Abstract
In this paper we explore the utility of compressive sensing for object signature generation in the optical domain. We use laser scanning in the data acquisition stage to obtain a small (sub-Nyquist) number of points of an object’s boundary. This can be used to construct the signature, thereby enabling object identification, reconstruction, and, image data compression. We refer to this framework as compressive scanning of objects’ signatures. The main contributions of the paper are the following: 1) we use this framework to replace parts of the digital processing with optical processing, 2) the use of compressive scanning reduces laser data obtained and maintains high reconstruction accuracy, and 3) we show that using compressive sensing can lead to a reduction in the amount of stored data without significantly affecting the utility of this data for image recognition and image compression.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Frueh, C., Zakhor, A.: Constructing 3D city models by merging ground-based and airborne views. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II-562–II-569 (June 2003)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Inc., Upper Saddle River (2006)
Tamir, D.E., Shaked, N.T., Geerts, W.J., Dolev, S.: Compressive sensing of object-signature. In: Dolev, S., Oltean, M. (eds.) OSC 2010. LNCS, vol. 6748, pp. 63–77. Springer, Heidelberg (2011)
Ye, J.C.: Compressed sensing shape estimation of star-shaped objects in fourier imaging. IEEE Signal Processing Letters 14(10), 750–753 (2007)
Rivenson, Y., Stern, A., Javidi, B.: Compressive fresnel holography. Display Technology. Journal of Display Technology 6(10), 506–509 (2010)
Pavlidis, T.: Algorithms for graphics and image processing. Digital system design series. Computer Science Press (1982)
Baggs, R., Tamir, D.E.: Image registration using dynamic space warping. In: Artificial Intelligence and Pattern Recognition 2008, pp. 128–135 (2008)
Keogh, E., Wei, L., Xi, X., Hee Lee, S., Vlachos, M.: LB Keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures. In: VLDB, pp. 882–893 (2006)
Arkin, E.M., Chiang, Y.J., Held, M., Mitchell, J.S.B., Sacristan, V., Skiena, S.S., Yang, T.C.: On minimum-area hulls (1998)
Hug, C.: Extracting artificial surface objects from airborne laser scanner data. In: Automatic Extraction of Man-Made Objects from Aerial and Space Images II, pp. 203–212 (1997)
Kirchhoff, S.: Laser diode collimator flatbeam (2011), http://www.sukhamburg.com/onTEAM/pdf/cam_cat_44-45_en.pdf
Donoho, D.: Compressed sensing. IEEE Transactions on Information Theory 52(4), 1289–1306 (2006)
Candes, E., Tao, T.: Near-optimal signal recovery from random projections: Universal encoding strategies. IEEE Transactions on Information Theory 52(12), 5406–5425 (2006)
Porat, B.: A course in digital signal processing. John Wiley (1997)
Lustig, M., Donoho, D.L., Santos, J.M., Pauly, J.M.: Compressed sensing MRI. IEEE Signal Processing Magazine (2007)
Elad, M.: Optimized projections for compressed sensing. IEEE Transactions on Signal Processing 55(12), 5695–5702 (2007)
Candes, E., Wakin, M.: An introduction to compressive sampling. IEEE Signal Processing Magazine 25(2), 21–30 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tamir, J.I., Tamir, D.E., Dolev, S. (2013). Object Signature Acquisition through Compressive Scanning. In: Dolev, S., Oltean, M. (eds) Optical Supercomputing. OSC 2012. Lecture Notes in Computer Science, vol 7715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38250-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-38250-5_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38249-9
Online ISBN: 978-3-642-38250-5
eBook Packages: Computer ScienceComputer Science (R0)