A Defocus Based Novel Keyboard Design
Pages 363 - 379
Abstract
Defocus based Depth estimation has been widely applied for constructing 3D setup from 2D image(s), reconstructing 3D scenes and image refocusing. Using defocus enables us to infer depth information from a single image using visual clues which can be captured by a monocular camera. In this paper, we propose an application of Depth from Defocus to a novel, portable keyboard design. Our estimation technique is based on the concept that depth of the finger with respect to our camera and its defocus blur value is correlated, and a map can be obtained to detect the finger position accurately. We have utilised the near-focus region for our design, assuming that the closer an object is to our camera, more will be its defocus blur. The proposed keyboard can be integrated with smartphones, tablets and Personal Computers, and only requires printing on plain paper or projection on a flat surface. The detection approach involves tracking the finger’s position as the user types, measuring its defocus value when a key is pressed, and mapping the measured defocus together with a precalibrated relation between the defocus amount and the keyboard pattern. This is utilised to infer the finger’s depth, which, along with the azimuth position of the stroke, identifies the pressed key. Our minimalistic design only requires a monocular camera, and there is no need for any external hardware. This makes the proposed approach a cost-effective and feasible solution for a portable keyboard.
References
[1]
Kölsch, M., Turk, M.: Keyboards without keyboards: a survey of virtual keyboards. In: Workshop on Sensing and Input for Media-Centric Systems, Santa Barbara, CA (2002)
[2]
Kim JR and Tan HZ Auvray M and Duriez C Haptic feedback intensity affects touch typing performance on a flat keyboard Haptics: Neuroscience, Devices, Modeling, and Applications 2014 Heidelberg Springer 369-375
[3]
Kanevsky, D., Sabath, M., Zlatsin, A.: Virtual invisible keyboard. US Patent 7,042,442, 9 May 2006
[4]
Habib, H.A., Mufti, M.: Real time mono vision gesture based virtual keyboard system. IEEE Trans. Consum. Electron. 52(4), 1261–1266 (2006)
[5]
Du, H., Charbon, E.: A virtual keyboard system based on multi-level feature matching. In: 2008 Conference on Human System Interactions, pp. 176–181. IEEE (2008)
[6]
Murase, T., Moteki, A., Suzuki, G., Nakai, T., Hara, N., Matsuda, T.: Gesture keyboard with a machine learning requiring only one camera. In: Proceedings of the 3rd Augmented Human International Conference, p. 29. ACM (2012)
[7]
Huan, D., Oggier, T., Lustenberger, F., Charbon, E.: A virtual keyboard based on true-3D optical ranging. In: Proceedings of the British Machine Vision Conference, vol. 1, pp. 220–229 (2005)
[8]
Su, X., Zhang, Y., Zhao, Q., Gao, L.: Virtual keyboard: a human-computer interaction device based on laser and image processing. In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 321–325. IEEE (2015)
[9]
Lee, M., Woo, W.: ARKB: 3D vision-based augmented reality keyboard. In: ICAT (2003)
[10]
Adajania, Y., Gosalia, J., Kanade, A., Mehta, H., Shekokar, N.: Virtual keyboard using shadow analysis. In: 2010 3rd International Conference on Emerging Trends in Engineering and Technology, pp. 163–165. IEEE (2010)
[11]
Posner, E., Starzicki, N., Katz, E.: A single camera based floating virtual keyboard with improved touch detection. In: 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, pp. 1–5. IEEE (2012)
[12]
Grossmann P Depth from focus Pattern Recogn. Lett. 1987 5 1 63-69
[13]
Bülthoff HH and Mallot HA Integration of depth modules: stereo and shading J. Opt. Soc. Am. A 1988 5 10 1749
[14]
Ullman S The interpretation of structure from motion Proc. R. Soc. Lond. Ser. B Biol. Sci. 1979 203 1153 405-426
[15]
Levin A, Fergus R, Durand F, and Freeman WT Image and depth from a conventional camera with a coded aperture ACM Trans. Graph. 2007 26 3 70
[16]
Khoshelham K and Elberink SO Accuracy and resolution of Kinect depth data for indoor mapping applications Sensors 2012 12 2 1437-1454
[17]
Srikakulapu, V., Kumar, H., Gupta, S., Venkatesh, K.S.: Depth estimation from single image using defocus and texture cues. In: 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 1–4. IEEE (2015)
