Abstract
In this paper, we present a new recognition system for the fast detection and classification of objects in spatial 3D data. The system consists of two main components: A biologically motivated attention system and a fast classifier. Input is provided by a 3D laser scanner, mounted on an autonomous mobile robot, that acquires illumination independent range and reflectance data. These are rendered into images and fed into the attention system that detects regions of potential interest. The classifier is applied only to a region of interest, yielding a significantly faster classification that requires only 30% of the time of an exhaustive search. Furthermore, both the attention and the classification system benefit from the fusion of the bi-modal data, considering more object properties for the detection of regions of interest and a lower false detection rate in classification.
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References
Bennamoun, M., Mamic, G.: Object Recognition: Fundamentals and Case Studies. Springer, Heidelberg (2002)
Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57, 137–154 (2004)
Treisman, A., Gelade, G.: A feature integration theory of attention. Cognitive Psychology 12, 97–136 (1980)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. on Pattern Analysis & Machine Intelligence 20, 1254–1259 (1998)
Tsotsos, J.K., Culhane, S.M., Wai, W.Y.K., Lai, Y., Davis, N., Nuflo, F.: Modeling visual attention via selective tuning. AI 78, 507–545 (1995)
Backer, G., Mertsching, B., Bollmann, M.: Data- and model-driven gaze control for an active-vision system. IEEE Trans. on Pattern Analysis & Machine Intelligence 23(12), 1415–1429 (2001)
Surmann, H., Lingemann, K., Nüchter, A., Hertzberg, J.: A 3d laser range finder for autonomous mobile robots. In: Proc. 32nd Intl. Symp. on Robotics (ISR 2001), Seoul, South Korea, April 19–21, pp. 153–158 (2001)
Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology, 219–227 (1985)
Pessoa, L., Exel, S.: Attentional strategies for object recognition. In: Mira, J. (ed.) IWANN 1999. LNCS, vol. 1606, pp. 850–859. Springer, Heidelberg (1999)
Miau, F., Papageorgiou, C., Itti, L.: Neuromorphic algorithms for computer vision and attention. In: Proc. SPIE 46 Annual International Symposium on Optical Science and Technology, vol. 4479, pp. 12–23 (2001)
Johnson, A., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. on Pattern Analysis & Machine Intelligence 21, 433–449 (1999)
Ruiz-Correa, S., Shapiro, L.G., Meila, M.: A New Paradigm for Recognizing 3-D Object Shapes from Range Data. In: Proc. Int’l Conf. on Computer Vision (ICCV 2003), Nice, France (2003)
Frintrop, S., Rome, E., Nüchter, A., Surmann, H.: A bimodal laser-based attention system (submitted)
Draper, B., Lionelle, A.: Evaluation of selective attention under similarity transforms. In: Proc. of the Int’l Workshop on Attention and Performance in Computer Vision (WAPCV 2003), Graz, Austria, pp. 31–38 (2003)
Lee, K., Buxton, H., Feng, J.: Selective attention for cue-guided search using a spiking neural network. In: Proc. of the Int’l Workshop on Attention and Performance in Computer Vision (WAPCV 2003), Graz, Austria, pp. 55–62 (2003)
Frintrop, S., Rome, E., Nüchter, A., Surmann, H.: An Attentive, Multi-modal Laser “Eye”. In: Crowley, J., Piater, J., Vincze, M., Paletta, L. (eds.) ICVS 2003. LNCS, vol. 2626, pp. 202–211. Springer, Berlin (2003)
Papageorgiou, C., Oren, M., Poggio, T.: A general framework for object detection. In: Proc. 6th Int’l Conf. on Computer Vision (ICCV 1998), Bombay, India, pp. 555–562 (1998)
Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 297–304. Springer, Heidelberg (2003)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Machine Learning: Proc. 13th International Conference, pp. 148–156 (1996)
Parkhurst, D., Law, K., Niebur, E.: Modeling the role of salience in the allocation of overt visual attention. Vision Research 42, 107–123 (2002)
Ouerhani, N., von Wartburg, R., Hügli, H., Müri, R.: Empirical validation of the saliency-based model of visual attention. Elec. Letters on Computer Vision and Image Analysis 3, 13–24 (2004)
Nüchter, A., Surmann, H., Hertzberg, J.: Automatic Classification of Objects in 3D Laser Range Scans. In: Proc. 8th Conf. on Intelligent Autonomous Systems (IAS 2004), Amsterdam, The Netherlands, pp. 963–970. IOS Press, Amsterdam (2004)
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Frintrop, S., Nüchter, A., Surmann, H. (2005). Visual Attention for Object Recognition in Spatial 3D Data. In: Paletta, L., Tsotsos, J.K., Rome, E., Humphreys, G. (eds) Attention and Performance in Computational Vision. WAPCV 2004. Lecture Notes in Computer Science, vol 3368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30572-9_13
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DOI: https://doi.org/10.1007/978-3-540-30572-9_13
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