Abstract
This paper analyzes a new 3D recognition method for occluded objects in complex scenes. The technique uses the Depth Gradient Image Based on Silhouette representation (DGI-BS) and settles the problem of identification-pose under occlusion and noise requirements. DGI-BS synthesizes both surface and contour information avoiding restrictions concerning the layout and visibility of the objects in the scene. Firstly, the paper is devoted to show the main properties of this method compared with a set of known techniques as well as to explain briefly the key concepts of the DGI-BS representation. Secondly, the performance of this strategy in real scenes under occlusion and noise circumstances is presented in detail.
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References
Planitz, B.M., Maeder, A.J., Williams, J.A.: The correspondence framework for 3D surface matching algorithms. Computer Vision and Image Understanding 97, 347–383 (2005)
Mamic, G., Bennamoun, M.: Representation and Recognition of 3D Free-Form Objects. Digital Signal Processing 12(1), 47–76 (2002)
Besl, P., McKay, N.: A method for registration of 3-D shapes. IEEE TPAMI 14(2), 239–256 (1992)
Thirion, J.P.: Extremal points: definition and application for 3D image registration. In: Proc. of the IEEE Conference on CVPR, pp. 587–592. IEEE Computer Society Press, Los Alamitos (1994)
Krsek, P., Pajdla, T., Hlavac, V.: Differential invariants as the base of triangulated surface registration. Computer Vision and Image Understanding 87(1-3), 27–38 (2002)
Stein, F., Medioni, G.: Structural indexing: efficient 3D object recognition. IEEE TPAMI 14(2), 125–145 (1992)
Besl, P.J., Jain, R.C.: Invariant surface characteristics for 3D object recognition in range images. Comput. Vision Graph. Image Process 33, 33–80 (1886)
Linnainmaa, S., Harwood, D., Davis, L.: Pose determination of a three-dimensional object using triangle pairs. IEEE TPAMI 10(5), 634–647 (1988)
Olson, C.F.: Efficient pose clustering using a randomized algorithm. Int. J. Comput. Vision 23(2), 131–147 (1997)
Chen, C.S., Hung, Y.P., Cheng, J.B.: RANSAC- based DARCES: a new approach to fast automatic registration of partially overlapping range images. IEEE TPAMI 21(11), 1229–1234 (1999)
Mokhtarian, F., Khalili, N., Yuen, P.: Multi-scale free-form 3D object recognition using 3D models. Image Vision Comput. 19, 271–281 (2001)
Wolfson, H.J., Rigoutsos, I.: Geometric hashing: an overview. IEEE Comput. Sci. Eng. 4(4), 10–21 (1997)
Hebert, M., Ikeuchi, K., Delingette, H.: A Spherical Representation for Recognition of Free-Form Surfaces. IEEE TPAMI 17(7), 681–690 (1995)
Dorai, C., Jain, A.K.: COSMOS-A Representation Scheme for 3-D Free-Form Objects. IEEE TPAMI 19(10), 1115–1130 (1997)
Chua, C.S., Jarvis, R.: Point Signatures: A new representation for 3D Object Recognition. International Journal of Computer Vision 25(1), 63–85 (1997)
Johnson, A.E., Hebert, M.: Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes. IEEE TPAMI 21(5), 433–449 (1999)
Zhang, D.: Harmonic Shape Images: A 3D free-form surface representation and its applications in surface marching. Ph.D. Thesis, CMU-RI-TR-99-41, CMU, Pittsburgh (1999)
Yamany, S.M., Farag, A.A.: Surface Signatures: An Orientation Independent free-form Surface Representation Scheme for the Purpose of Objects Registration and Matching. IEEE TPAMI 24(8) (2002)
Liu, X., Sun, R., Kang, S.B., Shum, H.: Directional Histogram Model for Three-Dimensional Shape Similarity. In: Proc. of the IEEE Conference on CVPR, IEEE Computer Society Press, Los Alamitos (2003)
Adán, A., Cerrada, C., Feliu, V.: Modeling Wave Set: Definition and Application of a New Topological Organization for 3D Object Modeling. Computer Vision and Image Understanding 79(2), 281–307 (2000)
Merchán, P., Adán, A., Salamanca, S.: Depth Gradient Image Based On Silhouette: A Solution for Reconstruction of Scenes in 3D Environments. In: Proc. of the 3DPVT (2006)
Merchán, P., Adán, A.: Exploration trees on highly complex scenes: A new approach for 3D segmentation. Pattern Recognition 40(7), 1879–1898
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Merchán, P., Adán, A. (2007). Analyzing DGI-BS: Properties and Performance Under Occlusion and Noise. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_6
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DOI: https://doi.org/10.1007/978-3-540-74607-2_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74606-5
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