[18]
Magoulès, F., Zou, Q.: A novel contactless human machine interface based on machine learning. In: 2017 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES), pp. 137–140. IEEE (2017)
[19]
AlKassim, Z.: Virtual laser keyboards: a giant leap towards human-computer interaction. In: 2012 International Conference on Computer Systems and Industrial Informatics. IEEE, December 2012
[20]
Salmansha, P.N., Parveen, S., Yohannan, F., Vasavan, A., Kurian, M.: Mini keyboard: portative human interactive device. In: 2017 International Conference on Communication and Signal Processing (ICCSP), pp. 1531–1535. IEEE (2017)
[21]
Goldstein M, Chincholle D, and Backström M Assessing two new wearable input paradigms: the finger-joint-gesture palm-keypad glove and the invisible phone clock Pers. Technol. 2000 4 2–3 123-133
[22]
Fukumoto, M., Tonomura, Y.: Body coupled FingerRing. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems - CHI 1997. ACM Press (1997)
[23]
Zhao Y, Lian C, Zhang X, Sha X, Shi G, and Li WJ Wireless IoT motion-recognition rings and a paper keyboard IEEE Access 2019 7 44514-44524
[24]
Lv, Z., et al.: A new finger touch detection algorithm and prototype system architecture for pervasive bare-hand human computer interaction. In: 2013 IEEE International Symposium on Circuits and Systems (ISCAS 2013), pp. 725–728. IEEE (2013)
[25]
Erdem, M.E., Erdem, I.A., Atalay, V., Cetin, A.E.: Computer vision based unistroke keyboard system and mouse for the handicapped. In: 2003 International Conference on Multimedia and Expo, ICME 2003, Proceedings (Cat. No. 03TH8698), vol. 2, pp. II-765. IEEE (2003)
[26]
Srivastava S and Tripathi RC Kurosu M Real time mono-vision based customizable virtual keyboard using finger tip speed analysis Human-Computer Interaction. Interaction Modalities and Techniques 2013 Heidelberg Springer 497-505
[27]
Livada, Č., Proleta, M., Romić, K., Leventić, H.: Beyond the touch: a web camera based virtual keyboard. In: 2017 International Symposium ELMAR, pp. 47–50. IEEE (2017)
[28]
Malik, S.: Real-time hand tracking and finger tracking for interaction csc2503f project report. Technical report, Department of Computer Science, University of Toronto (2003)
[29]
Zhuo S and Sim T Defocus map estimation from a single image Pattern Recogn. 2011 44 9 1852-1858
[30]
Subbarao M and Surya G Depth from defocus: a spatial domain approach Int. J. Comput. Vision 1994 13 3 271-294
[31]
Tang C, Hou C, and Song Z Defocus map estimation from a single image via spectrum contrast Opt. Lett. 2013 38 10 1706-1708
[32]
Karaali A and Jung CR Edge-based defocus blur estimation with adaptive scale selection IEEE Trans. Image Process. 2017 27 3 1126-1137
[33]
Kumar, H., Gupta, S., Venkatesh, K.S.: Defocus map estimation from a single image using principal components. In: 2015 International Conference on Signal Processing, Computing and Control (ISPCC). IEEE (2015)
Recommendations
Depth from Defocus vs. Stereo: How Different Really Are They?
Special issue on computer vision research at the TechnionDepth from Focus (DFF) and Depth from Defocus (DFD) methods are theoretically unified with the geometric triangulation principle. Fundamentally, the depth sensitivities of DFF and DFD are not different than those of stereo (or motion) based systems ...
Deblur and deep depth from single defocus image
AbstractIn this paper, we tackle depth estimation and blur removal from a single out-of-focus image. Previously, depth is estimated, and blurred is removed using multiple images; for example, from multiview or stereo scenes, but doing so with a single ...
Optimal Camera Parameters for Depth from Defocus
3DV '15: Proceedings of the 2015 International Conference on 3D VisionPictures taken with finite aperture lenses typically have out-of-focus regions. While such defocus blur is useful for creating photographic effects, it can also be used for depth estimation. In this paper, we look at different camera settings for Depth ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© Springer Nature Switzerland AG 2020.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 19 July 2020
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 20 Jan 2025