Object Detection Using SURF and Superpixels

Abstract

This paper proposes a novel object detection method in which a set of local features inside the superpixels are extracted from the image under analysis acquired by a 3D visual sensor. To increase the segmentation accuracy, the proposed method firstly performs the segmentation of the image, under analysis, using the Simple Linear Iterative Clustering (SLIC) superpixels method. Next the key points inside each superpixel are estimated using the Speed-Up Robust Feature (SURF). These key points are then used to carry out the matching task for every detected keypoints of a scene inside the estimated superpixels. In addition, a probability map is introduced to describe the accuracy of the object detection results. Experimental results show that the proposed approach provides fairly good object detection and confirms the superior performance of proposed scene compared with other recently proposed methods such as the scheme proposed by Mae et al.

Share and Cite:

M. Lopez-de-la-Calleja, T. Nagai, M. Attamimi, M. Nakano-Miyatake and H. Perez-Meana, "Object Detection Using SURF and Superpixels," Journal of Software Engineering and Applications, Vol. 6 No. 9, 2013, pp. 511-518. doi: 10.4236/jsea.2013.69061.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] K. Lai, L. Bo, X. Ren and D. Fox, (2011) “A Large-Scale Hierarchical Multi-View RGB-D Object Dataset,” Proceedings of International Conference on Robotics and Automation, Shanghai, 2011, pp. 1817-1827.
[2] M. ozuysal, V. Lepetit and P. Fua, “Pose Estimation for Category Specific Multiview Object Localization,” Proceedings of International Conference on Computer Vision and Pattern Recognition, Miami, 20-25 June 2009, pp. 775-785.
[3] H. Harzallah, F. Jurie and C. Schmid, “Combining Efficient Object Localization and Image Classification,” International Conference on Computer Vision (ICCV), Kyoto, 29 September -2 October 2009, pp. 237-244.
[4] N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, 25 June 2005, pp. 886-893.
[5] G. Bouchard and B. Triggs, “A Hierarchical Part-Based Model for Visual Object Categorization,” Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, 20-25 June 2005, pp. 710-715.
[6] F. Lafarge, X. Descombe, J. Zerubia and P. Desillingy, “Structural Approach for building Reconstruction from a Single DSM,” IEEE Trans on Pattern Analysis and Machine Intelligence, Vol. 32, No. 1, 2010, pp. 135-147. doi:10.1109/TPAMI.2008.281
[7] F. Lafarge, X Descombe., J. Zerubia and P. Desillingy, “Structural Approach for Building Reconstruction from a Single DSM,” IEEE Trans on Pattern Analysis and Machine Intelligence, Vol. 32, No. 1, 2010, pp. 135-147. doi:10.1109/TPAMI.2008.281
[8] Y. Mae, J. Choi, H. Takahashi, K. Ohara, T. Takubo and T. Arai, “Interoperable Vision Component for Object Detection and 3D Pose Estimation for Modularized Robot Control,” Mechatronics, Vol. 21, No. 6, 2011, pp. 983-992. doi:10.1016/j.mechatronics.2011.03.008
[9] B. Leibe, A. Leonardis and B. Schiele, “Combined Object Categorization and Segmentation with an Implicit Shape Model,” Workshop on Statistical Learning in Computer Vision, Prague, May 2004, pp. 1-16.
[10] J. Gall and V. Lempitsky, “Class-Specific Hough Forests for Object Detection,” Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR), Miami, 20-25 June 2009, pp. 1022-1029.
[11] S. Lazebnik, C. Schmid and J. Ponce, “Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories,” Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR), New York, 2006, pp. 2169-2178.
[12] J. Shotton, M. Winn, C. Rother and A. Criminisi, “Textonboost: Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation,” Lecture Notes in Computer Science, Vol. 3951, 2006, pp. 1-15.
[13] P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, 2001, pp. 511-518.
[14] A. Andreas-Opelt and A. Zisserman, “A Boundary-Fragment-Model for Object Detection,” Lecture Notes in Computer Science, Vol. 3952, 2006, pp. 575-578. doi:10.1007/11744047_44
