Abstract
Object segmentation is an important capability for robotic systems, in particular for grasping. We present a graph-based approach for the segmentation of simple objects from RGB-D images. We are interested in segmenting objects with large variety in appearance, from lack of texture to strong textures, for the task of robotic grasping. The algorithm does not rely on image features or machine learning. We propose a modified Canny edge detector for extracting robust edges by using depth information and two simple cost functions for combining color and depth cues. The cost functions are used to build an undirected graph, which is partitioned using the concept of internal and external differences between graph regions. The partitioning is fast with \(\mathcal{O}(N log N)\) complexity. We also discuss ways to deal with missing depth information. We test the approach on different publicly available RGB-D object datasets, such as the Rutgers APC RGB-D dataset and the RGB-D Object Dataset, and compare the results with other existing methods.
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References
Gupta, S., Arbelaez, P., Malik, J.: Perceptual organization and recognition of indoor scenes from RGB-D images. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 564–571 (2013)
Ren, X., Bo, L., Fox, D.: RGB-(D) scene labeling: features and algorithms. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 2759–2766 (2012)
Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view RGB-D object dataset. In: 2011 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 1817–1824 (2011)
Kim, B.-S., Xu, S., Savarese, S.: Accurate localization of 3D objects from RGB-D data using segmentation hypotheses. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 3182–3189 (2013)
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 297–312. Springer, Cham (2014). doi:10.1007/978-3-319-10584-0_20
Abramov, A., Pauwels, K., Papon, J., Worgotter, F., Dellen, B.: Depth-supported real-time video segmentation with the kinect. In: 2012 IEEE Workshop on Applications of Computer Vision (WACV), pp. 457–464, January 2012
Mishra, A., Aloimonos, Y., Fah, C.L.: Active segmentation with fixation. In: 2009 IEEE 12th International Conference on Computer Vision. IEEE, pp. 468–475 (2009)
Mishra, A.K., Shrivastava, A., Aloimonos, Y.: “Segmenting simple objects using RGB-D. In: 2012 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 4406–4413 (2012)
Rao, D., Le, Q.V., Phoka, T., Quigley, M., Sudsang, A., Ng, A.Y.: Grasping novel objects with depth segmentation. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 2578–2585 (2010)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)
Gupta, S., Girshick, R., Arbeláez, P., Malik, J.: Learning rich features from RGB-D images for object detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 345–360. Springer, Cham (2014). doi:10.1007/978-3-319-10584-0_23
Amazon picking challenge. http://amazonpickingchallenge.org
Holzer, S., Rusu, R., Dixon, M., Gedikli, S., Navab, N.: Adaptive neighborhood selection for real-time surface normal estimation from organized point cloud data using integral images. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2684–2689, October 2012
Montabone, S., Soto, A.: Human detection using a mobile platform and novel features derived from a visual saliency mechanism. Image Vis. Comput. 28(3), 391–402 (2010)
Schäfer, H., Lenzen, F., Garbe, C.S.: Depth and intensity based edge detection in time-of-flight images. In: 3DV 2013, pp. 111–118 (2013)
Rutgers APC RGB-D dataset. http://pracsyslab.org/rutgers_apc_rgbd_dataset
RGB-D object dataset. http://rgbd-dataset.cs.washington.edu
Tejani, A., Tang, D., Kouskouridas, R., Kim, T.-K.: Latent-class hough forests for 3D object detection and pose estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 462–477. Springer, Cham (2014). doi:10.1007/978-3-319-10599-4_30
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This work was done in collaboration with TIM S.p.A.
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Toscana, G., Rosa, S., Bona, B. (2018). Fast Graph-Based Object Segmentation for RGB-D Images. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-56991-8_5
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