Abstract
In this paper we describe a pseudo-3D vision-based dual approach for Machine-Awareness in indoor environment. The so-called duality is provided by color and depth cameras of Kinect system, which presents an appealing potential for 3D robots vision. Placing the human-machine (including human-robot) interaction as a primary outcome of the intended visual Machine-Awareness in investigated system, we aspire proffering the machine the autonomy in awareness about its surrounding environment. Combining pseudo-3D vision, and salient objects’ detection algorithms, the investigated approach seeks an autonomous detection of relevant items in 3D environment. The pseudo-3D perception allows reducing computational complexity inherent to the 3D vision context into a 2D computational task by processing 3D visual information within a 2D-images’ framework. The statistical foundation of the investigated approach proffers it a solid and comprehensive theoretical basis, holding out a bottom-up nature making the issued system unconstrained regarding prior hypothesis. We provide experimental results validating the proposed system.
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References
de Greeff, J., Delaunay, F., Belpaeme, T.: Human-robot interaction in concept acquisition: a computational model. In: Proceedings of International Conference on Development & Learning, pp. 1–6 (2009)
Araki, T., Nakamura, T., Nagai, T., Funakoshi, K., Nakano, M., Iwahashi, N.: Autonomous acquisition of multimodal information for online object concept formation by robots. In: Proceedings of IEEE/IROS, pp. 1540–1547 (2011)
Kinect camera http://www.xbox.com/en-US/kinect/default.htm
Han, J., Shao, L., Xu, D., Shotton, J.: Enhanced computer vision with microsoft kinect sensor: a review. IEEE Trans. Cybern. 43(5), 1318–1334 (2013)
Zhang, Z.: Microsoft kinect sensor and its effect. IEEE Multimedia Mag. 19(2), 4–10 (2012)
Khoshelham, K., Elberink, S.O.: Accuracy and resolution of kinect depth data for indoor mapping applications. Sensors 12(2), 1437–1454 (2012)
Camplani, M., Mantecon, T., Salgad, L.: Depth-color fusion strategy for 3-D scene modeling with kinect. IEEE Trans. Cybern. 43(6), 1560–1571 (2013)
Lloyd, R., Closkey, S.M.: Recognition of 3D package shapes for single camera metrology. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision (IEEE-WACV 2014), pp. 99–106 (2014)
Skalski, A., Machura, B.: Metrological analysis of microsoft kinect in the context of object localization. J. Metril. Measure. Syst. 22(4), 469–478 (2015)
Zolkiewski, S., Pioskowik, D.: Robot control and online programming by human gestures using a kinect motion sensor. In: Rocha, Á., Correia, A.M., Tan, F.B., Stroetmann, K.A. (eds.) New Perspectives in Information Systems and Technologies, Volume 1. AISC, vol. 275, pp. 593–604. Springer, Cham (2014). doi:10.1007/978-3-319-05951-8_56
Stone, E.E., Skubic, M.: Fall detection in homes of older adults using the microsoft kinect. IEEE J. Biomed. Health Inf. 19(1), 290–301 (2015)
González-Jorge, H., Zancajo, S., González-Aguilera, D., Arias, P.: Application of kinect gaming sensor in forensic science. J. Forensic Sci. 60(1), 206–211 (2015)
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1597–1604 (2009)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20, 1254–1259 (1998)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. Adv. Neural. Inf. Process. Syst. 19, 545–552 (2007)
Achanta, R., Estrada, F., Wils, P., Süsstrunk, S.: Salient region detection and segmentation. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 66–75. Springer, Heidelberg (2008). doi:10.1007/978-3-540-79547-6_7
Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.-Y.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2001)
Liang, Z., Chi, Z., Fu, H., Feng, D.: Salient object detection using content-sensitive hypergraph representation and partitioning. Pattern Rec. 45(11), 3886–3901 (2012)
Ramik, D.M., Sabourin, C., Madani, K.: Hybrid salient object extraction approach with automatic estimation of visual attention scale. In: Proceedings of IEEE SITIS, pp. 438–445 (2011)
Ramik, D.M., Sabourin, C., Moreno, R., Madani, K.: A machine learning based intelligent vision system for autonomous object detection and recognition. J. Appl. Intell. 40(2), 358–375 (2014)
Moreno, R., Ramik, D.M., Graña, M., Madani, K.: Image segmentation on the spherical coordinate representation of the RGB color space. IET Image Proc. 6(9), 1275–1283 (2012)
Desingh, K., Madhava Krishna, K., Rajan, D., Jawahar, C.V.: Depth really matters: improving visual salient region detection with depth. In: Proceedings of 24th British Machine Vision Conference (BMVC 2013), Bristol, UK, pp. 98.1–98.11 (2013)
Peng, H., Li, B., Xiong, W., Hu, W., Ji, R.: RGBD salient object detection: a benchmark and algorithms. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 92–109. Springer, Cham (2014). doi:10.1007/978-3-319-10578-9_7
Cheng, Y., Fu, H., Wei, X., Xiao, J., Cao, X.: Depth enhanced saliency detection method. In: Proceedings of International Conference on Internet Multimedia Computing and Service (ICIMCS 2014), Xiamen, China, pp. 23–27 (2014)
Tang, Y., Tong, R., Tang, M., Zhang, Y.: Depth incorporating with color improves salient object detection. Vis. Comput. 32(1), 111–121 (2016)
Bertasius, G., Park, H.S., Shi, J.: Exploiting egocentric object prior for 3D saliency detection (2015). Preprint arXiv:1511.02682, arxiv.org
Cheng, M.M., Mitra, N.J., Huang, X.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)
Riche, N., Duvinage, M., Mancas, M., Gosselin, B., Dutoit, T.: Saliency and human fixations: state-of-the-art and study of comparison metrics. In: Proceedings of IEEE ICCV, pp. 1153–1160 (2013)
Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861–874 (2006)
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Fraihat, H., Madani, K., Sabourin, C. (2017). A Pseudo-3D Vision-Based Dual Approach for Machine-Awareness in Indoor Environment Combining Multi-resolution Visual Information. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_55
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