[15] J. Ponce, S. Lazebnik, F. Rothganger and C. Schmid, “Toward True 3d Object Recognition,” Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR), Washington, 2004, pp. 4034-4041.
[16] M. Attami, A. Mizutami, T. Nakamura, T. Nagai, K. Funakoshi and M. Nakano, “Real-Time 3D Visual Sensor for Robust Object Recognition,” Proceedings of International Conference on Intelligent Robots and Systems, Taipei, 18-22 October 2010, pp. 4560-4565.
[17] D. Hoiem, A. Efros and M. Hebert, “Automatic Photo Pop-Up,” Proceedings of International Conference on Computer Graphics and Interactive Techniques, (SIG-GRAPH), Los Angeles, July 2005, pp. 1-8.
[18] Y. Li, J. Sun, C. Tang and H. Shum, “Lazy Snapping,” Proceedings International Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), Los Angeles, 2004, pp. 303-308.
[19] X. He, R. Zemel and D. Ray, “Learning and Incorporating Top-Down Cues in Image Segmentation,” Lecture Notes in Computer Science, Vol. 3951, 2006, pp. 338-351. doi:10.1007/11744023_27
[20] B. Fulkerson, A. Vedaldi and S. Soatto, “Class Segmentation and Object Localization with Superpixel Neighborhoods,” Proceedings of International Conference on Computer Vision, (ICCV), Nara, 29 September-2 October 2009, pp. 670-677.
[21] X. Ren and J. Malik, “Learning a Classification Model for Segmentation,” Proceedings of International Conference on Computer Vision (ICCV), Nice, 13-16 October 2003, pp. 10-17. doi:10.1109/ ICCV.2003.1238308
[22] G. Mori, “Guiding Model Search Using Segmentation,” Proceeding of International Conference on Computer Vision, (ICCV), Las Vegas, 17-21 October 2005, pp. 1417-1423.
[23] P. Felzenszwalb and D. Huttenlocher, “Efficient Graph-Based Image Segmentation,” International Journal of Computer Vision, Vol. 9, No. 2, 2004, pp. 167-181.
[24] A. Vedaldi and S. Soatto, “Quick Shift and Kernel Methods for Mode Seeking,” European Conference on Computer Vision, Marseille, 2008, pp. 705-718.
[25] A. Levinshtein, A. Stere, K. Kutulakos, D. Fleet, S. Dickinson and K. Siddiqi, “Turbopixels: Fast Superpixels Using Geometric Flows,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 12, 2009, pp. 2290-2297. doi:10.1109/TPAMI.2009.96
[26] A. Moore, S. Prince, J. Warrell, U. Mohammed and G. Jones, “Superpixel Lattices,” Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, 23-28 June 2008, pp. 1-8.
[27] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua and S. Süsstrunk, “SLIC Superpixels,” School of Computer and Communications Sciences, EPFL Technical Report 149300, 2010.
[28] B. Herbert, E. Andreas, T. Tinne and V. Luc, “Speed-Up Robust Features (SURF),” Computer Vision and Image Understanding, Vol. 110, No. 3, 2008, pp. 346-359. doi:10.1016/j.cviu.2007.09.014
[29] L. Qin, W. Josephson, Z. Wang and C. Kai-Li, “Multi-Probe LSH: Ef?cient Indexing for High-Dimensional Similarity Search,” Proceedings of Very Large Database Conference, Vienna, 23-28 September 2007, pp. 950-961.
[30] S. Har-Peled, P. Indyk and R. Motwani, “Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality,” Theory of Computing, Vol. 8, No. 1, 2012, pp. 321-350.
[31] O. Miksik and K. Mikolajczyk, “Evaluation of Local Detectors and Descriptors for Fast Feature Matching,” Proceedings of International Conference on Pattern Recognition, Tsukuba, 2012, pp. 2681-2684.
[32] Y. Ke and R. Sukthankar, “PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,” Workshop on Generic Object Recognition and Categorization, Washington DC, 27 June-2 July 2004, pp. 506-513.
[33] K. Mikolajczyk and C. Schmid, “A Performance Evaluation of Local Descriptors,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 10, 2005, pp. 1615-1630. doi:10.1109/ TPAMI.2005.188
[34] http://www.robots.ox.ac.uk/~vgg/research/affine/
[35] D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, Vol. 20, No. 1, 2004, pp. 91-110. doi:10.1023/B:VISI.0000029664.99615.94
[36] M. Lopez-de-la-Calleja, T. Nagai and H. Perez-Meana, “Superpixel-Based Object Detection Using Local Feature Matching,” Proceedings of the 29th Conference of the Robotics Society of Japan, Toyosu, 2011, pp. 11-17.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.