Sparse Features for Finger Detection

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Abstract:

The use of hand as direct input device evolved with the development of Natural User Interfaces (NUI). Touch screens are well integrated in our daily life and the new challenge is to implement interfaces which involve no direct contact. This paper presents such a solution implemented within the framework of sparse techniques. The feature vectors representing key distances and angles are extracted and used to detect fingers. The experimental results have demonstrated that this technique is able to obtain an error rate about 5% in finger detection.

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535-542

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June 2013

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[1] S. Malassiotis, F. Tsalakanidou, N. Mavridis, V. Giagourta, N. Grammalidis, M. G Strintzis, A face and gesture recognition system based on an active stereo sensor, Proceedings 2001 ICIP, 3 (2001) 955–958.

DOI: 10.1109/icip.2001.958283

Google Scholar

[2] M. Bray, Koller-Meier, L. V Gool, Smart particle filtering for 3D hand tracking, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, (2004), 675-680.

DOI: 10.1109/afgr.2004.1301612

Google Scholar

[3] P. Breuer, C. Eckes, S Muller, Hand Gesture Recognition with a novel IR Time-of-Flight Range Camera - A pilot study, Computer Vision / Computer Graphics Collaboration Techniques and Applications, (2007) 247–260.

DOI: 10.1007/978-3-540-71457-6_23

Google Scholar

[4] E. Kollorz, J. Penne, J. Hornegger, A. Barke, Gesture recognition with a time-of-flight camera. Int. J. Intell. Syst. Technol. Appl. 5(3/4), (2008) 334–343.

DOI: 10.1504/ijista.2008.021296

Google Scholar

[5] J. Molina, M. Escudero-Viñolo, A. Signoriello, M. Pardás, C. Ferrán, J. Bescós, F. Marqués, J. Martínez, Real-time user independent hand gesture recognition from time-of-flight camera video using static and dynamic models. Machine Vision and Applications (2011).

DOI: 10.1007/s00138-011-0364-6

Google Scholar

[6] Information on http: /www. microchip. com/pagehandler/en-us/press-release/microchips-new-gestic-technolo. html.

Google Scholar

[7] B. Ommer, J.M. Buhmann , Learning the Compositional Nature of Visual, CVPR'07, IEEE, (2007).

Google Scholar

[8] B. Ommer, J.M. Buhmann, Learning Compositional Categorization Models Objects ECCV'06, LNCS 3953, Springer, (2006).

Google Scholar

[9] Li Fei-Fei, Pietro Perona A Bayesian Hierarchical Model for Learning Natural Scene Categories , EEE Comp. Vis. Patt. Recog. ( 2005).

DOI: 10.1109/cvpr.2005.16

Google Scholar

[10] A. Oikonomopoulos, I. Patras, M. Pantic, Spatiotemporal Salient Points for Visual Recognition of Human Actions, Ieee Transactions On Systems, Man, And Cybernetics—Part B: Cybernetics, 36, (2006).

DOI: 10.1109/tsmcb.2005.861864

Google Scholar

[11] I. Laptev, T. Lindeberg, Space-time Interest Points, Proceedings of the Ninth IEEE International Conference on Computer Vision, (2003).

DOI: 10.1109/iccv.2003.1238378

Google Scholar

[12] J. Canny, Finding edges and lines in images, Tehnical Reoprt AITR-720, Massachusetts Institute of Technologie, (1983).

Google Scholar

[13] J. Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(1986) 679–698.

DOI: 10.1109/tpami.1986.4767851

Google Scholar

[14] Information on http: /www. ai. sri. com/pubs/files/tn036-duda71. pdf.

Google Scholar