 91-110.
[14] Hsu, C.-W., Chang, C.-C., and C.-J. Lin. 2007. A Practical Guide to Support Vector Classification.
[15] Witkin, A.P. 1983. Scale-space filtering. In International Joint Conference on Artificial Intelligence, Karlsruhe, Germany, pp. 1019-1022.
[16] Koenderink, J.J. 1984. The structure of images. Biological Cybernetics, 50, pp. 363-396.
[17] Lindeberg, T. 1994. Scale-space theory: A basic tool for analyzing structures at different scales. Journal of Applied Statistics, 21(2), pp. 224-270.
[18] Edelman, S., Intrator, N. and Poggio, T. 1997. Complex cells and object recognition. Unpublished manuscript: http://kybele.psych.cornell.edu/~edelman/ archive.html
[19] Luong, Q.T., and Faugeras, O.D. 1996. The fundamental matrix: Theory, algorithms, and stability analysis. International Journal of Computer Vision, 17(1), pp. 43-76.
[20] Hartley, R. and Zisserman, A. 2000. Multiple view geometry in computer vision, Cambridge University Press: Cambridge, UK.
[21] Vapnik, V. 1995. The Nature of Statistical Learnng Theory, Springer-Verlag. New York.
[22] Denso Robotics webpage: http://www.densorobotics.com/