Abstract
A novel approach for detecting single objects in large clusters is presented. The proposed method is designed to work with structure from motion data, which typically includes a set of input images, a very sparse point cloud and camera poses. We use provided objects of interest from 2D classification, which are then projected to three dimensional space.
The main contribution of this paper is an algorithm, which accurately detects the objects of interest and approximates their locations in three dimensional space, by using 2D classification data and quadric filtering. Optionally, a partly dense reconstructed mesh, containing objects of interest only, is computed, without the need for applying patch based multiple view stereo algorithms first. Experiments are performed on a challenging database containing images of wood log piles with a known ground truth number of objects, provided by timber processing companies. The average true positive rate exceeds 98.0 % in every case, while it is shown how to reduce the false positive rate to less than 0.5 %.
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Banerjee, J., Moelker, A., Niessen, W.J., van Walsum, T.: 3D LBP-based rotationally invariant region description. In: Park, J.-I., Kim, J. (eds.) ACCV Workshops 2012, Part I. LNCS, vol. 7728, pp. 26–37. Springer, Heidelberg (2013)
Bao, S.Y., Savarese, S.: Semantic structure from motion. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011, pp. 2025–2032. IEEE (2011)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. Int. J. Comput. Vis. 74(1), 59–73 (2007)
Buch, N., Orwell, J., Velastin, S.A.: 3D extended histogram of oriented gradients (3DHOG) for classification of road users in urban scenes. In: British Machine Vision Association (BMVC) (2009)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. June 2005
Fehr, J., Burkhardt, H.: 3D rotation invariant local binary patterns. In: ICPR, Citeseer, pp. 1–4 (2008)
Fei-Fei, L., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 524–531. IEEE (2005)
Fink, F.: Foto-optische Erfassung der Dimension von Nadelrundholzabschnitten unter Einsatz digitaler, bildverarbeitender Methoden. Ph.D. thesis, Albert-Ludwigs-Universitt (2004)
Furukawa, Y., Ponce, J.: Accurate, dense, and robust multiview stereopsis. IEEE Trans. Pattern Anal. Mach. Intel. 32(8), 1362–1376 (2010)
Gutzeit, E., Ohl, S., Kuijper, A., Voskamp, J., Urban, B.: Setting graph cut weights for automatic foreground extraction in wood log images. In: VISAPP (2), pp. 60–67 (2010)
Gutzeit, E., Ohl, S., Voskamp, J., Kuijper, A., Urban, B.: Automatic wood log segmentation using graph cuts. In: Richard, P., Braz, J. (eds.) VISIGRAPP 2010. CCIS, vol. 229, pp. 96–109. Springer, Heidelberg (2011)
Gutzeit, E., Voskamp, J.: Automatic segmentation of wood logs by combining detection and segmentation. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Fowlkes, C., Wang, S., Choi, M.-H., Mantler, S., Schulze, J., Acevedo, D., Mueller, K., Papka, M. (eds.) ISVC 2012, Part I. LNCS, vol. 7431, pp. 252–261. Springer, Heidelberg (2012)
Herbon, C., Tönnies, K., Stock, B.: Adaptive planar and rotational image stitching for mobile devices. In: Proceedings of the 5th ACM Multimedia Systems Conference, pp. 213–223. ACM (2014)
Herbon, C., Tönnies, K., Stock, B.: Detection and segmentation of clustered objects by using iterative classification, segmentation, and Gaussian mixture models and application to wood log detection. In: 36th German Conference on Pattern Recognition, Münster, 2–5 September 2014
Khan, S.M.: Multi-view Approaches to Tracking, 3D Reconstruction and Object Class Detection. Ph.D. thesis, University of Central Florida (2008)
Lewis, J.: Fast template matching. Vis. Interface 95, 15–19 (1995)
Liu, K., Skibbe, H., Schmidt, T., Blein, T., Palme, K., Ronneberger, O.: 3D rotation-invariant description from tensor operation on spherical HOG field. NeuroImage 57(2), 416–422 (2011)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, vol. 2, pp. 1150–1157. IEEE (1999)
Molton, N., Davison, A.J., Reid, I.: Locally planar patch features for real-time structure from motion. In: BMVC, pp. 1–10 (2004)
Moulon, P., Monasse, P., Marlet, R., et al.: Global fusion of relative motions for robust, accurate and scalable structure from motion. In: Proceedings of IEEE International Conference on Computer Vision (2013)
Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994, vol. 1, pp. 582–585 (1994)
Okamoto Jr., J., Grassi Jr., V.: Visual servo control of a mobile robot using omnidirectional vision. In: Proceedings of Mechatronics, pp. 413–422 (2002)
Scherer, M., Walter, M., Schreck, T.: Histograms of oriented gradients for 3d object retrieval. In: Proceedings of the WSCG, pp. 41–48 (2010)
Skibbe, H., Reisert, M., Burkhardt, H.: SHOG - spherical HOG descriptors for rotation invariant 3D object detection. In: Mester, R., Felsberg, M. (eds.) DAGM 2011. LNCS, vol. 6835, pp. 142–151. Springer, Heidelberg (2011)
Szeliski, R.: Image alignment and stitching: a tutorial. Found. Trends Comput. Graph. Vis. 2(1), 1–104 (2006)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001. vol. 1, pp. I-511-I-518 (2001)
Wang, B., Liang, W., Wang, Y., Liang, Y.: Head pose estimation with combined 2D sift and 3D HOG features. In: Seventh International Conference on Image and Graphics (ICIG 2013), pp. 650–655 July 2013
Yang, J., Liang, W., Jia, Y.: Face pose estimation with combined 2D and 3D HOG features. In: 21st International Conference on Pattern Recognition (ICPR), 2012, pp. 2492–2495 (2012)
Zwillinger, D.: CRC Standard Mathematical Tables and Formulae, 32nd Edn., Discrete Mathematics and Its Applications, CRC Press (2011)
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Herbon, C., Otte, B., Tönnies, K., Stock, B. (2014). Detection of Clustered Objects in Sparse Point Clouds Through 2D Classification and Quadric Filtering. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_44
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DOI: https://doi.org/10.1007/978-3-319-11752-2_44
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